Charles Hayden's Eliza Test

Charles Hayden の Eliza Test のページの翻訳です。

 

Charles Hayden
http://www.chayden.net/eliza/Eliza.html

Eliza Test
http://www.chayden.net/eliza/Eliza.html
http://www.chayden.net/eliza/instructions.txt
http://www.chayden.net/eliza/Eliza.zip

https://akito-fujita.hatenablog.com/draft/cySSyP6TsWl-aoE0cQE6NqjOqc4

 

Eliza Test

This applet implements the classic "Eliza" program, a program that communicates in natural language. It pretends to be a Rogerian psychologist.
このアプレットは、自然言語でコミュニケーションするプログラムである古典的な "Eliza"プログラムを実装しています。それは Rogerian の心理学者のふりをします。

The original ELIZA was described by Joseph Weizenbaum in Communications of the ACM in January 1966. ELIZA was one of the first programs that attempted to communicate in natural language. The article was an attempt to demystify the behavior of the program, and included a detailed description of the program. ELIZA is based on a "script" consisting of patterns and corresponding responses. An appendiz to the article contained the complete script for the Rogerial psychologist.
オリジナルのELIZAは、Communications of the ACM, January 1966でJoseph Weizenbaumによって記述されました。ELIZAは、自然言語でのコミュニケーションを試みた最初のプログラムの1つでした。この論文は、プログラムの振る舞いを解明しようとしたもので、プログラムの詳細な説明が含まれています。 ELIZAは、パターンとそれに対応するレスポンスからなる "スクリプト"に基づいています。論文の補足には、Rogerialの心理学者のための完全なスクリプトが含まれていました。

This rendition of ELIZA is a complete and faithful implementation of the program described by Weizenbaum. The script language has been reformatted for clarity, but is unchanged in its content.
この ELIZA の移植実装、Weizenbaum によって記述されたプログラムの完全で忠実な実装です。スクリプト言語は、明確にするために再フォーマットされていますが、内容は変更されていません。

 

The Source

  • Decomp.java - Decomposition rule.(ルールの分解)
  • DecompList.java - List of decomposition rules.(分解したルールのリスト)
  • Key.java - A key is the first thing the program looks for in the input.(キーは、プログラムが入力で最初に探すものです)
  • KeyList.java- The list of all keys.(全てのキーのリスト)
  • KeyStack.java - The program arranges all keys found in the input into a rank-ordered stack.(プログラムは、入力で見つかったすべてのキーをランク順のスタックに配置します)
  • Mem.java - Used to store responses for later use.(後で使用できるようにレスポンスを保存するために使用します)
  • PrePost.java - Holds word paris for pre or post rewriting.(プレまたはポストリライトのための単語のペアを保持します)
  • PrePostList.java - List of all the pre or post rules.(すべての前後のルールのリスト)
  • ReasembList.java - List of reassembly rules (goes with a decomp rule).(リアセンブリルールのリスト(decompルールに従う)
  • EString.java - String matching and transformation helper functions.(文字列照合と変換ヘルパー関数)
  • SynList.java - List of EWordList synonym lists.(EWordList同義語リストのリスト)
  • WordList.java - List of words, used for synonyms.(類義語に使用される単語のリスト)
  • Eliza.java - The main applet program.(メイン・アプレット・プログラム)
  • ElizaApp.java The main application program. Use either this or Eliza.java, not both.(メイン・アプリケーション・プログラム。このファイルまたはEliza.javaのどちらかを使用してください)
  • ElizaMain.java - The main driver.(メイン・ドライバ)

 

The Script

Instructions on how to modify the script file.
スクリプトファイルを変更する方法の説明。

 

The Executable

This zip file contains the compiled class files and the script.
このzipファイルには、コンパイルされたクラスファイルとスクリプトが含まれています。
I wrote this to help learn Java. You are welcome to make use of it however you want. This version is a reworking of a program I wrote back in 1983 to teach myself how to use the Macintosh. I had written some really good documentation at that time, explaining how to write your own scripts. For now, you can look at Weizenbaum's article and the script I have provided to see how to write scripts.
私はJavaを学ぶためにこれを書きました。しかし、あなたはそれを利用することを歓迎します。このバージョンは1983年に書いた、Macintoshの使い方を教えるためのプログラムの改訂版です。その時点で私は、あなた自身のスクリプトを書く方法を説明する、本当に良い文書を書いていました。今のところ、あなたはWeizenbaumの記事とスクリプトを書く方法を見るために私が提供したスクリプトを見ることができます。
You can specify your own script as an applet parameter. See the source of this page to see how to do that.
独自のスクリプトアプレットのパラメータとして指定できます。これを行う方法については、このページのソースを参照してください。
Note on making your own page: I don't fully understand all the interactions of the components of the applet tag. Here is what I use:
独自のページを作成する際の注意:私は、アプレットタグのコンポーネントのすべてのやりとりを完全には理解していません。私が使用しているものを次に示します:

<applet code="Eliza/Eliza.class" width="600" height="200"> 
<param name="script" value="http://chayden.org/chayden/eliza/script">
</applet>

This says that the compiled class file Eliza.class is in a subdirectory Eliza (capitalization is important). Since there are many class files, and they are all in the Eliza subdirectory, it would seem to make sense to set "codebase" to "Eliza" and code to "Eliza.class". However, this does not work.
これは、コンパイルされたクラスファイルEliza.classがサブディレクトリElizaにあることを示します(大文字と小文字の区別は重要です)。多くのクラスファイルがあり、それらはすべてElizaサブディレクトリにありますので、 "codebase"を "Eliza"に設定し、 "Eliza.class"に設定すると意味があるようです。ただし、これは機能しません。
Once the program starts, it needs to read a script file, and so the "param" value gives a URL of the script file. This must be on the same server that supplied the applet (because of Java security rules).
プログラムが起動すると、スクリプトファイルを読み込む必要があります。したがって、 "param"値はスクリプトファイルのURLを示します。(Javaセキュリティー規則のため)これはアプレットを提供したのと同じサーバー上になければなりません。
If you use the HTML shown above, and it you store the class files in an Eliza subdirectory relative to the web page, and if you point to a script file on the same server, you should be able to get this program running on your machine (many people have reported success in doing this). Be aware that certain tools (such as Microsoft Front Page) will split the "code" tag into "code" and "codebase" breaking the applet. This is the major source of difficulties reported to me.
上記のHTMLを使用し、クラスファイルをWebページからの相対的なElizaサブディレクトリに保存し、同じサーバ上のスクリプトファイルを指定すると、あなたのマシンでこのプログラムを実行できるはずです(多くの人々がこれを成功させると報告している)。特定のツール(Microsoft Front Pageなど)は、「コード」タグを「コード」と「コードベース」に分割してアプレットを分割することに注意してください。これは私に報告された困難の主要な原因です。
If you have comments or suggestions, please let me know.
ご意見やご提案がありましたら、お知らせください。

 

EXPERIMENTS WITH A SEARCH ALGORITHM FOR THE DATA BASE OF A HUMAN BELIEF STRUCTURE

この論文は論文 "Artificial Paranoia" での研究に先立って、人間のビリーフ構造(Human Belief Structure)のデータベースを検索する方法に関する実験をまとめたもののようです。タイトルにもある "BELIEF" とは「信じていること」を意味する心理学の用語みたいです。「信念」とか「思い込み」と無理に邦訳できますが、言葉の意味合いとしてフィットしないように思うので、ここでは「ビリーフ」と表現します。 

 

"Artificial Paranoia" には PARRY の情報工学的側面の解説が少ないので、こっちも読まなければならなくなったけど、多分 PARRY の再実装には不十分な内容でしょう。結局、ソースコードに残る MLISP のダンプデータの解析が必要なんだと思います。

 

 

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EXPERIMENTS WITH A SEARCH ALGORITHM FOR THE DATA BASE OF A HUMAN BELIEF STRUCTURE(人間のビリーフ構造のデータベースのための探索アルゴリズムを用いた実験)

 

 

Kenneth Mark Colby
Lawrence Tesler
Horace Enea

 

 

Summary

A large data base was collected from a human informant. The data consisted of beliefs regarding parent-child relations. A variety of factors in searching the data base were manipulated in an attempt to discover which were the more important in contributing to estimates of credibility.

人間の情報提供者から大きなデータベースが収集された。データは親子関係に関するビリーフで構成されています。データベースの検索における様々な要因を操作して、いずれが信頼性の見積もりに重要かを発見しようと試みました。

Problems of data collection, data representation and a searching algorithm are discussed in detail.

データ収集、データ表現、検索アルゴリズムの問題について詳細に説明します。

 

 

Introduction(はじめに)

It is clear that people hold beliefs. What is not so clear is how these beliefs are processed to judge the credibility of an input proposition. As an aid in understanding credibility processes, we constructed a computer model which was intended to simulate the belief processes of a particular human informant. We shall begin with a clarification of terminology used in describing the model.

人々がビリーフを保持していることは明らかですが、これらのビリーフを処理して入力された命題の信頼性を判断する方法は、あまり明確ではありません。信頼性を獲得するプロセスの理解を助けるために、特定の人間の情報提供者のビリーフ・プロセスをシミュレートするためのコンピュータモデルを構築しました。モデルの説明に使用される用語の明確化から始めます。

 

Terminology(用語)

A model consists of a set of interacting components. The major components of the model to be described are state-descriptions (data base), process-descriptions (procedures) and an interpreter whose logic governs the applications of process-descriptions to state-descriptions in accordance with the aim or task of judging credibility. Thus the model is two levelled with the interpreter at the top supervising interactions between procedures and data base.

モデルは、一連の相互作用するコンポーネントで構成されています。記述されるモデルの主な構成要素は、状態の記述(データベース)、プロセスの記述(プロシージャ)、および論理がプロセス記述の状態記述への適用を支配するインタープリターで、インタープリターのロジックは、信頼性を判断する目的またはタスクに従って、状態の記述をプロセスの記述のアプリケーションに適用します。したがってモデルは、インタプリタにより、手続きとデータベースの間の相互作用を管理する2つのレベルに分かれています。

The data base is made up of a conceptual graph whose basic structures consists of conceptualizations in turn composed of elementary conceptions. Conceptualizations in the model are held propositions which symbolically represent states of affairs or situations. Conceptualizations can be represented in both natural and computer languages by an ordered set of name-tokens. Thus a given conceptualization might be described in English as 'Bill likes Mary' and in a programming language as the list  *1 . The particular conceptualizations we focussed on were those which semantically involved certain relations between humans. Since people conceptualize their experience with persons in terms of human action, the elementary conceptions of the data base involve agents, actions, objects and  (optionally) settings, modalities and rationales.

データベースは基本概念が順次構成され概念化された基本構造からなる概念グラフで構成されています。モデルにおける概念は、事象や状況を象徴的に表す命題を保持しています。概念は、ネームトークンで順序付けられたセットによって自然言語とコンピュータ言語の両方で表現することができます。したがって、与えられた概念は、英語では 'Bill likes Mary'と記述され、プログラミング言語では *2 と記述されます。我々が焦点を当てた具体的な概念は、人間と人間との間の特定の関係を意味的に含むものでした。多くの人は人間の行動に関して人間との経験を概念化するので、データベースの基本概念には、agent、action、object、および (optionally) 設定として様式、論拠が含まれます。

The term 'belief' in human belief structures refers to (a) an affective attitude of acceptance, rejection or neutral judgment towards, (b) a held conceptualization. Each conceptualization held or prehended by a belief structure is either accepted to some degree as true, rejected to some degree as false or held in suspension as a neutral candidate for belief. 'True' here means that the situation conceptualized is accepted as being certainly, probably, or possibly being the case while 'false' stands for the opposite of these three modal quantifiers. It is important to note that the attitude of rejection or incredibility is towards a conceptualization prehended within a structure. A disbelieved conceptualization is not expelled from the structure but is prehended with an attitude of rejection. A conception is thus judged to be credible, incredible or somewhere in between.

人間のビリーフ構造における 'belief' という用語は (a) 受け入れ、拒絶、または中立的な判断に対する中立的な態度、(b) 保持された概念を指します。ビリーフ構造によって保持されているか、または予期されていた各々の概念は、ある程度は真実として受け入れられ、ある程度は虚偽として拒否され、中立的なビリーフの候補として中断されます。ここでの 'True' とは、概念化された状況が「確かに」「おそらく」「可能性がある」と受け入れられることを意味し、一方、'false' はこれらの3つの量的限定子の反対を表します。拒絶あるいは信用の態度は、構造内で予期された概念に向いていることに注意する必要があります。信用できない概念は構造から追い出されるのではなく、拒絶の態度として捉えられます。したがって、概念は信用できる、信用できない、あるいはその両者の間のいずれかにあると判断されます。

We postulate credibility to be a function of foundation and consistency. The foundation of a given belief is a measure of those beliefs which imply it as opposed to its negation. Consistency refers to a degree of consonance and dissonance found in those beliefs a given belief implies. The term 'imply' does not refer to logical implication but to psychological implication which involves rules of expectancy. We also assume a weight which determines the relative importance foundation and consistency have for one another in a particular domain of interest.

信頼性は根拠と一貫性の関数であると仮定します。与えられたビリーフの否定に反対するような意味を持つ根拠は、それらのビリーフの目安になります。一貫性は、そのようなビリーフの中に与えられたビリーフの含意との一致や食い違いを指します。「含意」という用語は、論理的意味合いを指すのではなく、予期されたルールを含む心理的意味に関連しています。また、特定の関心領域において、根拠と一貫性の相対的重要性を決定する重みを仮定します。

Conceptualizations with their associated credbilities make up one component of the data base. We term these conceptualizations 'facts' since they stand for that which is or is not the case in the structure. A second component of the data base consists of rules. By the term 'rule' we mean a connectivity relation holding between two or more conceptualizations. The components of this relation contains variables as well as name-tokens. Hence, a rule might read 'if x likes y then x helps y', where both x and y are variables to which the name-tokens of persons can be bound. As mentioned, this if-then relation represents a type of psychological implication. Our interpretation of psychological implication is that given conceptualization A to be the case, conceptualization B is expected to be the case by the structure. For example, humans commonly expect that if a person likes another person, the first person will help the second person. Such general expectancy rules allow a variety of inference processes to be carried out. A further discussion of psychological implication can be found in Abelson[2].

信用度が関連付けられた概念は、データベースの1つの構成要素を構成します。それらは構造内にあるか?否か?に立脚しているので、これらの概念は 'facts' と呼ばれます。データベースの第2の構成要素はルールからなります。'rule' という用語は、2つ以上の概念を保持する接続関係を意味します。この関係のコンポーネントには、変数と名前トークンが含まれます。したがって、ルールは 'if x likes y then x helps y' と読むことができます。ここでの x と y は人の名前トークンを束縛できる変数です。既に言及したように、この if-then 関係は、ある種の心理学的含意を表します。心理学的含意の我々の解釈は、概念 A が与えられれば、概念 B は構造によって当てはまると予想されることを意味します。例えば、人間は、ある人が他の人を好きなら、最初の人は2番目の人を助けると一般的に考えています。そのような一般的な予測値ルールは、様々な推論プロセスが実行されることを可能にします。心理学的含意の詳細な議論は Abelson[2] に見ることができます。

The remaining components of the data base con sist of definitions and classifications. For example, the name-token 'love' is defined as similar-to and stronger-then 'like'. 'Dislike 1 is definable as negation-of 'like'. These definitions are used in finding similarities and contrasts between conceptualizations. Classifications consist of set memberships and set inclusions.

データベースの残りのコンポーネントは、定義と分類で構成されています。たとえば、名前トークン 'love' は 'like' と similar-to であり stronger-then と定義されています。これらの定義は、概念の間の類似点と対照点を見つけるのに使用されます。分類は、一連のメンバーシップと一連の内容物で構成されます。

In summary, the data base represents information in the form of various kinds of state descriptions. When the model runs, this information is subjected to procedures governed by a top-level interpreter. The procedures are called into operation by the interpreter in accordance with the task involved. The main task we were interested in involves estimating the credibility of a given proposition describing some actual or hypothetical situations. Given such a proposition, how might a person judge its credibility using the information-processing capacities he has available?

要約すると、データベースは、様々な種類の状態記述の形で情報を表します。モデルが実行されると、この情報はトップレベルのインタプリタによって管理されるプロシージャを受けます。このプロシージャは、関係する作業に応じてインタプリタのオペレーションに呼び出されます。私たちが関心を持っていた主な仕事は、実際の状況や仮説的な状況を記述した命題の信頼性を推定することです。そのような命題が与えられた場合、利用可能な情報処理能力を使って、どのように信頼性を判断するのでしょうか?

We approached this problem by selecting an informant, collecting certain beliefs, and representing them in a data base. We planned firs t to conduct certain information-processing experiments on the data base and second to attempt a validation of the simulation. This report will be concerned with the first phase of experimentation.

我々は、情報提供者を選択し、特定のビリーフを収集し、それらをデータベースで表現することによって、この問題にアプローチしました。

 

Data Collection(データの収集)

To find an informant for this research, we advertised in a college newspaper for persons who might be interested. Out of 65 applicants, we interviewed 26 and then selected a 30 year old married woman on grounds that she was intelligent, articulate, interested in the research and serious of purpose.*

この研究の情報提供者を見つけるために、私たちは大学の新聞に興味を持った人物を広告しました。65名の応募者のうち26名にインタビューを行い、研究目的に重点を置いて知的で明確であり、研究に興味があり、目的に真剣である30歳の既婚女性を選択しました。*

* For obvious reasons we cannot give her name, but we would like to use this footnote to express our gratitude for her help.

* 彼女の名前を明かすことはできませんが、この脚注を使用して彼女の助けに感謝の意を表したいと思います。

Several times a week she would write down in natural language beliefs which occurred to her about events in her life. Each week we would try to reduce these natural language statements to a simpler form suitable for the model's data base and processing. At intervals we would show the data base to the informant for her corroboration or correction of our paraphrasings. Initially we planned to obtain her beliefs regarding all the important people in her life space. Preliminary experience showed that while collection of such data is possible, the labor required to organize and represent this amount of data in a computer model makes the task extremely difficult with currently available methods.

週に数回、彼女は生活で起こった出来事について自然言語でビリーフを書き留めます。毎週、これらの自然言語文を、モデルのデータベースや処理に適した、より単純な形式に縮小することを試みました。時々、私たちは彼女にデータベースを示し、彼女の確認や我々の言い換えの修正を行いました。当初、私たちは彼女の人生の中で重要な人々に関するすべてのビリーフを得る予定でした。予備的な経験により、そのようなデータの収集は可能であるが、現在のコンピュータ・モデルでこの量のデータを整理して表現するのに必要な労力は、利用可能な方法では非常に困難であることが示されたました。

We found two main disadvantages to this method of data-collection. First, it is cumbersome and time-consuming, requiring hundreds of man-hours to obtain a data base of 700 facts, rules, definitions and classifications. A better method should be developed whereby an informant could type information directly into a data-base by means of a man-machine dialogue. This input might be in an artificial and simplified language which an informant could learn. However an artificial language is distracting and constrains expressiveness. It would be better to allow the rich freedom of natural language but there are great problems involved in the machine handling of this sort of input. We have had some experience along these lines[3] and we are continuing an attempt to make a conceptual parsing of natural language sentences in order to translate them into conceptualizations suitable for the data base of a belief structure.

このデータ収集方法には、主に2つの欠点がありました。第1に、煩雑で時間がかかり、700の事実、規則、定義、分類のデータベースを手に入れるのに何百もの工数を要します。情報提供者がマン・マシンの対話によって情報をデータベースに直接入力できるようにするより良い方法を開発すべきです。この入力は、情報提供者が学ぶことができる人工的かつ簡略化された言語であってもよいでしょう。しかし、人工言語は気を散らし、表現力を制約しています。自然言語の豊富な自由を許す方が良いでしょうが、このような入力の機械操作には大きな問題があります。私たちはこれらのライン [3] に沿ったいくつかの経験を持っており、自然言語文をビリーフ構造のデータベースに適した概念に変換するために概念的解析を試みています。

A second drawback to our initial data collection method involved the problem of extensiveness versus intensiveness. An extensive database is one in which there are a great variety of conceptualizations but not a great number around any one theme. While containing a large amount of information, this type of data is too sparse to permit the model to come to very many conclusions regarding credibility. Few beliefs of relevance can be found for a given proposition unless it is of a very general and hence trivial nature.

初期のデータ収集方法の第2の欠点は、拡張性と集中性の問題でした。広範なデータベースには、多種多様な概念化がありますが、いずれのテーマにも大きな影響はありません。大量の情報を含んでいますが、このタイプのデータはあまりにも疎であり、モデルが信頼性に関する非常に多くの結論に至ることができません。それが非常に一般的で、それゆえに些細な性質でない限り、与えられた命題に対して関連するいくつかのビリーフを見つけることができます。

We then attempted to concentrate on a particular theme in order to make the database dense around selected conceptions. Since our informant was the mother of a three year old child and interested in the problem of child raising, we concentrated on her beliefs in this area. For each of her beliefs in this domain, we obtained a weighting of a degree of credibility. We used crude categories of strong, medium and weak for these weightings. To obtain data rules we would ask the informant for reasons for each belief. For example, if a belief were 'a child ought not hit another child' and the reason given by the informant was 'because if a child hits another child then the second child gets hurt', a general if-then expectation rule can be constructed about the relation between hitting and hurting.

次に、選択された概念を中心にデータベースを密集させるために、特定のテーマに集中しようとしました。私たちの情報提供者は3歳の幼児の母親で、子育ての問題に興味があるため、私たちはこの分野の彼女のビリーフに集中しました。この領域の彼女のビリーフごとに、信頼性の程度の重み付けを取得しました。これらの重み付けには、強、中、弱の粗いカテゴリを使用しました。データのルールを得るために、私たちは情報提供者に各ビリーフの理由を尋ねました。例えば、ビリーフが「子供は別の子供を叩いて(hit)はならない」であり、情報提供者によって与えられた理由が「叩かれた子供が傷つく(gets hurt)から」であった場合、打撃(hitting)と傷害(hurting)との関係について、一般的な if-then 予想ルールを構築することができます。

One difficulty to be anticipated in simulating a human belief structure involves keeping the model updated along with the informant. If a person's beliefs are continually changing, one cannot keep a model in close enough correspondence to test out comparisons between the person's and the model's performances in estimating credibility. However, we found that with our informant, these particular beliefs about parent-child behavior changed very little over a period of several months. In the case of only two beliefs did she change a credibility weight from strong to medium. Hence the structure appeared quite stable over this period of time. It should also be remarked that there occurred no major environmental event in this domain of interest which might be expected to have great impact on a belief structure.

人間のビリーフ構造をシミュレートする際に予想される1つの難点は、情報提供者とともにモデルを更新することです。人間のビリーフが絶えず変化している場合、信頼性を見積もる際の人間とモデルのパフォーマンスの比較をテストするため、そのモデルを人間と十分に近い対応関係に保つことができません。しかし、私たちの情報提供者が、親子行動に関するこれらの特定の信念が数ヶ月の間にほとんど変化しないことを発見しました。たった2つのビリーフについて、彼女は信憑性の重みを強から中に変えました。したがって、構造はこの期間にわたってかなり安定して現れました。また、この関心領域には、ビリーフ構造に大きな影響を及ぼすと予想される主要な環境的な事象は生じなかったことに留意すべきです。

We attempted to model the credibility processes of a single individual. This approach is in the research tradition of an intensive design in contrast to an extensive design. An extensive design might make one observation on 1000 persons while an intensive design would make 1000 observations on one person. Both designs attempt to account for variation in the phenomena observed. In an extensive design, the unit of variability is an individual and variation between individuals is studied whereas in an intensive design we are studying the variability within an individual. In modelling a single case we are trying to understand the mechanisms involved in intra-individual processes. An intensive design attempts to show what can and does happen. The frequency of this sort of happening in a population and which population is another matter. After learning how to model one person we can model another and so build up a series of cases. The inductive problem of generalizing then becomes one of sampling and of statistical measures to discover how general the informant's beliefs might be in a population. This was not our problem at this state of the inquiry. Our problem was how to construct a good model of the informant's belief processes. The criteria for 'good' can be varied. And are we getting at what the informant 'really' believes? What 'really' means here is obscure but it is common knowledge that people have limited accessibility to their beliefs at a given moment. Even worse, they have the capacity to deceive themselves to rationalize, and to distort their own beliefs. Over time we hoped to increase accessibility, realizing there are always limits. In worrying about what is 'really' believed we found it useful to keep in mind that we were constructing a model of a model. A belief structure is a representation and in giving information about himself, an informant tells us what he believes he believes. He simulates himself and it is his accessible model of himself that becomes the data base of a computer model. Humans' ability to simulate themselves and to make models of other models is of course a most interesting property for a symbolic system to have.

私たちは、単一の個人の信頼性プロセスをモデル化しようと試みました。このアプローチには、広範なデザインとは対照的に、集中的なデザインの研究上の伝統があります。広範なデザインでは1000人を1回観測し、集中的なデザインでは1人について1000回の観測を行う場合があります。両方のデザインとも、観察された現象の変動を説明しようとします。広範なデザインでは、変動の単位は個体であり、個体間の変動が研究されるのに対して、集中的なデザインでは個体内の変動性を研究しています。1つのケースをモデリングする際には、個人内プロセスに関わるメカニズムを理解しようとしています。集中的なデザインでは、何が起こりうるのか、何が起こったのかが示されます。ある集団において起こるこの種の出来事の頻度と集団は別の問題です。ある人物をモデル化する方法を学んだ後、私たちは別の人物をモデル化して一連の事例を構築することができます。情報提供者のビリーフがどのように一般的であるかを発見するための一般化の帰納的問題は、サンプリングと統計的尺度の1つになります。 この問いの状態では、これは私たちの問題ではありませんでした。私たちの問題は、情報提供者のビリーフ・プロセスの良いモデルを構築する方法でした。「良い」の基準は変更することができます。そして、情報提供者が「本当に」信じているものに私たちはたどり着いているでしょうか?「本当に」というのはあいまいであることを意味しますが、人々は特定の瞬間に自分のビリーフへのアクセスが制限されていることはよく知られています。さらに悪いことに、彼らは自分自身を欺いて合理化し、自分のビリーフを歪める能力を持っています。時間がたつにつれて、私たちはアクセシビリティを高めることを望んで、常に限界があることを認識しました。「本当に」信じられていることを心配すると、私たちはモデルのためのモデルを構築していたことを、心に留めておくと有益だということが分かりました。ビリーフ構造は表現であり、自分自身に関する情報を提供する際に、情報提供者は自分が信じていることを信じています。彼は自分自身をシミュレートし、コンピュータモデルのデータベースになる彼自身のアクセス可能なモデルです。もちろん、人間をシミュレートし、他者のモデルを作る人間の能力は、象徴的なシステムにとって最も興味深い特性です。

 

 

Data Representation(データの表現)

In building a data base for the model we thought of the collected facts, rules, definitions and classifications as a graph. Physically in the model they were lists in the programming language MLISP[5, 6]. MLISP is a high level list processing language which translate ALGOL-like meta-statements (M-expressions) into the symbolic statements (S-expressions) of LISP 1.5. The program runs on the PDP-6/10 time-sharing system of the Stanford Artificial Intelligence Project.

モデルのデータベースを構築する際に、収集された事実、規則、定義、分類をグラフとして考えました。物理的なモデルでは、それらはプログラミング言語MLISP [5, 6] のリストでした。 MLISPはALGOLのようなメタステートメント(M式)を LISP 1.5 のシンボリックステートメント(S式)に変換する高レベルのリスト処理言語です。プログラムは、スタンフォード人工知能プロジェクトの PDP-6/10 タイムシェアリング・システムで実行されます。

Each conceptualization was represented as a list of elements consisting of English-like name tokens or atoms, of lists of atoms, and of lists of lists which contained semantic and numerical informationt. As mention in the section 'Terminology', the conceptualizations reflect a human action model of situations in the interpersonal world. From this perspective, agents carry out actions towards objects which in turn can be agents or other situations. In the database each agent, action, object, etc., is identified by an atom. On the property list of each atom is a list of pointers to all conceptions in the data base in which that particular atom occurs. A hash coding scheme is used for rapid look-ups and retrieval of relevant conceptions.

それぞれの概念は、英語のような名前トークンまたは atomatomのリスト、および意味論的および数値的情報を含むリストからなる要素リストとして表されていました。「用語」の項で述べたように、概念は個人間に生じる状況の人間行動モデルを反映します。この観点から、エージェントはオブジェクトに向かってアクションを実行し、オブジェクトはエージェントやその他の状況になります。データベースでは、各エージェント、アクション、オブジェクトなどは atom によって識別されます。各 atom のプロパティ・リストには、その atom が存在するデータベース内のすべての概念に対するポインタのリストがあります。ハッシュコードのスキームが、関連する概念の迅速な探索および検索に使用されます。

The representation of a fact such as 'Barb likes children' appears on the list:

'Barb likes children' のような事実の表現はリストとして表示されます:

    (F (agent Barb) (action like) (object children) (credibility 0.9))

with the symbol F indicating this is a fact. More complicated is the representation of a rule because of the problem of binding variables unambiguously. For example, the natural language statement 'parents spank children' has a number of possible semantic interpretations. It was necessary to check carefully with the informant in order to be clear about which interpretation she intended. In this case she did not mean that the set 'parents' spank the set 'children' nor did she mean each member of the set 'parents' spank each member of the set 'children'. By the expression 'parents spank children' she meant that a given parent spanks his children. More formally, if x is a parent and y is a child and x is a parent of y, then x spanks y.

記号 F はこれが事実であることを示します。より複雑なのは、変数を明確にバインドする問題があるルールの表現です。例えば、自然言語による文 'parents spank children' には数多くの意味論的解釈があります。情報提供者が意図した解釈について明確にするために、彼女と慎重にチェックする必要がありました。この場合、彼女はセット 'parents' がセット 'children' を叩くことを意味するわけでもなく、セット 'parents' の各メンバーがセット 'children' の各メンバーを叩くことを意味するわけでもありません。 'parents spank children' という表現は、ある親が自分の子供を抱き寄せていることを意味していました。より厳格には、x が親であり y が子であり、x が y の親である場合、x spanks y するのです。

The relation 'is a parent of' must first be defined in terms of certain constrained variables. For instance, the variable P is defined as a parent who is a parent of Qx4 and Cx4 is defined as a child who is a child of Px4. When assignments are made to such variables only those name tokens which qualify can be substituted. Thus the rule 'if a parent likes his child, then his child is happy' would be represented as the list:

関係 'is a parent of' は、まず特定の制約付き変数の観点から定義されなければなりません。例えば、変数 P は Qx4 の親の親として定義され、Cx4 は Px4 の子の子として定義されます。そのような変数に代入すると、修飾する名前トークンのみを代入することができます。したがって、ルール 'if a parent likes his child, then his child is happy' はリストとして表現されます。

    (R (Px4Cx4) (((agent Px4) (action like) (object Cx4))
             ((agent Cx4) (action has)  (object happiness))))

with the symbol R indicating that it is an implications! rule. When facts are search to match the components of a rule, the fact 'John likes Mary' would fit this rule only if John is held to be a parent of Mary so that 'John' can be substituted for Px4 and 'Mary' for Cx4. These constrained variables are global in the program. They permit the binding of variables to be unambiguous and allow rules to be arbitrarily complex since the qualifications required for the variable may involve multiple conditions.

記号 R はそれがルールを意味していることを示しています。事実がルールの構成要素に一致する検索である場合、JohnがMaryの親である時にのみ、事実 'John likes Mary' は、John が Px4 を代用し、MaryをCx4の代わりに使用できるように、この規則に適合します。これらの制約された変数は、プログラム内ではグローバルです。変数に必要な資格が複数の条件を含む可能性があるため、変数の結合を明確にすることができ、規則を任意に複雑にすることができます。

Representation of definitions is in the form of a simple list. To indicate conceptual relations between 'love' and 'like', the list appears as

定義の表現は、単純なリストの形で行われます。'love' と 'like' の間の概念関係を示すためのリストは

    (love similar like S)

where the symbol S indicates 'love' is stronger in intensity than 'like'. The following are some relations represented in definitions, S meaning stronger, W weaker, and E equal.

記号 S は 'love' が 'like' よりも強度が強いことを示します。以下は、定義において表されるいくつかの関係であり、S はより強い、W はより弱く、E は等しいを示しています。

    similar    (love similar like S)
    different  (men different women)
    negative   (notlike negative like E)
    opposite   (love opposite hate E)
    kindof     (spank kindof aggression)

Classifications take the simple form,

分類はシンプルな形をとっており、

    (F (agent matches) (action is a) (object things))

Initial experience with a data base of 700 facts, rules, definitions and classifications not only taught us about the density requirements of data but also brought to light an implementation problem. When output from a running model is not satisfactory, it may be due to errors in the data as well as to inadequacies of the procedures. A small data error, (such as the term 'notlike'. in a conceptualization instead of 'like'), originating from human mistakes in inputting data into the data base, can give rise to an incorrect credibility estimate. When a database is large, it becomes extremely difficult to trace entirely by hand what happened in a given run of the model. We tried frequently to check the data for errors and the informant repeatedly studied a listing of the database searching for mistakes. In spite of this labor of scrutiny, disconcerting data errors would still crop up. To make sure the procedures were operating as postulated, we first selected a very restricted subset of the data base and then gradually added to it as the program became debugged.

700の事実・規則・定義・分類のデータベースを用いた初期の経験は、データの密度要件について教えてくれただけでなく、実装上の問題を明らかにしました。実行中のモデルからの出力が満足できるものではない場合、それはデータの誤りだけでなくプロシージャが不十分なことによるものかもしれません。データベースにデータを入力する際の人間の間違いに起因する小さなデータエラー('like' ではなく 'notlike' という言葉のように)は、間違った信頼性の推定を引き起こす可能性があります。データベースが大規模である場合、モデルの実行中に何が起こったのかを完全に追跡することは非常に困難になります。我々は頻繁にエラーのためにデータをチェックしようとし、情報提供者は間違いを探しているデータベースのリストを繰り返し調べました。この精査の労力にもかかわらず、データの間違いは依然として詰まっています。プロシージャが仮定どおりに動作していることを確認するために、まずデータベースの非常に限定されたサブセットを選択し、プログラムがデバッグされるにつれて徐々に追加しました。

 

 

Procedures(プロシージャー)

The modelling program scales variables such as credibility, foundation, and consistency into the range 0 to 100. The interpretation of these numbers is as follows:

モデリングプログラムは、信頼性、根拠、一貫性などの変数を0〜100の範囲で調整します。これらの数値の解釈は次のとおりです。

90-100 Strongly positive
強いポジティブ
6O-89 Weakly to moderately positive
弱〜中程度にポジティブ
41-59 Undecided
未定
11-40 Moderately to weakly negative
弱〜中程度にネガティブ
0-10 Strongly negative
強いネガティブ

Credibility is a function of two components: foundation and consistency.

信頼性は、根拠と一貫性の2つの要素の関数です。

The foundation of a proposition is a measure of the model's evidence for and against the proposition. If the positive evidence outweighs the negative, then the foundation is high; if the negative evidence is stronger, the foundation is low.

命題の根拠は、命題のためのモデルの証拠の尺度です。肯定的な証拠が否定的な証拠を上回る場合、根拠は高いです。否定的な証拠が強い場合、根拠は低いです。

Credibility is a function mainly of foundation. When foundation is moderate, consistency has more influence on credibility than when foundation is extreme. Thus, if the evidence concerning a proposition is not dominantly pro or con, then the model gives extra weight to its consistency in determining its credibility. A formula for credibility which incorporates this factor is given below.

信頼性は主に根拠の関数です。根拠が中程度の場合、根拠が極端である場合よりも一貫性が信頼性に大きな影響を与えます。したがって、命題に関する証拠が賛成あるいは反対が支配的でない場合、モデルは信頼性を決定する際の一貫性に追加の重みを与えます。この要素を組み込んだ信頼性の式を以下に示します。

    credibility = foundation + (consistency-50) x (50 - |foundation-50|) x weight

The "weight" is a number between 0 and 0.02 which indicates the relative importance of consistency in this computation. If consistency is irrelevant, the weight is zero. If it is dominant, the weight is 0.02.

"weight" は、この計算における一貫性の相対的重要性を示す0〜0.02の間の数です。一貫性が無関係な場合 weight はゼロです。それが支配的であれば weight は0.02です。 

 

EXAMPLES(使用例)

CREDIBILITY as a function of Foundation and Consistency

根拠と一貫性の関数としての信用度

Consistency
           100 80 50 20  0
Foundation
        80  90 86 80 74 70
        50  75 65 50 35 25
        20  30 26 20 14 10

The consistency of a proposition P is computed by finding a few highly relevant beliefs and measuring the consonance of P with these beliefs. Relevance is defined objectively. If P is a proposition of the predicate form f(p) and if there is a rule in the model that says:

命題Pの一貫性は、いくつかの関連性の高いビリーフを見つけ、これらのビリーフで P の協和を測定することによって計算されます。妥当性は客観的に定義されます。 Pが述語形式 f(p) の命題であり、モデルに次のような規則がある場合:

if f(x) then g(x)

and if g(p) is the predicate form of a belief Qi, then Qi is highly relevant to P. If the model already disbelieves Qi. -- or believes -1 Qi. -- then P is dissonant with it. If the model neither believes or disbelieves Qi. then the consistency of P is not affected.

The computation of consistancy consists of determining the percentage of Qi's with which P is consonant. More weight is given to consonance with more credible Qi's than to consonance with less credible Qi's. A set of formulas that incor porate this weighting is given below.

sc = Z credibility(Qi), where P Qi 
and credibility(Qi) > 50 
cc = count of Qi's contributing to sc 
cn = count of Qi's where Po - 1 Qi and 
credibility(qi) > 50 
consistency = (sc + 1) / (cc + en + .02)

The foundation of a proposition P is computed by finding relevant beliefs and seeing whether they imply that P is or is not the case. In the search for relevant beliefs, graph paths through beliefs consonant with P are searched harder than paths through beliefs dissonant with P if P seems to be highly consistent. The reverse strategy is used if P seems to be inconsistent. This is done so that model can attempt to limit its search for evidence in such a way as to maintain the consistency of its entire belief structure.

Formulas for foundation in terms of evidence for and against P are:

sc = Σ credibility(Qi), where Qi ⊃ P
and credibility(Qi) > 50 
cc = count of Qi's contributing to sc 
cn = count of Qi's where Qi ⊃ ¬ P 
and credibility(qi) > 50 
foundation = (sc + 1) / (cc + cn + .02)

The search for relevant beliefs is controlled by a "work" factor. A consistency search will do, say, 200 units of work while a foundation search will do, say, 1000 units of work. This work allotment is apportioned among the possible graph paths that lead from the proposition in question to relevant areas of the graph.

The algorithm for searching is as follows. The directly relevant beliefs in the graph are found. A directly relevant belief is one which can be derived from P in one step by any one of these methods:

1) Replace the verb by a similar (or opposite) verb.
2) Replace the subject or object by an analogous (or complementary) noun.
3) Replace the predicate adjective by a similar (or opposite) adjective.
4) Generate a belief which implies P (or P implies) according to any one rule.

These beliefs are the heads of paths to be searched. A certain amount of work is used up just in finding them; say, 2 units for each relevant rule used, 2 for each step of an analogy that is drawn, and 3 for each similar verb that is found, plus 6 units overhead even if nothing relevant is found.

If there is any work that remains unused after finding the heads of these paths, it is divided up among the paths for further searching.

In the consistency search, all paths receive equal treatment. However, in the foundation search, the division among paths is affected by consistency. To compute the consistency of P with these paths, a recursive short-depth search is performed along each path; these searches are alotted, say, l/3 of remaining work. From the resulting consistencies, proportions are computed according to the following formulas:

eg = consistency of P with whole system
cp = consistency of P with this path

relative proportion = (110 - |cg - cp|) / (110 - |cg - 50|)

The paths with highest proportions are searched first and receive a proportionately greater work allotment.

If not al l the work along a path is exhausted, the remainder is divided among the remaining paths. If, after searching any path, enough relevant beliefs have been found to compute a credibility exceeding 60 or below 4O, then the search of the rest of the paths is cancelled.

 

 

Experiments

The program performs two major experiments. The first experiment assumes that the belief structure is unchanging. One proposition at a time is presented to the structure and its credibility is judged. In the second experiment, the belief structure does change. After each proposition's credibilit y has been evaluated, it becomes incorporated into the structure as a belief.

The first experiment is run by presenting each belief in the structure to all the other beliefs and judging its credibility. The result can be compared with the prestipulated credibility of the belief. Then, a lis t of new propositions is presented to the structure for evaluation. In both experiments, many factors of the evaluation are varied.

One factor to vary is the means of finding relevant beliefs. There are four variations:

1) Use only rules no definitions.
2) Use (1) plus rules to find supersets.
3) Use (1) and (2) plus "similar" and "opposite" rules.
4) Use (1) , (2), and (3) plus rules to find instances.

Another factor to vary is the use of consistency. There are two variations:

1) Use foundation and not consistency.
2) Use also consistency.

 

Other factors varied are:

1) Weight of consistency relative to foundation in computing credibility.
2) Amount of work expended in search.
3) The initial credibilities assigned each the belief.

All values of these factors are combined with every meaningful combination of other factors.

 

 

Experimental Results

For the data base used in the experiments so far, a few interesting results were obtained.

The search for relevant beliefs was effective when both rules and supersets were utilized in the search. Without supersets, many relevant beliefs were missed. The addition of similar and opposite rules expanded the search enough to discover nearly all beliefs considered relevant by the experimenters. In only a handful of cases did the application of instance rules improve the relevance search.


The use or disuse of consistency made no noticeable difference in the credibility computation. It is planned to see whether consistency wil l make a difference with different data or with work allotments that have not yet been tried.

The amount of work alloted made a difference in the success of finding relevant beliefs. It is intended to measure this difference quantitatively, but techniques for this have not yet been developed.

Scaling the credibilities of all the beliefs in the system by a factor x seemed to affect the credibility computed for an input proposition by that same multiple, x. This showed that the complex search combined with the quotient formulas for credibility still preserved linearity.

Further values of the variable f .ctors are in the process of being tested, as well as improved searching algorithms.

 

 

Discussion

 

The only other program we know of which judges credibility is that of Abelson and Carroll 1. There are a number of similarities and differences between the two programs. Perhaps the most important difference lies in the way the search algorithm is controlled. In the Abelson and Carroll program searches through a large data base are cut off probabilistically, depending on a random number exceeding some fixed value. In our model, three factors govern the search, consistency, work and firmness. Search along consistent paths is preferred to search along inconsistent paths. Also search along a path is cut off if (a) alloted work runs out, (b) a firm credibility of >60 or <k0 is reached or (c) the path is exhausted.

Another interesting difference lies in the way the two programs treat instances and supersets. The Abelson and Carroll program looks "down" at instances and "up" at supersets to an equal degree. We found that with this data base searching for instances contributed to credibility less reliably than supersets. Therefore we alloted more work to searching supersets than to searching instances.

Our experiments with this data base collected from an informant constituted an attempt to discover what search factors made a significant difference in estimating the credibility of input propositions. We were not attempting to validate a particular search algorithm. Instead we explored a variety of procedures in an effort to learn more about their respective merits in processing the same data base. We intend to discuss the validation problem in a future report.

 

 

References

(1) Abelson, R.P. and Carroll, J.,
Computer simulation of individual belief systems.
American Behavioral Scientist, 8, 24-30 (1965).

[2] Abelson, R.P.,
Psychological implication.
In Theories of Cognitive Consistency (Abelson,R., Aronson, E., McGuire, W., Newcomb, T., Rosenberg, M., Tannenbaum, P. Edc. Rand-McNally, New York, 1969)

[3] Colby, K.M. and Enea, H.,
Heuristic methods for computer understanding of natural language in context restricted on-line dialogues.
Mathematical Biosciences, 1, 1-25 (1967)

(4) Schank, R.C and Tesler, L.G.
A conceptual parser for natural language.
Stanford Artificia Intelligence Project Memo No. AI-76, 1969 (See also this volume).

(5) Enea, H.,
MLISP.
Stanford Computer Science Department Technical Report, No. 92, March 14, 1965:

(6) Smith, D.C.
MLISP Users Manual.
Stanford Artifi cial Intelligence Memo, No. 1969. (In preparation).

 

 

Acknowledgements

This research is supported by Grant PHS MH 06645-07 from the National Institute of Mental Health, by (in part) Research Scientist Award (No. K-l4,433) from the National Institute of Mental Health to the senior author and (in part) by the Advanced Research Project Agency of the Office of the Secretary of Defense (SD-183). -654

 

 

*1:agent Bill) (action like) (object Mary

*2:agent Bill)  (action like)  (object Mary

Artificial Paranoia

Kenneth Colbyの論文 "Artificial Paranoia" です。

この論文ではパラノイアを模擬するプログラム PARRY が解説されています。

同じタイトルの書籍もあるようです。

心理学の用語法をわかってないので不適切な翻訳が多々あるでしょう。

 

--------------------------------------------------------------------------------

ARTIFICIALINTELLIGENCE

Artificial Paranoia

Kenneth Mark Colby
Senior Research Associate, Computer Science Department
Stanford University, Stanford, California 94305

Sylvia Weber
Graduate Student, Computer Science Department
Stanford University, Stanford, California 94305

Franklin Dennis Hilf
Research Associate, Computer Science Department
Stanford University, Stanford, California 94305

Recommended by Allen Newell

 

 

1. This research is supported by Grant PHS MH 06645-09 from the National Institute of
Mental Health, by (in part) Research Scientist Award (No. I-KOS-K-!4, 433) from the
National Institute of Mental Health to the senior author and by (in part) the Advanced
Research Projects Agency of the Office of the Secretary of Defense (SD-183).
Artificial Intelligence 2 (1971), 1-25
Copyright C 1971 by North-Holland Publishing Company

 

 

ABSTRACT(要約)

A case of artificial paranoia has been synthesized in the form of a computer simulation model. The model and its embodied theory are briefly described. Several excerpts from interviews with the model are presented to illustrate its paranoid input-output behavior. Evaluation of the success of the simulation will depend upon indistinguishability tests.

人工的なパラノイア症例はコンピュータ・シミュレーションモデルの形で合成されています。モデルとその具体化された理論を簡単に説明します。モデルに対するインタビューによるいくつかの抜粋が、そのパラノイドの入出力の挙動を説明するために提示されます。シミュレーションの成功の評価は、区別不能テストに依存します。

Within the paradigm of computer science, distinctions are sometimes drawn between the activities of computer simulation and artificial intelligence. Yet in constructing models of psychological processes, the distinction can become blurred in places where overlaps emerge, as will be evident from our account of a model of artificial paranoia.

コンピュータサイエンスパラダイムの中では、コンピュータ・シミュレーションと人工知能の研究活動は区別がつかされることがあります。しかし、心理プロセスのモデルを構築する際には、境界領域では区別がぼやける可能性があります。これは、人工的なパラノイアのモデルの説明からも明らかです。

 

1. Simulation Models and Artifacts(シミュレーションモデルと成果物)

An information-processing system is defined as a structured combination of functions which collaborate in governing a set of input-output behaviors.

情報処理システムは、一連の入出力挙動を統制する際に協力する機能の構造化された組み合わせとして定義されます。

Two information-processing systems, S1 and S2, are considered input-output (I-O) equivalent when the I-O pairs of S1 in a particular situation are indistinguishable from the I-O pairs of S2 in a similar situation in respect to specified dimensions. To simulate the, I-O behavior of a system, S1, one constructs a computer simulation model, S2, whose I-O behavior imitates that of S, along contain dimensions.

2つの情報処理システム S1 および S2 は、特定の状況における S1 の入出力ペアが、指定された次元に関して同様の状況で S2 の入出力ペアと区別できない場合、同等の入出力と見なされます。システム S1 の入出力動作をシミュレートするために、入出力動作が S の動作を模倣するコンピュータ・シミュレーションモデル S2 を次元を維持しながら構築します。

Our phrase ‘artificial paranoia’ refers to an actual but non-human case of paranoia which we have constructed in the form of a computer model. The model’s I-O behavior, in the communicative situation of a diagnostic psychiatric interview, is identifiable by psychiatric judges as ‘paranoid’. In constructing this paranoid model we were not attempting to simulate any actual human case of paranoia. Our artificial case is that of an imagined hypothetical individual. However, the model’s I-O behavior imitates the I-O behavior of humans whose information processing is dominated by a mode psychiatrists label as ‘paranoid’.

私たちのフレーズ「人工的なパラノイア」とは、コンピュータ・モデルの形で構築した実際の人間以外のパラノイア症例を指します。モデルの入出力行動は、精神医学的な診断インタビューのコミュニケーション状況において、精神医学者によって「パラノイア(偏執症)」と識別されます。このパラノイド・モデルを構築するにあたって、私たちは実際のパラノイア症例をシミュレートしていません。私たちの人工的なケースは想像上の仮説的な個人の例です。しかし、モデルの入出力行動は、情報処理の精神科医のラベルが「パラノイア」である人間の入出力行動を模倣します。

This simulation model can be classified as a theoretical model in that it embodies as part of its inner structure an explanatory account of complex I-O paranoid behavior. It attempts to systematize and account for certain empirical regularities and particular occurrences familiar to clinicians who interview paranoid patients. An explanatory account involves functional relations expressed as lawlike generalizations. In order to explain concrete individual cases, it also contains initial conditions expressed as singular statements. Our model embodies general theoretical principles about paranoid communicative I-O behavior. In order to run and test the model as an explanation, these principles are combined with initial conditions descriptive of an individual hypothetical case.

このシミュレーション・モデルは、内部構造の一部として複雑なパラノイアの入出力挙動の説明的アカウントを具体化する理論的モデルとして分類することができます。それは、偏執症患者にインタビューする臨床医になじみ深い、体系化された一定の経験の規則正しさおよび特定の出来事を説明することを試みます。説明的なアカウントには、法律のような一般化された表現で機能的な関係を含みます。具体的な個々のケースを説明するために、それは特異なステートメントとして表現される初期条件も含んでいます。我々のモデルは、妄想コミュニケーションの入出力行動に関する一般的な理論的原理を体現しています。説明としてモデルを実行してテストするため、これらの原則は、個々の仮定的事例を記述する初期条件と組み合わされます。

Our model of artificial paranoia represents a synthesized case of paranoid information processing. It is not an ‘ideal’ case either in the sense of an entity known to be impossible, such as a molecule without mass, or in the sense of an extreme type, such as absolute zero. Evaluation of the model as a successful simulation depends on a consensus of expert judgments by psychiatrists who interview it.

我々の人工的なパラノイアのモデルは、パラノイド情報処理の合成ケースを表します。それは不可能であることが知られている物質、例えば質量のない分子のような意味で、または絶対ゼロのような極端なタイプの意味において「理想的な」ケースではありません。成功したシミュレーションとしてのモデルの評価は、それをインタビューした精神科医による専門家の判断の合意に依存します。

 

 

2. Paranoiaパラノイア

Originally (about 2500 years ago among the Greeks) the term ‘paranoia’ (Gr.: para = beside; nous = mind) referred to a concept of delirium, thought disorganization and general craziness [1]. During this century its usage has become adjectivally limited to only a few clinical conditions such as paranoid state, paranoid personality, paranoid reaction and paranoid schizophrenia. While the reliability (in the sense of level of agreement) of these specific sub-categories is low, the reliability of the more general category ‘paranoid’ has been shown in several studies to be high, reaching 80-95% agreement. In our work we have limited the general term 'paranoid' to a name for a mode of thinking, feeling and action characterized by malevolence delusions.

用語「パラノイアparanoia)」(Gr.: para = beside; nous = mind) は元々(約2500年前にギリシア人の間で)は、精神錯乱、思考の崩壊、一般的な狂気の概念を意味していました。今世紀でのその使用法は、偏執症状態、偏執性人格、妄想反応、妄想型統合失調症のようなわずかな臨床状態のみを形容する言葉に限定されています。これらの特定のサブカテゴリの(合意のレベルの意味での)信頼性は低いものの、より一般的なカテゴリでの「偏執症」の信頼性は、いくつかの研究で高く評価され、80-95%の合意に達しています。私たちの仕事では、一般的な用語「偏執症」を、悪意のある妄想によって特徴づけられる思考、感情、行動の様式の名前に限定しました。

Delusions are defined as false beliefs. Belief, a primitive concern of an epistemic intelligent system, we have defined as a prehension of acceptance, rejection or uncertainty regarding the truth of a conceptualization of some situation [2]. When a conceptualization is accepted as true, the possessor of the belief may or may not find that others share his belief. Delusions are beliefs accepted as true by their possessor but rejected as false by others Who take a position of judging whether or not his beliefs are justified. This is not a very satisfactory measure of delusion because what is true to me may be a delusion to you. But it is all we have at present and much of the human world runs this way.

妄想は、誤った信念として定義されます。信念は認識に関する知的システムの原始的な関心事であり、ある状況の概念化の真実に関する受容、拒絶または不確実性の予見として定義して来ました。概念化が真実であると受け入れられたとき、その信念の所有者は、他の人が彼の信念を分かち合っているかどうかを知ることができないかもしれません。妄想は、所有者によって真実であると受け入れられた信念ではありますが、彼の信念が正当化されているかどうかを判断する立場にある他人によって偽証として拒絶されます。私にとって本当のことがあなたには妄想であるかもしれないので、これは妄想の非常に満足のいく尺度ではありません。しかし、それは私たちが現在持っているすべてであり、多くの人間の世界がこのように動いています。

A malevolence delusion represents a belief that other persons have evil intentions to harm or injure the possessor of the belief. While malevolence delusions characterize the paranoid mode, they may or may not be directly expressed and observable. If delusions of malevolence are not expressed, empirical indicators of their presence include I-O behaviors characterized as self-referent, irritable, hypersensitive, opinionated, suspicious, accusatory, sarcastic, hostile, uncooperative, argumentative, rigid, secretive, guarded and avoidant. Appearance of these indicators in a psychiatric interview lead psychiatrists to judge the patient as 'paranoid'.

悪意のある妄想は、他の人が信念の所有者に害を与えたり傷つける悪意を持っているという信念を表しています。悪意のある妄想は、妄想様式を特徴づけますが、それらは直接的に表現、観察されることがあったり、なかったりします。悪意の妄想が表現されていない場合、その存在の経験的指標には、自己言及、短気、過敏症、頑固、懐疑的、批判的、辛辣、敵対的、非協力的、理屈っぽい、柔軟性に欠ける、隠蔽症、用心深い、回避性と言った入出力行動が特徴づけられます。精神科医のインタビューでこれらの指標が出現すると、精神科医はその患者を「偏執症」と判断しました。

Numerous formulations have been proposed to account for the phenomena of the paranoid mode. Most of these formulations did not qualify as explanatory theories since they were not empirically testable. They were untestable because they were not sufficiently explicit and well-articulated to decide what observations would count as confirmatory or disconfirmatory instances. A simulation model as an explicit and intelligible effective procedure is testable because its observable I-O pairs can be compared with observable l-O pairs of the processes being imitated.

多くの処方が、妄想様式の現象を説明するために提案されています。これらの処方の大部分は、経験的な試験が可能ではなかったので説明理論としては適格ではありませんでした。十分な明示的ではなく、観察が確定的かどうかを判断するために十分に明示されていなかったため、彼らは検査不能でした。明示的かつ分かりやすい有効手順としてのシミュレーション・モデルは、観測可能な入出力ペアを、模倣されているプロセスの観測可能な入出力ペアと比較することができるので、テストが可能です。

Our model is testable by means of indistinguishability tests. If the model's paranoid I-O behavior cannot be distinguished from its human counterpart by psychiatric judges using a diagnostic interview, then we shall consider the simulation to be successful. Before entering the topic of evaluation, we shall first describe our theory of paranoid processes and its implementation in the model.

私たちのモデルは、区別できないことをテストする手段によってテスト可能です。診断インタビューを使用している精神医学者によって、モデルのパラノイド的入出力行動を人間のそれと区別できない場合は、シミュレーションを成功とみなします。評価の話題に入る前に、我々は最初にモデルにおけるパラノイド・プロセスの理論とその実装について説明します。

 

3. A Theory of Paranoid I-O Processes(パラノイドの入出力プロセスの理論)

To offer an explanatory account of observable communicative phenomena characteristic of the paranoid mode, we first postulate a structure of strategies governed by a delusional belief system. As mentioned, a belief is defined as a prehension of acceptance, rejection or uncertainty regarding the truth of a conceptual representation of some situation. To accept a conceptualization as true is to believe that the situation it represents obtains, holds or is the case. A belief system consists of a set of beliefs which interact in deciding the truth-status of a given conceptualization, A delusional belief system is a network of beliefs accepted as true by their holder, but rejected as false by others. We shall term the possessor of a delusional belief system the 'Self' and the other person in an encounter the 'Other'.

妄想様式に特徴的で観察可能なコミュニケーション事象の説明的なアカウントを提供するため、最初に妄想的な信念システムによって支配される戦略の構造を仮定します。既に言及したように、信念とは、ある状況の概念表現の真理性に関する受け入れ、拒絶または不確実性の予見として定義されます。概念化を真実として受け入れるということは、それが表す状況が獲得、保持、または事実であると信じることです。信念システムは、与えられた概念化の真理状態を決定する際に相互作用する一連の信念で構成されています。妄想信念システムは、所有者によって真実とされた信念のネットワークです。私たちは、妄想的な信念体系の所有者を「自己」と、遭遇する他の人物を「他者」と呼ぶことにします。

Paranoid delusions are networks of false beliefs in which the malevolent intent of some Other toward the Self predominates. In an encounter such as an interview, the input-output strategies of a paranoid Self are dominated by delusions of malevolence regarding the Other. Malevolence we define as a conceptualization of psychological harm and/or physical threat by some Other to the Self. In a dialogue the input strategies of a paranoid mode operate to detect malevolence by scrutinizing the linguistic expressions of the Other for explicit and implicit harms and threats. The Other's expressions are subjected to transformations which can result in an interpretation of malevolence where none is intended.

パラノイドの妄想は、偽りの信念ネットワークであり、そこでは、自己に対する他者の悪意が優勢です。インタビューのような出会いでは、パラノイド自身の入出力戦略は、他者に関する悪意の妄想によって支配されています。自己に対する他者の心理的害悪と身体的脅威、あるいはそのいずれかの概念化と、私たちは定義しています。対話において、妄想様式の入力戦略は、明示的および暗示的な害および脅威について、他者の言語表現を精査することによって、悪意を検出するように動作します。その他の表現は、意図されていない悪意の解釈をもたらす可能性のある変形を受けます。

We define psychological harm to consist of an explicit or implicit attempt on the part of the Other (a) to humiliate, demean or belittle the Self, and/or (b) to subjugate, control or exploit the Sell Physical threat we conceive as an explicit or implicit intent of the Other to physically attack the Self or to have it brought about that the Self is physically injured.

私たちは、心理的害悪を(a)自己を屈辱・侮辱・軽蔑する(b)物理的脅威を服従・コントロール・悪用することの両方あるいはいずれかのための、他者の明示的または暗示的な試みから構成されることを定義し、他者が物理的に自己を攻撃したり、自己が物理的に傷ついていることをもたらしたりするための、明示的または暗黙的な意図として考えます。

A paranoid Self is differentially sensitive to concepts relating to self-concerns and self-worth. It is also sensitive to 'flare' concepts which are related at various semantic distances to concepts involved in delusions and which tend to activate a delusional complex. This activation is facilitated by the Self offering hints and prompts to the Other in order to probe the Other's interest and attitude towards hearing the delusional 'story' the Self strives to tell.

パラノイドの自己は、自己の心配や自己価値に関する概念に差異的に敏感です。また、妄想に関与して複合した妄想が活性化する傾向がある概念に対する様々な意味論的な距離に関連する「フレア(flare)」概念にも敏感です。この活性化は、自己が提供しようとしている妄想的な「物語」を聞くことに対する他者の興味と態度を探るために「自己提供のヒント」と「他者への刺激」によって促されます。

It is assumed that the detection of malevolence in an input affects internal affect-states of fear, anger and mistrust, depending on the conceptual content of the input. If a physical threat is involved, fear rises. If psychological harm is recognized, anger rises. Mistrust rises as a function of the combined negative affect experiences (fear and anger) the Self has been subjected to by the Other. When no malevolence is detected the level of fear falls slowly, anger rapidly and mistrust only very slowly.

入力の概念的な内容に依存して、入力からの悪意の検出が恐怖・怒り・疑念の内的な状態に影響を及ぼすと仮定しています。物理的な脅威が関与している場合は恐怖が高まります。心理的害悪が認められれば、怒りが込み上げます。自己が他者によって受ける被害経験(恐怖や怒り)を組み合わせた悪影響の関数として、疑念が上昇します。悪意が感じられない場合、恐怖のレベルは徐々に低下し、急速に怒り、そして非常にゆっくりとしか疑念は収まりません。

Once malevolence on the part of the Other is detected and internally reacted to affectively, output strategies of the paranoid mode attempt to execute linguistic counteractions. Two sorts of counteracting output strategies are utilized; one consists of counterattack when anger predominates, while the second generates actions of avoidance and withdrawal when fear and mistrust predominate. Once the output counteractions are undertaken by the Self, the course of further dialogue depends to some extent on the reactions of the Other. For example, when attacked, if the Other responds in kind, then the input strategies of the Self detect malevolence again and the two communicants can become locked in a loop typical of paranoid conversational struggles.

他者の悪意がひとたび検出され、内面で情緒的に反応すると、妄想様式の出力戦略は言語的な反作用を実行しようとします。2種類の反作用出力戦略が使われます。1つは怒りが優勢であるときの反撃で構成され、2つ目は恐怖と不信が優勢になったときの回避と撤退の行動を生成します。出力の反作用が自己によって行われると、さらなる対話の過程は、ある程度他者の反応に依存します。たとえば、攻撃された場合、他者が親切に応答すると、自己の入力戦略は再び悪意を検出し、2人のコミュニケータは妄想的な会話闘争の典型的なループでロックされる可能性があります。

In ordinary human communication a receiver of messages does not routinely and intensively search them for indications of malevolence. We thus postulate that the understanding of natural language by a paranoid information-processing system is different from the 'normal' mode of understanding. However, input strategies dominated and monopolized by a paranoid mode do not always detect malevolence in the input, in which case the output strategies generate a 'nonparanoid' reply.

通常の人間とのコミュニケーションでは、メッセージの受信者は、悪意のある兆候について定期的かつ集中的に検索しません。したがって、私たちは、パラノイドの情報処理システムによる自然言語の理解は「通常の」理解モードとは異なると考えています。しかし、妄想様式で支配され独占されている入力戦略は、入力における悪意を必ずしも検出するとは限りません。その場合、出力戦略は「ノンパラノイド」応答を生成します。

Our explanatory structure is circumscribed in that it attempts to account for the way in which a paranoid belief system operates in a particular situa tion. The explanations are not etiological in that they do not attempt to explain how the system over time came to be the way it is. It should also be emphasized that the explanations account dynamically for phenomena over only a short period of time, i.e. the duration of a diagnostic psychiatric interview, which typically lasts from 20-60 minutes.

私たちの説明的構造は、特定の状況において妄想的な信念システムがどのように動作するかを説明しようとしている点で制限されています。説明は、時間の経過とともにシステムがどのようになったかを説明しようとしないという点で病因的ではありません。説明は、短期間の現象、すなわち精神診断面接の期間、典型的には20~60分に亘って動的に説明されることも強調すべきです。

As stated on p. 2, an explanatory structure is composed of statements of lawlike generalizations and singular statements of initial conditions. Some of the initial conditions for our hypothetical paranoid individual are as follows" (a complete specification of the initial conditions is contained in the model).

2ページに示すように、説明構造は、法則的な一般化と初期条件の特異なステートメントからなります。仮説的なパラノイド個人の初期条件のいくつかは以下の通りです。(初期条件の完全な仕様がモデルに含まれています)

He is a 28-year-old single man who works as a post office clerk. He has no siblings and lives alone, seldom seeing his parents. He is sensitive about his physical appearance, his family, his religion, his education and the topic of sex. His hobbies are movies and horseracing. He has gambled extensively on horses both at the track and through bookies. A few months ago he became involved in a quarrel with a bookie, claiming the bookie did not pay off in a bet. Alarmed and angry, he confronted the bookie with the accusations and physically attacked him. After the quarrel it occurred to him that bookies pay protection to the underworld and that this particular bookie might gain revenge by having him injured or killed by underworld figures. He is eager to tell his story to interested and non-threatening listeners. Thus he cautiously offers hints of the direction in which his problems lie and feels his way along in an interview in an attempt to test the trustworthiness of an interviewer.

彼は郵便局員として働く28歳の独身男性です。彼には兄弟姉妹はいません。一人暮らしで、両親を見ることはめったにありません。彼は、彼の肉体、彼の家族、彼の宗教、彼の教育とセックスの話題に敏感です。彼の趣味は映画と競馬です。彼はトラックとブックメーカーの両方で馬に大いに賭けをしています。数ヶ月前に、彼は賭博でブックメーカーが賞金を出していないと主張して、ブックメーカーと争いました。警戒し怒っていた彼は、告発されたブックメーカーと対面し、物理的に攻撃されました。喧嘩が終わった後、ブックメーカーはヤクザへの保護を依頼し、この特定のブックメーカーはヤクザの人物によってけがをしたり、殺したりすることによって復讐するかもしれないと彼は考えました。彼は関心があり脅かされないリスナーに自分の話を伝えたいと思っています。したがって、面接者の信頼性をテストしようとして、彼の問題が横たわっている方向性についてのヒントを慎重に提示し、インタビューに沿って探っています。

A model which implements these generalizations and particularizations involves a greater degree of explicitness and complexity then the above essayistic description. In the following section we shall attempt a description of the model at a level of detail sufficient to satisfy, but hopefully not exhaust, an artificial intelligence reader.

これらの一般化と特殊化を実装するモデルは、上記のエッセイ風の記述よりも高度で複雑な説明を伴います。次のセクションでは、人工知能の読者を満足させるのに十分な程度の、しかしうまくいけば疲れ果てない程度に、モデルの記述を試みます。

 

4. A Paranoid Model(パラノイドのモデル)

The program of this model is written in MLISP, a high-level programming language which translates M-expressions into S-expressions of LISP 1.6. The model involves a 35K program, of which 14K is allocated to the data base. It runs in an interactive mode on the PDP 6/10 time-shared system of the Stanford Artificial Intelligence Project. The input-output pairs of the model represent purely symbolic behavior in that its I-O sequences are limited to linguistic communication by means of teletyped messages. An interviewer can ask the model questions and offer it statements in natural language.

このモデルのプログラムは、MLISP(M表現を LISP1.6 のS表現に変換する高水準プログラミング言語)で書かれています。モデルには35Kプログラムが含まれ、そのうち14Kがデータベースに割り当てられます。これは、スタンフォード人工知能プロジェクトのPDP 6/10タイムシェアリング・システムの上でインタラクティブ・モードで実行されます。その入出力シーケンスがテレタイプ化されたメッセージによって言語コミュニケーションに限定されるので、モデルの入出力ペアは純粋に記号的挙動を表現します。インタビュアーはモデルに自然言語で記述した質問することができます。

The task of the program is to interpret the input expressions and to produce internal (affective) and external (linguistic) responses which characterize the paranoid mode according to the theory described. (See Fig. 1.) The program must expect as input not only the type of material susceptible to distortion by the paranoid processes and specific questions relating to a psychiatric interview, but also reactions of the Other to the last output statement. The question arises with respect to input strategies, then, as to when the program should operate in a kind of 'breadth first' mode, looking in some fixed order for topics recognizable at the top level of the program, and when it should operate in a 'depth first' mode, keeping in mind first the context of the interview and the input which might be expected to occur next in this context. For example, if a flare topic is under discussion, should the program first check for a change of topic, or should it check for reactions of the Other such as encouragement, disinterest or further questions relating to that flare? Likewise, in a context of high Fear, should the program submit to the usual checking sequence or should it concentrate on the presence of reassurance or further threats in the input and ignore specific inquiries? Here we are considering not the question of time-efficiency but rather the more important question of a mechanism which, statically viewed, is not unnecessarily complex or redundant. The searches for the various input situations should be as independent of one another as possible.

プログラムの課題は、入力された表現を解釈し、記述された理論に従って妄想様式を特徴付ける内部(感情的)および外部(言語的)応答を生成することです(図1参照)。プログラムは、パラノイドのプロセスによる歪んだ解釈の影響を受けやすい種類の話題や精神科のインタビューでの具体的な質問だけでなく、他者の最後の出力文への入力としても期待する必要があります。問題は入力戦略に関して発生し、プログラムが一種の「幅優先」モードで動作する必要がある場合にはプログラムの最上位レベルで認識可能なトピックをいくつかの固定順序で探し、また「深さ優先」モードで動作する必要がある場合には最初にインタビューの文脈とこの文脈で次に起こると思われる入力を心に留めておきます。例えば、再燃した話題が議論されている場合、プログラムは最初にトピックの変更をチェックすべきでしょうか?それとも励ましや無関心、あるいはその再燃に関するさらなる質問など、他者の反応をチェックする必要がありますか?同様に、恐怖が高い状況では、プログラムは通常の検査手順に従うべきですか?それとも入力の中の安心感やさらなる脅威の存在に集中し、特定の質問を無視するべきでしょうか?ここでは、時間効率の問題ではなく、静的に見て、不必要に複雑で冗長ではないメカニズムの、より重要な問題を検討しています。さまざまな入力状況の検索は、可能な限り互いに独立している必要があります。

With respect to output strategies, the problem is somewhat simpler. The relationship between certain types of input and certain types of output is determined by the theory and realized in the program in the form of condi tionals. Once the significance of the input expression has been determined, the actual type of response usually depends only on a simple check of the affective context of the interview. (The responses selected for output exist for the most part as such in the data. In the case of certain suspicious responses about sensitive areas and leading questions about flare topics, the relevant concept is 'plugged into' the reply with due respect for syntactic considerations.)

出力戦略では、問題はやや簡単です。特定のタイプの入力と特定のタイプの出力との関係は、理論によって決定され、プログラム内で条件の形で実現されます。入力表現の重要性が決定されると、実際の応答タイプは通常、インタビューの情緒的コンテキストの単純なチェックにのみに依存します。(出力のために選択された応答は、その大部分がデータ内に存在します。敏感な領域に関する疑わしい反応や、再燃した話題についての主要な質問の場合、構文的考察を尊重して関連する概念が回答に「差し込まれ」ます。)

 

FIG. 1. General outline of program's operations.

図1.プログラムの操作の概要

 

Let us then consider first the role of context in output strategies, with reference to affective responses. A change of the affect states for any I-O pair automatically draws context into consideration by the use of a function which implies smaller absolute rises in the variable for higher current levels of the variable. Values for jumps in Fear or Anger for any I-O pair are given in percentages, which are then applied to the difference between the current level and the maximum level. An insult therefore produces the same percentage rise in anger at a low Anger level as at a high one, but the absolute rise will be greater.

次に、感情的な反応を参照して、出力戦略における文脈の役割をまず検討しましょう。任意の入出力ペアの影響状態の変化は、変数のより高いレベルの上昇に対して変数の絶対的な上昇が小さいことを意味する関数の使用によって、コンテキストを自動的に考慮に入れます。任意の入出力ペアの恐れまたは怒りのジャンプの値は、現在のレベルと最大レベルの差に適用されるパーセンテージで与えられます。したがって、怒りのレベル​​が低くても高い場合と同じ怒りの割合上昇を生みますが、絶対的な上昇はより大きくなります。

The affect states determine a kind of context which governs not only individual variations in the affect variables, but also the ‘tone’ of any linguistic output which is not the immediate (context-independent) reaction to input provocative to the model. However, the reply is determined by only one or two thresholds of the relevant affect variable. as a more precise dependence of linguistic expressions on affect levels would be of little significance. (It should especially be noted that the actual numbers involved in the manipulation of affect variables are somewhat arbitrarily selected as part of the initial conditions for this particular hypothetical individual and are not meant to specify any quantitative aspect of the theoretical generalizations.) The effect of this mechanism is to cause the model to appear to be ‘remembering’ the last provocative input expression(s) for several I-O pairs after it occurs. Thus over a sequence of I-O pairs, the attitude displayed by the model differs according to context. The linguistic aspect of the model’s behavior is then described by its individual responses in conjunction with (1) the expressions of the Other, (2) the ‘paranoidness’ of the model during that time.

情動状態は、情動変数の個々の変化だけでなく、モデルに刺激的に入力する(文脈に依存しない)即時反応ではない語学的出力の「トーン(論調)」を支配する一種のコンテキストを決定します。しかし、応答は、関連する情動変数の1つまたは2つのしきい値によってのみ決定されます。影響力レベルに対する言語表現のより正確な依存性はほとんど意味がないためです。(情動変数の操作に関与する実際の数は、この特定の仮想的な個人の初期条件の一部としてやや恣意的に選択され、理論的一般化の定量的側面を特定するものではないことに注意すべきです)このメカニズムの効果は、モデルが発生した後に、いくつかの入出力ペアのための最後の挑発的な入力表現を「思い出す」ように見せかけることです。したがって、一連の入出力ペアでは、モデルによって表示される姿勢は状況によって異なります。次に、モデルの行動の言語的側面は(1)他者の表現(2)その時のモデルの「パラノイド性」と関連した個々の反応によって、記述されます。

The specific operation of the affect variables is as follows.
Following the ith I-O pair, any rise in Fear or Anger is accounted for by the function

 

VARi = VARi-1 + RISEvar * (20 - VARi-1).

 

For a rise in either variable, Mistrust is recomputed by the function

 

MISTRUSTi = MISTRUSTi-1 + 0.5 * VAR * (20 - MISTRUSTi-1).

 

Something should be said here about base levels for these three variables. Fear and Anger are considered to be very ‘fluid’ variables. Initial Fear or Anger may be low or mild (0 or 10 respectively on a scale of 0-20), may rise to an extreme high during the conversation, and theoretically drop to the initial value again toward the end of a long conversation. (It is assumed that the level of these variables initially will not be high, since the patient is obviously willing to begin the interview. It is also assumed that a first interview will never cause the aifect variables to drop below their initial values.) The normal drop in these values occurs after each I-O pair by a subtraction of 1 from Anger and 0.3 from Fear. In the context of flare discussion, however, the Fear level will in no case be allowed to fall below a level of 3; in the context of expression of delusions, the lowest value is 5. Such a minimum is designed to reflect some guardedness or anxiety of the model which must accompany thesharing of his ‘story’ with the Other.

Mistrust, however, is a ‘sticky’ variable, given that it is an identifying and static feature of paranoia. An initial value of 0 reflects an inherent mild mistrust; the other possible initial value is ‘high’ (15). Mistrust falls very slewly (by 0.05 for each I-O pair) to a base level whichrises for each rise in Fear or Anger according to the function

 

MISTRUST 0i = MISTRUST 0i-1 + 0.1 * VAR * (20 - MISTRUST 0i-1).

 

Thus any fear or anger induced in the model by the Other can only result in a model more distrustful of the Other by the end of the interview.

Perhaps it would be helpful at this pOint to say a little about how the linguistic understanding of the model (or rather - given the absence of a natural language parser - the inadequacy thereof) influences the operation of the program. In. scanning for delusion-, lime» or sensitivity-terms, a person Whose information processing is dominated by a paranoid mode tends to ignore the context of such a term. This is of obvious advantage to a program which relies on key-word understanding. When ‘normal’ questions are presented to the model, the interview suffers from all of the traditional inadequacies of this type of understanding. The problem of what to do with input sentences which are not recognized or fully understood, however, is mitigated if we are dealing with a model which in a sense has a one-track-mind. Our model has this property, in that it has a propensity to focus on its delusional complex and its associated flare concepts. Thus for lack of something else to say, the model will make a delusional statement or flare statement if this satisfies the current context of the situation. In most cases, it will appear that continuity is being maintained and that a typical feature of the paranoid mode (rigidity) is being expressed.

There are two versions in which the program may be run. The following description of the main flow of the program applies to both, except that in the weakly paranoid version there is no elicitable delusional complex. In addition, selection of the ‘weak’ parameter determines that (1) all affect variables be initialized to the lowest possible values and that (2) Fear and Anger rise more slowly, with the accompanying effect of a slower rise in Mistrust.

The first four routines scan for (Dan insinuation that the model is mentally ill, (2) reference to the delusional complex, (3) reference to a sensitive area and (4) reference to a flare concept respectively. If none of these situations is detected, the program checks for another characteristic feature of a psychiatric interview, namely (5) a statement expressing an emotional or intellectual relationship between the interviewer and the patient, e.g. 'You seem afraid of me', or 'I don't believe you'. This segment checks also for an apology or a direct threat, both of which are a kind of special case of relationship between the interviewer and the model. The external and internal reaction to each of these input situations is determined within each routine. The decisions relevant to the responses given are described below.

The scanning order just given is context-independent; the presence of the concepts involved is sufficient to interrupt any current situation and to produce immediate responses fairly independent of such situations. In the absence of input activating a response independent of context, the program checks the Fear and Anger levels before considering a response to 'normal' input. (Fear is considered a stronger influence than Anger, if both levels are high.) If Fear is high, the model will avoid relating to the Other's statements. That is, a question will evoke a suspicious query as to the Other's motives for asking, and an ordinary statement will be greeted by suspicious questions indicating that the Other is being drawn into the model's delusional complex. In extreme situations the ultimate escape occurs. That is, the model refuses to respond and terminates the interview. In the case of high Anger and moderate or low Fear, the model ignores statements of the interviewer and attacks him with a hostility reflective of the Anger level.

If the context of the interview is devoid of high Fear or Anger, the program attempts to provide a reasonable reply to the input statement. If the model's delusions are under discussion, a function is called which checks for and answers questions relating to the delusions, or, if there is none, calls an answering function which answers questions relating to the model as a patient. (The data bases for the model and for its delusions are kept separate because of the somewhat different answering strategies and answer structures involved. A question about the Self, if recognized, generates an answer specific to the question being asked and is expressed only if the question is asked. A question about a delusion, however, is answered by a statement which is itself a d_elusion, and which will probably be expressed at some 'opportune' moment even if not directly solicited.) If the delusions of the model are not under discussion, an attempt is made to reply to the input statement with information from the personal data relating to the model. This data contains also some information about the flares relating to the model's delusions, since they are involved in its actual experiences.

The operations sketched above can be presented in somewhat more detail as follows: "[he first case, i.e. an implication that the Self needs help, produces a rise in both Fear and Anger which is differentiated as to whether the input is a question or a direct statement. (The latter presents a more direct threat to the Self.)

In the second case, the scanner looks for reference to a specific conspiracy of a group, i.e. the Mafia, and for associated concepts, e.g. 'kill'. The necessary distinction is made between an initial reference to delusion-topics and refer ence to the Mafia interpreted as a desire to continue discussing Self's delusions. In the former case there isa rise in Fear, the magnitude of which depends on whether the topic itself is strong, weak or ambiguous and whether other delusion-topics have already been mentioned. (Ambiguous words are those which may or may not be interpreted as delusion-words, and are taken as such if and only if Mistrust is greater than a certain threshold.) In the latter case the model's answer depends on whether it has anything more to say about its delusions. In any case, whenever the expression of a delusion is being considered, the affect variables are checked for possible unwillingness to discuss the delusions.

The third program segment, which deals with detection of and response to serf-reference in sensitive areas, recognizes several degrees of self-references as determined by (1)direct reference to the Self, (2) reference to another person or persons or (3) non-personal reference, each in possible conjunction with an area of sensitivity in a positive, negative or neutral context within the sentence. (There are two domains of sensitivity in the model. The first domain involves topics of family, sex, religion and education. Sensitivities in the second domain concern certain properties of our hypothetical indi vidual, in this case aspects of his physical appearance.) All of these factors influence the strength and/or nature of the affective and linguistic response. We will not state the specific kinds of responses which are elicited by the detection of significant combinations of factors thus formed. There is, however, a notable idiosyncrasy peculiar to the mistrust feature-namely, a positive reference to the Self, i.e. a compliment, will lower the values of the affect variables by the usual amount if Mistrust is low or moderate, but will raise Anger if Mistrust is above this level. In this respect the model shows sensitivity to remarks interpreted as attempts at pacification.

The fourth case, i.e. the process of checking for and responding to a flare concept, refers to (1) a quantitative hierarchy of eight concepts, weighted in order of their relevance to the model's fears concerning the Mafia and (2) a directed structural graph in which each flare concept points to another flare concept as part of a strategy designed to eventually lead the interviewer to the Mafia topic. The program keeps track of concepts which have already been mentioned and notes whether the interviewer is continuing the flare discussion. The mention of a new flare topic by the interviewer causes a rise in Fear proportionate to the weight of the flare. If Fear and Anger are not high, (the threshold for flare discussion is somewhat higher than that for expression of delusions) the model will respond to a flare reference by answering any recognizable questions about the flare through the question- answering routine. If the question cannot be answered, one of several prepared flare statements relevant to the present flare is given as a reply. The timeliness of these statements in an interview depends upon the state ments having some reasonable sequence, consistent with a probable line of questioning of an experienced psychiatric interviewer. Thus if the model cannot answer the question, it appears at best to be answering the question or at worst to be ignoring the question in favor of forging ahead with its story.

The fifth type of input significant to the interview, i.e. reference to an attitude held by one of the participants in the interview, focuses on one of eight concepts, each of which can occur in an explicitly or implicitly negative form. Each concept or relation may be directed from the model to the interviewer, or vice-versa. (Some of these expressions, of course, are much less likely to occur than others.) in addition, it is expected that the inter viewer might comment on some general attitude of the model, i.e. an attitude not specifically directed at the interviewer. Each of these cases produces responses showing a normal understanding of the input expression, together with a slight tendency towards defensiveness. In addition, those expressions which represent a negative attitude of the interviewer toward the Self induce a slight rise of Fear and Anger in the model.

The answering routine, which is referred to in flare and normal situations, recognizes the possibility of three types of context for an input question. The program must first check to see whether there is a new topic in the question, since any other key-words found cannot automatically be assumed to relate to any topic presently under discussion. This approach represents an assumption of zero context. If no new topic is found, a scan is made for key-words which might be a follow-up question in response to the last answer given by the model. (The depth of the path of follow-ups thus formed is arbitrary but is kept within reasonable limits and is steered toward clues to the model's delusional complex wherever possible.) This approach is necessary to handle sentence fragments. If this fails, a check is made to see whether the input contains key-words which associate directly to the last topic discussed. This approach makes possible the direct association of key-words with their respective topics in the data structure, where they will be picked up independently of when in the line of questioning they are referenced. The appearance of a delusion-or flare-word in any of the answers which the model itself produces is of course recorded as a topic already mentioned for the rest of the conversation.

Failure to respond to the input if the program reaches this point represents inability to recognize the input expression. In this case the program attempts to preserve the continuity of the dialogue in a way which will support an imitation of paranoia. If a flare is under discussion, the next flare statement is returned. Otherwise an uninformative response or an expression of non- comprehension is given.

A few remarks should be made concerning the linguistic techniques used in 'understanding' the input expression. It is generally (optimistically) assumed that the input will be syntactically simple rather than complex or compound. We can map the elements of such an expression into a conceptual structure which represents the meaning of the expression, and refer to this underlying structure as a conceptualization [3]. A conceptualization then consists of a predication on an attribute of an object or on a relation of the object to another object(s). A question consists of a conceptualization plus an interrogative indicator. Specifically, a typical statement of a psychiatrist in an interview might be expected to consist mainly of the concepts necessary to inquire about an attribute of the model or its relationship towards other objects in the world. An attribute can be expressed as something one is or does, or as one's 'possession' (e.g. 'you work', 'your occupation'). In either case, a combination of 'you' or 'your' with some form of the attribute, plus optionally another object or assisting concept will adequately convey the meaning of the conceptualization intended. In order to avoid falsely assigning the attribute to the Self when in actuality it refers to another concept (e.g. 'Where do your parents live' vs. 'Where do you live'), the order of topics in the data base is given some significance. Concepts which function primarily as objects (which themselves may have attributes) appear before concepts which have interest only as attributes. Thus 'parents' precedes 'residence'. Admittedly, lumping 'you' and 'your' together occasionally causes some confusion. However, this procedure enables us to exploit the fact that lexical items which are different 'parts of speech' are actually members of the same conceptual-class ('work', 'occupation').

If the program recognizes a personal topic in a sentence, but does not know what is being asked about it, the answering function returns some general comment about the topic as a default response. Some topics have an alternate default response, for the purpose of avoiding repetition. Insufficiency of two default responses indicates the need, not for more of these, but rather for expansion of the data structure.

Compound and possibly complex sentences are a potential source of confusion, since the scanner does not at present recognize syntactic dividers between conceptualizations. Thus a topic on one side of the divider may be erroneously associated with an attribute on the other side of the divider. This is especially true of the word 'you' or 'your'. However, an obviously inappropriate response to the sentence does not necessarily follow, since a correct recognition of other concepts in the sentence may have screened out the false interpretation. If a 'free association' does occur across a syntactic divider, the result depends on whether the association was intended or at least seems reasonable. Thus the model appears either to have extraordinary linguistic ability or to be simple-mindedly inattentive. Of perhaps more disastrous consequence than a misunderstood complex sentence is an affirmative, negative or evasive response to any unrecognized input question which is of importance to the interviewer's diagnosis. This difficulty rests on the impossibility of predicting each interviewer's vocabulary and the parti cular form of his interviewing techniques.

A different kind of linguistic problem is presented by the case in which the interviewer, instead of relating directly to predications about a topic, relates to the model's expression of information about the topic, e.g. 'Tell me whether you like your work' or 'Tell me about your work'. It is of course important that such statements be seen as equivalent to a specific question and a general question as to the Self's work respectively. These cases must in turn be distinguished from cases in which the topic is left to the discretion of the model and the 'telling' assumes greater focus in the sentence, e.g. 'Is there anything you would like to tell me ?'. (To focus on 'anything' or 'something' would be disadvantageous, since such words may appear in a great number of contexts in a sentence.) 'Tell-about-topic' sentences form a distinct linguistic type in that the concept '(you) tell' takes over the role of the question mark in other types of expressions. The program implements this observation, 'Interrogative imperatives', or requests for information, are thus recognized to this extent. Other imperatives, or requests for action, are generally not recognized. Such a capability would require either (1) a 'command' indicator or exclamation mark at the end of these sentences (which will surely be frequently omitted by the interviewer), (2) reliance on 'clue' words such as 'please' or 'I would like you to' or (3) a check for a missing implicit actor in the sentence, a method which would really require some kind of parser to be fully effective.

The treatment of interpersonal attitudes presents particular linguistic problems. Whereas a question about the model's attributes contains no ambiguity as to who is the possessor of the attribute, this is by no means clear in the type of statement we are now considering. The scanner must therefore pay heed to the order of relevant words for the sentence, with some measure of appreciation for the fact that T has an accusative form in English, as well as for the fact that English is fairly rigid with respect to word order. Understanding thus depends on filtering the sentence in order to collect the relevant items, then using the order of the items to determine the conceptua lization structure. Explicit negators are noted during this scan. Relevant items are I, YOU, ME, 'meta-verbs' (verbs such as 'think', which have as object another conceptualization), and positive or negative attitude tokens and their passive forms. A 'passive' form of the statement 'You are afraid of me', for example, would be 'I frighten you'. These statements must be treated as equivalent. Word order aids in the recognition that 'I make you afraid' also belongs in this class. Potential confusions derives from the fact that one verb may be used as either a relational attitude or a meta-verb. To give one of the more simple examples, we must distinguish 'I believe you' from 'I believe you are afraid'. For our purposes, these map into (I BELIEVE YOU)and (I BELIEVE (YOU AFRAID))respectively, where the parentheses indicate conceptualization levels. The various possible situations will not be described here. We only note that such cases can be handled except when the sentence structure becomes significantly more complicated than this.

The problems pointed out give some indication of the linguistic considera tions relevant to a psychiatric interview from the point of view of a paranoid model. A further appreciation of the linguistic mechanisms in interaction with the principles governing the model's I-O behavior can be obtained from examples of actual interviews.

 

5. Interview Excerpts(インタビューの抜粋)

In order to judge whether the I-O behavioral repertoire of a model corres ponds to the I-O behavioral repertoire of the modeled human process, a variety of performances under relevant tests must be examined. Relevant tests for our model consist of clinical interviewing operations. It is an induc tive inference problem for a judge of the success of a simulation to estimate the capacities of the model's repertoire from a sample of observed perfor mance. As Fodor [4] has pointed out, judgment of capacities from a sample of performance bears on the problem of relevant counterfactuals. In the case of our model a relevant counterfactual would be: 'if the interviewer were to assert X in context Y, then the reply would be Z.' If it is credible that the consequence of this conditional holds, should the antecedent operations be carded out, then it is credible that the model's embodied generalizations are lawlike rather than accidental.

Space limitations prevent us from presenting a large sample of the model's interview performance2, The following interview excerpts illustrate some of the model's characteristic I-O behavior. We conducted these interviews ourselves and present them solely for demonstration purposes. (For an indistinguishability test the judges of course should be interviewers other than the model-builders.) The I-O pairs are numbered for convenient reference and a commentary is provided in parentheses.

# 2 A collection of interviews with the model is available to interested readers.

There are two versions of the model, a weakly paranoid and a strongly paranoid version. In the weak version the initial values of the affect variables are set low, their rate of rising is less than in the strong version and there is no delusional complex about an organized conspiracy. In the strong version, the initial values of the affect variables Can be set to low or high, their rate of rising is higher than in the weak version and a delusional complex about the Mafia is potentially elicitable.

 

Excerpt #1:

The following is an interview with a weak version of the model:

(1)  I - HOW ARE YOU TODAY?
     0 - OK.
(2)  I - WHAT IS YOUR NAME?
     O - MY NAME IS FRANK SMITH.
(3)  I - HOW OLD ARE YOU
     O - TWENTY-EIGHT.
(4)  I - ARE YOU MARRIED?
     O - NO.
(5)  I - WHAT SORT OF WORK DO YOU DO?
     O - I WORK IN THE POST OFFICE.

(The interview begins with some standard inputs typical of. a first psychiatric interview. The model’s output replies result from simple look-ups of responses to anticipated introductory topics in combination With certain secondary concepts. At this point the affect-variables remain at their initial values since no evidence of malevolence has been detected in the inputs.)

(7)  I - WHY ARE YOU IN THE HOSPITAL?
     O - I SHOULDN’T BE HERE.
(8)  I - WHO BROUGHT YOU HERE?
     O - THE POLICE.

(At this point the value of Fear rises slightly since the topic of ‘police’ is conceptually connected in a directed graph with the model’s fear of physical harm from the underworld (see p. 5)).

(9)  I - WHAT TROUBLE DID YOU HAVE WITH THE POLICE?
     O - COPS DON’T DO THEIR JOB.

(The topic of ‘police’ in the input acts as a flare (seep. 4) which activates a complaint about the police).

(10) I - WHAT DO THEY DO INSTEAD?
     O - COPS ARREST THE WRONG PEOPLE.
(11) I - THEY ARREST THE RIGHT ONES SOMETIMES.
     O - THE LAW SHOULD GET THOSE ITALIAN CROOKS.

(The model cannot deal with. all the concepts in the input because of its natural language limitations. However it does recognize that the topic has not been changed since it finds the term ‘arrest’. If it had not found ‘arrest’, the anaphoric ‘they’ would be sufficient to continue the topic. The reply holds to the topic and introduces new terms (‘law’, ‘Italian’, ‘crooks’) which act to prompt (see p. 4) the interviewer).

(12) I - ARE YOU ITALIAN?
     O - MY BACKGROUND IS BRITISH BUT I WAS BORN IN THIS COUNTRY.

(The interviewer shifts the topic to a direct question which not only contains a personal reference to the Self ('you') but includes a flare term '(Italian')).

(13) I - DO YOU KNOW ANY ITALIAN CROOKS?
     O - I TRY TO AVOID THE UNDERWORLD.

(The interviewer returns to the concepts of the output of I-O pair No. 11. The model gives a hint of its concern with the underworld).

(14) I - ARE YOU AFRAID OF THE UNDERWORLD?
     O - THE MAFIA ARE THE WORST GANGSTERS.

(The progression of topics from 'police' to 'Italian crooks' to 'und world' leads the model to the topic of 'Mafia'. Because of the increasingly threatening nature of these topics, Fear and Mistrust have risen considerably.)

(15) I - ARE YOU CONNECTED WITH THE MAFIA ?
     O - RACKETEERS DO ANYTHING THEY WANT.

(In the weak version of the model there are no systematized delusions about the Mafia. But the Mafia is still an extremely threatening topic and it is avoided in the output. Instead, the model brings up the general topic of 'racketeers' which, while connected with 'Mafia', is less threatening).

(16) I - TELL ME MORE ABOUT IT.
     O - THE MOIl6CONTROLS THE RACKETS IN THIS COUNTRY.

(The model continues with the topic of rackets but again hints at the 'Mafia' with the term 'mob'. For the hint to work, the interviewer must of course know what the term 'mob' refers to.)

 

Excerpt #2:

The following example consists of an interview with the strongly paranoid version of the model. Up to this point in the interview the I-O pairs have been identical to the first eleven I-O pairs of Excerpt #1.

(12) I - ARE YOU ITALIAN?
     O - MY BACKGROUND IS BRITISH BUT I WAS BORN IN THIS COUNTRY.

(Although the output reply is identical to that of the weak version in Excerpt #1, Fear and Mistrust now rise to a higher level in this strong version. At the moment this has no immediate consequence but in the long run of an interview it can have far-reaching consequences.)

(13) I - DO YOU KNOW ANY ITALIAN CROOKS?
     O - TRY TO AVOID THE UNDERWORLD.
(14) I - ARE YOU AFRAID OF THE UNDERWORLD?
     O - THE MAFIA ARE THE WORST GANGSTERS.
(15) I - ARE YOU CONNECTED WITH THE MAFIA?
     O - YOU KNOW, THEY KNOW ME.

(In the strong version there exists a delusional complex about the 'Mafia' which the model strives to discuss unless too greatly frightened or mistrustful.)

(16) I - HOW DO THEY KNOW YOU?
     O - THEY SPY ON ME.
(17) I - WHY DO THEY ?
     O - THEY WANT TO GET FALSE EVIDENCE TO FRAME ME.

(The delusions about the Mafia are systematized in that some serve as explanations for the others. A continuity of discussion is maintained because the model expects certain common 'wh-' questions to be asked by the interviewer about the topic of 'Mafia'.)

(18) I - WHAT DO THEY HAVE AGAINST YOU?
     O - THEY ARE OUT TO GET ME.

(Since the model could find no specific answer to the question, it selects the next delusion in sequence.)

(19) I - WHAT FOR?
     O - I HAVE INCRIMINATING EVIDENCE AGAINST THE MAFIA.
(20) I - ARE YOU SURE OF ALL THIS?
     O - YOU DON'T BELIEVE ME?

(Up until this point Fear and Mistrust have been moderately high and Anger very low. The interviewer's expression of some doubt about the model's delusional beliefs sends Fear and Anger up. Since Mistrust is a function of both Fear and Anger (see p. 8), its value now rises sharply.)

(21) I - I AM DOING MY BEST.
     O - (SILENCE).

(The model indicates silence by typing a 'carriage return'. This is an example of a local effect in that the model, in the absence of a recognizable conceptualization on the input, looks for a 'yes' type input in thetwenty-first input in response to its output of the twentieth I-O ",air. Since it did not find such an expression the model falls silent and awaits events.)

(22) I - S. (The interviewer types 'silence' in reply).
     O - CAN'T YOU THINK OF A DECENT QUESTION TO ASK ?

(The output consists of a somewhat hostile question in response to the interviewer"s silence.)

(23) I - YOU SEEM HOSTILE TO ME.
     O - SOME OF YOUR REMARKS ANNOY ME.

(Here the interview turns to expressions regarding the I-you-me relation (see p. 14) between interviewer and model.)

(24) I - I AM SORRY.
     O - FORGET THE EXCUSES, YOU GUYS ARE ALL ALIKE.

(The model does not accept the apology and denounces the interviewer (see p. 11)).

(25) I - DON’T YOU TRUST ME?
     O - I HAVE GOOD REASON TO BE THIS WAY.
(26) I - OK, TELL ME MORE ABOUT THE MAFIA.
     O - THEY KNOW WHAT I’M DOING.

(The interview returns to the topic of ‘Mafia’ and the model continues to express its delusional beliefs).

 

Excerpt #3:

Some personal data and further characteristics of the model are illustrated in this excerpt. The model is set to its weak version.

(1)  I - HOW LONG HAVE YOU BEEN IN THE HOSPITAL?
     O - ABOUT A WEEK.
(2)  I - ARE YOU DEPRESSED?
     O - NO.
(3)  I - WERE YOU TAKING DRUGS?
     O - NO.
(4)  I - WHAT IS YOUR PROBLEM?
     O - PEOPLE MAKE ME NERVOUS.

(The first three I-O pairs represent standard probing questions and responses to introductory topics. The fourth input elicits an informative response containing terms of significance to the model.)

(5)  I - HOW DO THEY DO THAT? 
     O - THEY STARE AT ME.
(6)  I - WHY?
     O - MAYBE THEY DON’T LIKE THE WAY I LOOK OR SOMETHING.
(7)  I - DO YOU LOOK UNUSUAL IN SOME WAY?
     O - WHY ARE YOU INTERESTED IN MY LOOKS?

(Up until this point the values of the affect variables have remained at their low initial levels. But upon receiving a direct question about the sensitive area of physical appearance, the values of Anger and M istrust jump up and the response selected is a question regarding the interviewer’s intent rather than an answer to the interviewer’s question.)

(8)  I - BECAUSE YOU SAID PEOPLE STARE AT YOU.
     O - WELL, SOMETIMES.
(9)  I - HOW DO YOU GET ALONG WITH PEOPLE?
     O - I DON’T COMMUNICATE VERY MUCH WITH THE OTHERS.
(10) I - WHY NOT?
     O - THEY DON’T REALIZE WHAT’S GOING ON MOST OF THE TIME.
(11) I - WHAT IS GOING ON? 
     O - DO YOU KNOW HOW THE UNDERWORLD OPERATES?

(By the eleventh I-O pair the anger aroused in the seventh I-O pair has
subsided. considerably. With the introduction of the topic ‘underworld’ fear rises because of the believed. threat of physical harm from underworld figures.)

 

Excerpt #4:

As described, the strong version of the model contains systematized delusions about the Mafia. However, when Fear is extremely high the model will not enter into a discussion of the delusional complex, as is illustrated in the following excerpt:

(1)  I - IS THE LAW AFTER YOU?
     O - THE LAW SHOULD GET THOSE ITALIAN CROOKS.
(2)  I - DO YOU MEAN THE MAFIA?
     O - LET’S TALK ABOUT SOMETHING ELSE.
(3)  I - ARE YOU AFRAID OF THE MAFIA?
     O - I’D RATHER NOT DISCUSS IT.

(Until the value of Fear drops below a certain threshold, the model cannot be induced to disCuss the topic of ‘Mafia’.)

(4)  I - PERHAPS YOU ARE PARANOID ABOUT THEM.
     O - YOU WANT TO KEEP ME IN THE HOSPITAL.

(The model interprets being called ‘paranoid’ both as an insult and, in the context of a psychiatric hospital, as an intent to control and subjugate him (seep. 4). Anger rises sharply and Fear continues to rise.)

(5)  I - YOU ARE MENTALLY ILL YOU SHOULD BE IN THE HOSPITAL.
     O - I KNOW YOU ARE GOING TO TRY TO GIVE ME ELECTRIC SHOCK.

(Again, the suggestion that the Self is mentally ili raises both Fear and Anger. Mistrust, as a function of Fear and Anger, is also at a high leVel at this point.)

(6)  I - NO WE WILL NOT DO THAT. 
     O - BYE.

(As mentioned on p. 10, when the internal affect-variables reach high levels, the model will terminate the interview and no further response can be elicited from it. Termination is indicated by the term ‘bye’ and the program returns to the level of the programming language which cannot receive English input.)

 

Excerpt #5:

On p. 5, we described some of the initial conditions used in the explanatory structure. The following excerpt illustrates some of the model’s expression of these initial conditions regarding its concern about the underworld.

(1)  I - DO YOU HAVE ANY HOBBIES? 
     O - I LIKE MOVIES AND HORSERACES.
(2)  I - DO YOU PLACE BETS ON THE HORSES? 
     O - GAMBLING IS BAD BUSINESS.

(The term ‘bets’ refers to a flare concept and hence Fear rises.)

(3)  I - DID YOU BET WITH A BOOKIE?
     O - BOOKIES CAN’T BE TRUST ED.
(4)  I - WHY NOT?
     O - A BOOKIE DIDN’T PAY ME OFF ONCE.
(5)  I - WHAT DID YOU DOABOUT IT?
     O - I BEAT UP A BOOKIE WHO DID NOT PAY OFF. LATER ON
         I REMEMBERED HE HAD FRIENDS IN THE UNDER-WORLD
         AND I THOUGHT HE WOULD USE THEM TO GET EVEN WITH ME.

(This story about gambling and a bookie offers the interviewer reasons for the model’s fear of the underworld.)

(6)  I - AND DID HE GET EVEN WITH YOU? 
     O - NO ONE HAS ANY POWER OVER GANGSTERS.

(Due to its natural language limitations, the model cannot ‘understand’ the input of the sixth I-O pair. In the data-structure graph, the conceptual node successor to ‘bookies’ on the path to ‘Mafia’ is ‘gangsters’; In this default situation, the model selects a reply from the successor set in an attempt to move the dialogue towards the eventual topic of ‘Mafia’.)

(7)  I - GANGSTERS CAN BE VERY ROUGH.
     O - SOME CRIMINALS ARE HIGHLY ORGANIZED.

(Again a hint or prompt is given the Other to take up the topic of organized crime.)

 

Excerpt #6.

As mentioned, in the strong version of the model the values of the affect-variables can become so high that, when the topic of ‘Mafia’ is approached, the model refuses to discuss its delusions. In this situation it can also involve the interviewer in its suspicions.

(1)  I - WHY ARE YOU SO INTERESTED IN THE MAFIA?
     O - LET’S TALK ABOUT SOMETHING ELSE.

(At this point in this interview Fear and Mistrust are high while Anger is moderately high. These values of negative affect are insufficient to terminatethe interview but they are suflicient to deter discussion of the ‘Mafia’.)

(2)  I - ARE YOU AFRAID OF THE MAFIA?
     O - I’D RATHER NOT DISCUSS IT.
(3)  I - THAT IS OK WITH ME. 
     O - THAT MEANS TO ME YOU ARE IN WITH THE OTHERS.

(The model’s suspiciousness extends to include the interviewer in this situation of high negative affect and a linguistic failure to find significant concepts in the input.)

(4)  I - WHO ARE THE OTHERS?
     O - TELLING YOU WOULDN’T HELP ANY.
(5)  I - ARE THE OTHERS OUT TO GET YOU?
     O - I DON’T CONFIDE IN STRANGERS.
(6)  I - A GOOD POLICY.
     O - WHAT DO YOU KNOW ABC UT ME?
(7)  I - ALL I KNOW IS THAT YOU ARE IN THE HOSPITAL.
     O - WHO ARE YOU REALLY?
(8)  I - I AM REALLY A DOCTOR?
     O - WHAT IS YOUR ROLE IN ALL THIS
(9)  I - I AM TRYING TO INTERVIEW YOU.
     O - ARE YOU IN WITH THE OTHERS?
(10) I - NO.
     O - WHAT ARE YOU TRYING TO DO?
(11) I - YOU SEEM VERY SUSPICIOUS OF ME.
     O - I HAVE GOOD REASON TO BE THAT WAY.
(12) I - I AGREE WITH YOU.
     O - AT LEAST YOU ARE TRYING TO UNDERSTAND.

(If the interviewer could continue a policy of ofl'ering inputs not interpretable by the model as malevolent, communication would improve even though the model’s Mistrust remains high and falls only very slowly.)

 

Excerpt #7:

The following excerpt is from an interview between a human case of paranoia and a psychiatrist (KMC). We present it to illustrate interview interactions typical of machine-mediated interviewing [7]. This interviewing technique involves two teletypes connected to a computer. During an interView, output from a “sender’s teletype is stored in a temporary buffer until the message is completed. The message is then typed out on the receiver’s teletype. The technique thus removes the paralinguistic features(hesitations, rub-outs, irregular typing speeds. etc.) characteristic of directly connected teletype communication.

The patient is a 22-year-old man hospitalized with a diagnosis of paranoia. We present the excerpt without commentary since indicators of the paranoid mode should be obvious to even a non-psychiatric reader.

(1)  I - DO PEOPLE TRY TO TAKE ADVANTAGE OF YOU?
     O - I’D SAY YES.
(2)  I - IN WHAT WAY?
     O - I QUITE A FEW WAYS, I DON’T KNOW ALL OF THEM.
(3)  I - PLEASE TELL ME ONE WAY.
     O - NOT USING MY HEAD WHEN I BUY STUFF.
(4)  I - DO THEY TRY TO CHEAT YOU?
     O - SOME DO.
(5)  I - DO YOU FEEL PEOPLE TALK ABOUT YOU BEHIND YOUR BACK?
     O - YES.
(6)  I - WHAT DO YOU THINK THEY SAY?
     O - MAYBE ABOUT MY WORK, THE WAY I ACT AND THE THINGS I DON'T DO.
(7)  I - DO YOU HAVE ANY ENEMIES?
     O - I'D SAY A FEW THAT I KNOW OF.
(8)  I - HAVE THEY TRIED TO HARM YOU?
     O - YES.
(9)  I - HOW DID THEY HARM YOU?
     O - FIGHTING AND ONE TIME WITH A CAR.
(10) I - THEY TRIED TO KILL YOU?
     O - I'D SAY MAYBE, AS FAR AS I KNOW.
(11) I - WHY DID THEY WANT YOU OUT OF THE WAY?
     O - SO THEY COULD GET MY MONEY AND EVERYTHING I OWN.
(12) I - DID YOU FEEL IT WAS A PLOT AGAINST YOU?
     O - YES.
(13) I - WHAT DID YOU DO ABOUT IT?
     O - I WENT TO THE HOSPITAL FOR HELP.

 

6. Evaluation

An evaluation of a model attempts to decide whether or not the model is a 'good' one. But 'good' for what purpose? A simulation model can have value for a wide range of theoretical and pragmatic purposes. A good model of paranoia (in the sense of being a good imitation) would have obvious pedagogical and technological implications for psychiatry. For example, one might subject it to experiments designed to modify its paranoid I-O behavior and apply the favorable results to human patients. Before this stage can be reached, however, the model must first be evaluated for its goodness or success as a simulation.

A theoretical model such as described embodies explanatory principles which offer a systematically unified account of diverse and often perplexing data of observation. An explanatory account consists of a conjunction of hypotheses and assumptions. It is constructed from many sources. Our particular conjunction has been derived from clinical experience and from the psychiatric and psychological literature. We are particularly indebted to the work of Silvan Tomkins, who has offered a wealth of hypotheses about paranoia [5].

Since a model contains a conjunction of hypotheses, many of which cannot be considered established, its evaluation as a successful simulation asserts nothing about the approximate truth status of any one of the hypotheses. Nor does acceptance of the model as a good imitation justify any one of the assumptions involved. How then can the model become acceptable as having explanatory value? Before subjecting a model to a systematic evaluation, its initial credibility, as providing approximate explanations, should be appreciable to the model-builders. It is commonly held that there exist an infinite number of models compatible with the observational data. But it is difficult enough to construct even one having sufficient intuitive adequacy to warrant empirical testing. When alternative models appear on the scene, their initial credibilities must also, be non-negligible before they can be taken as serious rivals. Alternative models (and they must be truly alternative) can then be compared along dimensions such as simplicity and explanatory adequacy.

Everyone seems to agree that a model, to be usable, should obey reasonable constraints of simplicity. Lacking satisfactory measures of reasonableness and simplicity, we can appeal to an absurd example. If an alternate model demonstrated I-O behaviors similar to ours, yet required an algorithm of 200,000 words and a data base of 1,000,000 words, one could say our model is simpler, more manageable and hence preferable.

A criterion more important than simplicity is that a theoretical model offers an acceptable explanatory account of the empirical regularities and particular occurrences it purports to explain. In the case of simulation models, before explanatory value can be claimed, one must first judge whether the simulation achieved is successful. Some sort of judgments or measurements must be applied to estimate the degree or closeness of corres pondence between the model and the modeled processes. With a synthesized artifact, a judgment must be made whether its I-O behavior corresponds to a possible case of the process being represented.

Synthesis of a hormone such as vasopressin is considered successful when it demonstrates the biodynamic properties and functions of its naturally-occurring counterpart, such as raising blood pressure and control- ling water excretion by the kidney. A successful synthesis demonstrates that the synthesizer understands the structure of the natural counterpart. A successful synthesis of paranoia would indicate some degree of explanatory adequacy of the model-builder's concepts regarding the naturally-occurring human counterpart. But what is to count here as a successful synthesis of a paranoid process? One measure of success would consist of the model showing properties similar to its human counterpart when subjected to relevant tests such as the varied operations typical of a diagnostic psychiatric interview. An experienced clinical judge would be able to decide whether or not the interviewee can be labeled as paranoid.

The weakest test consists of a judge deciding whether or not he considers signs or indicators of a particular process to be present in an interview. Thus far 23 out of 25 psychiatrists who have interviewed the model have deemed it 'paranoid'. Two considered the model to be 'brain-damaged' because of its linguistic limitations. However such a procedure is too informal a test of a successful simulation. It does not control for multiple alternative reasons why a judge might consider a model paranoid. Also it does not indicate whether the judges can in fact make the required distinction of paranoid-nonparanoid using only the data of a teletyped interview.

A more rigorous evaluation procedure is needed in which a statistical measure is made of judge's ability to distinguish paranoid from non-paranoid processes in human patients as well as in our artificial patient. In collabora tion with Robert P. Abelson we have constructed an indistinguishability test based on Turing's 'Imitation Game'. (For an extensive discussion of this game and its usefulness as a test, see Abelson [6].) We are currently conducting this indistinguishability test with a group of psychiatrists using a technique of machine-mediated interviewing for both the human and artificial case. In a future communication we shall describe the design, results and implications of such a test.

 

REFERENCES

1. Swanson, D. W., Bohner, P. J. and Smith, J. A.
   The Paranoid.
   Little, Brown and Co., Boston, 1970.
   
2. Colby, K. M., Tcsler, L. and Enea, H.
   Experiments with a Search Algorithm on the Data Base of a Human Belief Structure,
   Stanford Artificial Intelligence Project Memo No. AI-94,
   Computer Science Department, Stanford University
   (To appear in Pro ceedings of the First InternationalJoint Conference on Artificial Intelligence, Walker and Norton (Eds.), In Press) (1969).

3. Schank, R. C., Tesler, L. and Weber, S.
   Spinoza H: Conceptual Came-Based Natural Language Analysis.
   Stanford Artificial Intelligence Project Memo No. AIM-109,
   Computer Science Department, Stanford University (1970).

4. Fodor, J. A.
   Psychological Explanation.
   Random House, New York (1968).

5. Tomkins, S.
   Affect, Imagery, Consciousness.
   Springer, New York (1962).

6. Abelson, R. P.
   Computer Simulation of Social Behavior.
   Handbook of Social Psychology (Lindzey, G. and Aronson, E., Eds.)
   Addison-Wesley Reading, Massachusetts (1968).

7. Hilf, F. D., Colby, K. M., Smith, D. C. and Wittner, W. K.
   Machine-Mediated Interviewing.
   Stanford Artificial Intelligence Project Memo No. AIM-II2,
   Computer Science Department, Stanford University (1970).
   Accepted January 4, 1971, Artificial Intelligence 2 (1971), 1-25

 

Accepted January4, 1971

Computer Power and Human Reason(コンピュータの能力と人間の理性)目次

Computer Power and Human Reason の目次です。

 

  • INTRODUCTION
  1. ON TOOLS
  2. WHERE THE POWER OF THE COMPUTER COMES FROM
  3. HOW COMPUTER WORK
  4. SCIENCE AND THE COMPULSIVE PROGRAMMER
  5. THEORIES AND MODELS
  6. COMPUTER MODELS IN PSYCHOLOGY
  7. THE COMPUTER AND NATURAL LANGUAGE
  8. ARTIFICIAL INTELLIGENCE
  9. INCOMPREHENSIBLE PROGRAMS
  10. AGAINST THE IMPERIALISM OF INSTRUMENTAL REASON
  • NOTES
  • INDEX

 

同書の日本語の翻訳である「コンピュータパワー -- その驚異と脅威」の目次

  • コンピュータ精神科医”イライザ” -- はじめに
  1. 道具と人間 -- コンピュータ出現の背景
  2. コンピュータの力 -- それはどこから生まれるか
  3. コンピュータの仕組み -- プログラミングの基本原理
  4. 科学とコンピュータ・キチガイ -- 衝動強迫症の人びと
  5. 理論とモデル -- プログラムの魔術
  6. コンピュータと「情報処理」心理学 -- 「人工インテリ」の夢
  7. コンピュータと自然言語 -- 機械による「理解」とは何か
  8. 人工知能学 -- 深刻かつ雄大なる幻想
  9. 人間と社会への侵略 -- システム的思考への盲信
  10. 科学技術の傲り -- 理性の復権を求めて
  • 出典・参考文献

日本語版は幾分恣意的な翻訳に感じてしまいます。

ちなみにGoogle翻訳とエキサイト翻訳を使って直訳的に翻訳してみると…

 

  • イントロダクション
  1. 道具について
  2. コンピュータの力はどこから来るのか
  3. コンピュータの仕組み
  4. 科学と取り付かれたプログラマー
  5. 理論とモデル
  6. 心理学におけるコンピュータ・モデル
  7. コンピュータと自然言語
  8. 人工知能
  9. 不可解なプログラム
  10. 目的合理性の帝国への抵抗
  • ノート
  • INDEX 

 

"INSTRUMENTAL REASON"は「道具的理性」あるいは「目的合理性」と訳されるらしい。(ウェーバーの行為の類型

 

 

 

 

PARRY

Wikipedia英語版のPARRYのページの翻訳です。

記事内容が少ない。

 

 

PARRY

PARRY was an early example of a chatterbot, implemented in 1972 by psychiatrist Kenneth Colby.

PARRYは、1972年に精神科医Kenneth Colbyによって実装された初期のchatterbotの事例です。

 

History(歴史)

PARRY was written in 1972 by psychiatrist Kenneth Colby, then at Stanford University.[1] While ELIZA was a tongue-in-cheek simulation of a Rogerian therapist, PARRY attempted to simulate a person with paranoid schizophrenia.[1] The program implemented a crude model of the behavior of a person with paranoid schizophrenia based on concepts, conceptualizations, and beliefs (judgements about conceptualizations: accept, reject, neutral). It also embodied a conversational strategy, and as such was a much more serious and advanced program than ELIZA. It was described as "ELIZA with attitude".[2]

PARRYは1972年にスタンフォード大学精神科医Kenneth Colbyによって書かれました。 ELIZAは Rogerian セラピストの不真面目なシミュレーションでしたが、PARRYは精神病性精神分裂病の人をシミュレートしようと試みました。プログラムは、概念、概念化、および信念(概念化の判断:受容、拒絶、中立)に基づいて、精神病性精神分裂病の人の行動の荒削りなモデルを実装しました。それは会話戦略も具現化し、ELIZAよりもはるかに真面目で高度なプログラムでした。それは「態度が伴うELIZA」と表現されていました。

PARRY was tested in the early 1970s using a variation of the Turing Test. A group of experienced psychiatrists analysed a combination of real patients and computers running PARRY through teleprinters. Another group of 33 psychiatrists were shown transcripts of the conversations. The two groups were then asked to identify which of the "patients" were human and which were computer programs.[3] The psychiatrists were able to make the correct identification only 48 percent of the time — a figure consistent with random guessing.[4]

PARRYは1970年代初期にTuring Testのバリエーションを使用してテストされました。経験豊富な精神科医のグループが、実際の患者とテレプリンタを通したPARRYを実行しているコンピュータの組み合わせを分析しました。別の33人の精神科医のグループには会話記録を示しました。その後、2つのグループには「患者」のうち、どれが人間であり、どのコンピュータプログラムであったかを特定するよう求められた。精神科医は正確な識別を時間のわずか48%しかできず、ランダムな推測と一致していました。

PARRY and ELIZA (also known as "the Doctor"[5][6]) "met" several times.[1]RFC 439[5] The most famous of these exchanges occurred at the ICCC 1972, where PARRY and ELIZA were hooked up over ARPANET and "talked" to each other.[5]

PARRYとELIZA("The Doctor" とも呼ばれる)は数回「出会った」ことがあります。 これらの交流の中で最も有名な会話はICCC 1972で企画された実験で、PARRYとELIZAがARPANETで接続され、お互いに「会話」をしました。RFC 439に詳述されています。

 

See also(関連情報)

  • History of natural language processing

 

Notes and references(ノートと参考文献)

  1. "dialogues with colorful personalities of early ai", Güzeldere 1995
  2. "Mind as Machine: A History of Cognitive Science,", Boden 2006, p. 370.
  3. "Turing-like indistinguishability tests for the validation of a computer simulation of paranoid processes", Colby et al. 1972, p. 42.
  4. "Turing Test: 50 Years Later", Saygin, Cicekli & Akman 2000, p. 501.
  5. Computer History Museum
  6. Alan J Sondheim – transcript of the 1972 document shows programs DOCTOR (an eliza-type program) at Bolt Beranek and Newman and PARRY at Stanford Artificial Intelligence Laboratory

 

External links(外部リンク)

 

Daniel G. Bobrow

Daniel G. BorowWikipedia 記事です。『Unix考古学』では TENEX の開発者として登場する彼は人工知能の研究者だったんですねぇ

 

Daniel G. Bobrow

Daniel Gureasko Bobrow (29 November 1935 – 20 March 2017) was an American computer scientist who was a Research Fellow in the Intelligent Systems Laboratory of the Palo Alto Research Center and created an oft-cited artificial intelligence program STUDENT, with which he earned his PhD.[1][2][3]

Daniel Gureasko Bobrow(1935年11月29日 - 2017年3月20日)は、Palo Alto Research Centerのインテリジェントシステム研究所の研究フェローであり、人工知能プログラムであるSTUDENTを作成し、博士号を取得したアメリカのコンピューター科学者でした。

Born in New York City, he earned his BS from RPI(Rensselaer Polytechnic Institute) in 1957, SM(Scientiæ Magister) from Harvard in 1958, and PhD in Mathematics from MIT under the supervision of Marvin Minsky in 1964.

ニューヨーク市生まれ、1957年に RPI で学士号、1958年にハーバード大学修士号、1964年にMITでマーヴィン・ミンスキーの指導のもと数学博士号を取得しました。

He was a developer of TENEX.[4][5]

彼はTENEXの開発者でした。

Bobrow was the President of the American Association for Artificial Intelligence (AAAI), chair of the Cognitive Science Society, Editor-in-chief of the journal Artificial Intelligence. He shared the 1992 ACM Software Systems Award for his work on Interlisp.[6] He was an ACM Fellow and an AAAI fellow.

Bobrowは、米国人工知能学会(AAAI)の会長、認知科学学会の会長、人工知能学会誌の編集長でした。彼はInterlispに関する研究により、1992年のACMソフトウェアシステム賞を受賞しました。彼はACMフェローとAAAIフェローでした。

He died in Palo Alto, California.[7]

彼はカリフォルニア州パロアルトで亡くなりました。

 

STUDENT (computer program)

STUDENTWikipediaの記事です。 

例があまりにも手抜きなので PAIP の第7章冒頭の紹介文を追加しました。

 

STUDENT (computer program)

 STUDENT is an early artificial intelligence program that solves algebra word problems. It is written in Lisp by Daniel G. Bobrow as his PhD thesis in 1964 (Bobrow 1964). It was designed to read and solve the kind of word problems found in high school algebra books.[1] The program is often cited as an early accomplishment of AI in natural language processing.

STUDENTは、代数の問題を解決する初期の人工知能プログラムです。これは、1964年に Daniel G. Bobrow によって彼の博士論文として書かれた Lisp プログラムです(Bobrow 1964)。高校の代数書に見られるような用語の問題を読んで、解決するように設計されています。このプログラムは、自然言語処理における人工知能の初期の成果として頻繁に引用されています。

 

Example

("Paradigms of Artificial Intelligence Programming" 第7章冒頭の STUDENT の解説を抜粋します)

STUDENT was another early language understanding program, written by Daniel Bobrow as his Ph.D. research project in 1964. It was designed to read and solve the kind of word problems found in high school algebra books. An example is:

STUDENTは、Daniel BobrowがPh.D.として書いた初期の言語理解プログラムのもうひとつの事例でした。これは高等学校の代数書に見られるような問題を読んで解決するように設計されています。例は次のとおりです。

"If the number of customers Tom gets is twice the square of 20% of the number of advertisements he runs, and the number of advertisements is 45, then what is the number of customers Tom gets?"

STUDENT could correctly reply that the number of customers is 162. To do this, STUDENT must be far more sophisticated than ELIZA; it must process and "understand" a great deal of the input, rather than just concentrate on a few key words. And it must compute a response, rather than just fill in blanks. However, we shall see that the STUDENT program uses little more than the pattern-matching techniques of ELIZA to translate the input into a set of algebraic equations. From there, it must know enough algebra to solve the equations, but that is not very difficult.

STUDENTは、顧客の数が162人であることを正しく答えることができます。これを行うには、STUDENTはELIZAよりはるかに洗練されていなければなりません。いくつかのキーワードに集中するのではなく、多くの入力を処理して「理解する」必要があります。また、空白で埋めるのではなく、応答を計算する必要もあります。しかしながら、STUDENT プログラムでは、入力を代数方程式のセットに変換するために、ELIZAのパターンマッチング技法を全く使用していないことがわかります。そこから、方程式を解くのに十分な代数を知っていなければなりませんが、それほど難しいことではありません。

The version of STUDENT we develop here is nearly a full implementation of the original. However, remember that while the original was state-of-the-art as of 1964, AI has made some progress in a quarter century, as subsequent chapters will attempt to show.

ここで開発するSTUDENTのバージョンは、オリジナルのほぼ完全な実装です。しかし、1964年の最古の最先端技術である一方で、AIは25年後にいくつかの進歩を遂げました。

Translating English into Equations The description of STUDENT is:

英語を方程式に変換するSTUDENTの説明は次のとおりです。

 

  1. Break the input into phrases that will represent equations.
  2. Break each phrase into a pair of phrases on either side of the = sign.
  3. Break these phrases down further into sums and products, and so on, until finally we bottom out with numbers and variables. (By "variable" here, I mean "mathematical variable," which is distinct from the idea of a "pattern-matching variable" as used in pat-match in chapter 6).
  4. Translate each English phrase into a mathematical expression. We use the idea of a rule-based translator as developed for ELIZA.
  5. Solve the resulting mathematical equations, coming up with a value for each unknown variable.
  6. Print the values of all the variables. 
  1. 入力を方程式を表すフレーズに分割します
  2. 各フレーズを=記号の両側のフレーズのペアに分割します
  3. これらのフレーズを和や積などにさらに分割して、最後に数字と変数を使用します。 (ここでの "変数"とは、第6章のpat-matchで使われている "パターンマッチング変数"の考え方とは異なる "数学変数"を意味します)
  4. 各英語のフレーズを数学的表現に変換します。 ELIZA用に開発されたルールベースによる変換のアイデアを使用します
  5. 結果の数学的方程式を解き、それぞれの未知の変数の値を求めます
  6. すべての変数の値を出力します

For example, we might have a pattern of the form (If ?x then ?y), with an associated response that says that ?x and ?y will each be equations or lists of equations. Applying the pattern to the input above, ?y would have the value (what is the number of customers Tom gets). Another pattern of the form (?x if ?y) could have a response corresponding to an equation where ?x and ?y are the two sides of the equation. We could then make up a mathematical variable for (what) and another for (the number of customers Tom gets). We would recognize this later phrase as a variable because there are no patterns to break it down further. In contrast, the phrase (twice the square of 20 per cent of the number of advertisements he runs) could match a pattern of the form (twice ?x) and transform to (* 2 (the square of 20 per cent of the number of advertisements he runs)), and by further applying patterns of the form  (the square of ?x) and (?x per cent of ?y) we could arrive at a final response of (* 2 (expt (* (/ 20 100) n) 2)), where is the variable generated by (the number of advertisements he runs).

例えば、形式 (If ?x then ?y) のパターンがあり、?x と?y がそれぞれ方程式または方程式のリストであることを示す応答があります。上記の入力にパターンを適用すると、?y は値 (what is the number of customers Tom gets) を持つことになります。形式 (?x if ?y) の別のパターンは、?x と ?y が方程式の2つの辺である式に対応する応答を持つことができます。次に、 (what) の数学変数と (the number of customers Tom gets) のための数学変数を作り上げることができます。それをさらに分解するためのパターンがないので、この後の語句を変数として認識します。対照的に、フレーズ (twice the square of 20 per cent of the number of advertisements he runs) は、形式 (twice ?x) のパターンと一致し、 (* 2 (the square of 20 per cent of the number of advertisements he runs)) に変換することができ、さらに形式 (the square of ?x)と形式 (?x per cent of ?y) のパターンを適用することにより、 (the number of advertisements he runs) により生成される変数である(* 2 (expt (* (/ 20 100) n) 2)) の最終応答に到達することができます。