Controlling Inadvertent Ambiguity in the Logical Structure of Legal Drafting by means of the Prescribed Definitions of the A-Hohfeld Structural Language

Two principal sources of imprecision in legal drafting (vagueness and ambiguity) are identified and illustrated. Virtually all of the ambiguity imprecision encountered in legal discourse is ambiguity in the language used to express logical structure, and virtually all of· the imprecision resulting is inadvertent. On the other hand, the imprecision encountered in legal writing that results from vagueness is frequently, if not most often, included there deliberately; the drafter has considered it and decided that the vague language· best accomplishes the purpose at hand. This paper focuses on the use of some defined terminology for minimizing inadvertent ambiguity in the logical structure of legal discourse, where desired by the drafter. The current set of signaled structural definitions that are included in the A-Hohfeld language are first set forth and their use is illustrated in an extensive example from the treaty establishing the European Economic Community. The use of definitions· in legal writing is widespread, but addressed almost exclusively to controlling the vagueness of substantive legal terms; they are seldom used for structural purposes. Furthermore, their use in American legislative drafting is unsignaled. Here, attention is devoted to the relatively-neglected domain in legal discourse of imprecisely expressed logical structure, and the remedy offered, where desired by the drafter, is a set of signaled structural definitions for use in controlling such imprecision

One Use of Computerized Instructional Gaming in Legal Education: To Better Understand the Rich Logical Structure of Legal Rules and Improve Legal Writing

This article describes an innovation in legal education and speculates about its importance and effectiveness as an educational tool. The speculations about its potential use, however, are ones that each legal educator will be able to test individually to determine the effectiveness of this use of microcomputers to improve legal education. The computer software that permits the innovation to be used will be available to interested persons by the time that this article is published

A-Hohfeld: A Language for Robust Structural Representation of Knowledge in the Legal Domain to Build Interpretation-Assistance Expert Systems

The A-Hohfeld language is presented as a set of definitions; it can be used to precisely express legal norms. The usefulness of the AHohfeld language is illustrated in articulating 2560 alternative structural interpretations of the four-sentence 1982 Library Regulations of Imperial College and constructing an interpretation-assistance legal expert system for these regulations by means of the general-purpose Interpretation-Assistance legal expert system builder called MINT. The logical basis for A-Hohfeld is included as an appendix

One Use of Computerized Instructional Gaming in Legal Education: To Better Understand the Rich Logical Structure of Legal Rules and Improve Legal Writing

This article describes an innovation in legal education and speculates about its importance and effectiveness as an educational tool. The speculations about its potential use, however, are ones that each legal educator will be able to test individually to determine the effectiveness of this use of microcomputers to improve legal education. The computer software that permits the innovation to be used will be available to interested persons by the time that this article is published

The Legal Argument Game of Legal Relations

The Language of LEGAL RELATIONS (LLR) is a representation language for expressing rules and arguments in the legal domain. The fundamental legal conceptions of Wesley N. Hohfeld, one of the foremost legal philosophers of the 20th Century, provide the first giant step in the development of the LEGAL RELATIONS Logic (LRL) that underlies LLR. LRL is an extension, enrichment, and formalization of the eight fundamental legal conceptions that Hohfeld viewed as the lowest common denominators of legal discourse that could be used to describe every possible legal state of affairs and every change in such states.This robustness is actually achieved by LRL, along with its capacity to represent every possible legal rule as well as every possible legal argument

The Legal Argument Game of Legal Relations

The Language of LEGAL RELATIONS (LLR) is a representation language for expressing rules and arguments in the legal domain. The fundamental legal conceptions of Wesley N. Hohfeld, one of the foremost legal philosophers of the 20th Century, provide the first giant step in the development of the LEGAL RELATIONS Logic (LRL) that underlies LLR. LRL is an extension, enrichment, and formalization of the eight fundamental legal conceptions that Hohfeld viewed as the lowest common denominators of legal discourse that could be used to describe every possible legal state of affairs and every change in such states.This robustness is actually achieved by LRL, along with its capacity to represent every possible legal rule as well as every possible legal argument

Exploring Computer Aided Generation of Questions for Normalizing Legal Rules

The process of normalizing a legal rule requires a drafter to indicate where the intent is to be precise and where it is to be imprecise in expressing both the between-sentence and within-sentence logical structure of that rule. Three different versions of a legal rule are constructed in the process of normalizing it: (1) the logical structure of the present version, (2) the detailed marker version, and (3) the logical structure of the normalized version. In order to construct the third version the analyst must formulate and answer specific questions about the terms that are used to express the logical structure of the first version that relates the constituent sentences marked in the second version. Questions about the two types of logical structure may be of two different kinds: (1) direct questions about the interpretation of terms that express each type of structure, and (2) indirect questions by means of hypothetical situations that indicate how the terms that express structure are intended to be interpreted. Direct questions are generated from natural language terms that are used to express structure by a series of transformations that use progressively more detailed defined structural terms and that culminate in structure that is expressed entirely in the defined structural terms of the basic normalized form. Arrow diagrams accompany these direct questions to help teach normalization to those unfamiliar with it. Examples of such direct questions, as well as examples of indirect ones, are provided with respect to normalization of section 2-207 of the Uniform Commercial Code. Indirect questions are generated about hypothetical situations that involve various appropriate combinations of conditions expressed in the rule that lead to the various mentioned results. This kind of question may be easier for an expert to respond to and thus be a better vehicle for eliciting the expertise of such a person. It is possible that some computer assistance can be provided in generating direct questions, but less likely for indirect questions. Furthermore the number of indirect questions generated my be unmanageably large and require too much human assistance to be practical. In this chapter the feasibility of such computer-aided question generation will be explored to determine to what extent it can facilitate the normalizing of legal rules

Exploring Computer Aided Generation of Questions for Normalizing Legal Rules

The process of normalizing a legal rule requires a drafter to indicate where the intent is to be precise and where it is to be imprecise in expressing both the between-sentence and within-sentence logical structure of that rule. Three different versions of a legal rule are constructed in the process of normalizing it: (1) the logical structure of the present version, (2) the detailed marker version, and (3) the logical structure of the normalized version. In order to construct the third version the analyst must formulate and answer specific questions about the terms that are used to express the logical structure of the first version that relates the constituent sentences marked in the second version. Questions about the two types of logical structure may be of two different kinds: (1) direct questions about the interpretation of terms that express each type of structure, and (2) indirect questions by means of hypothetical situations that indicate how the terms that express structure are intended to be interpreted. Direct questions are generated from natural language terms that are used to express structure by a series of transformations that use progressively more detailed defined structural terms and that culminate in structure that is expressed entirely in the defined structural terms of the basic normalized form. Arrow diagrams accompany these direct questions to help teach normalization to those unfamiliar with it. Examples of such direct questions, as well as examples of indirect ones, are provided with respect to normalization of section 2-207 of the Uniform Commercial Code. Indirect questions are generated about hypothetical situations that involve various appropriate combinations of conditions expressed in the rule that lead to the various mentioned results. This kind of question may be easier for an expert to respond to and thus be a better vehicle for eliciting the expertise of such a person. It is possible that some computer assistance can be provided in generating direct questions, but less likely for indirect questions. Furthermore the number of indirect questions generated my be unmanageably large and require too much human assistance to be practical. In this chapter the feasibility of such computer-aided question generation will be explored to determine to what extent it can facilitate the normalizing of legal rules

Computer-Aided Normalizing and Unpacking: Some Interesting Machine-Processable Transformations of Legal Rules

One way of dealing with an important aspect of the natural language barrier that researchers m artificial intelligence have been wrestling with for more than two decades is to normalize the expression of the logical structure of legal rules. The computer program, NORMALIZER, will enable a legal analyst to automatically generate Normalized Versions of legal rules and Outlines of them from Parenthesized Logical Expressions of their structure and Marked Versions of the Original Text of the rules. In brief: Parenthesized Logical Expression & Marked Version = = \u3e Outline & Normalized Version. The Parenthesized Logical Expression of a normalized rule is a statement that expresses the logical structure of the rule in brief notation. The Marked Version of the Original Text of a rule divides that text into constituent sentences and associates a short name with each of them. The short names of the sentences in the Marked Version are used in the Parenthesized Logical Expression to represent those sentences. In the Parenthesized Logical Expression, the logical structure of the Normalized rule is presented in a single dimension -- horizontally. In the Outline of the Normalized rule, the logical structure is presented in two dimensions -- both horizontally and vertically. In the Outline, short names are used to represent the constituent sentences, but in the Normalized Version the short names are replaced by the sentences themselves. In the Normalized Version, the logical structure of the rule is presented in two dimensions -horizontally and vertically by means of defined (and signalled) structural terminology. Unpacking the logical structure of a Normalized rule into progressively more basic structural terms is done automatically by part of NORMALIZER. A completely unpacked rule (an elementary normalized one) will be expressed in terms of three of the four basic structural terms (AND OR NOT and IF· THEN) and will be in the form of a conjunction of elementary norms. Although some drafters may prefer to use advanced Normalized Versions, probably the most frequently used ones will be clear Normalized Versions and basic Normalized Versions. In using NORMALIZER a legal analyst must first specify the Parenthesized Logical Expression and Marked Version of the legal rule being normalized, and then NORMALIZER can be used to generate the Outline and Normalized Version of the rule. Thus, the interpretation of the Original Text is a result of the expertise of the human analyst, while the formatting of the expression of that interpretation is done automatically by the program. The program can also automatically generate equivalent Normalized Versions that are expressed in logically more basic form (and also the reverse)

Automatic Generation of a Legal Expert System

The use of the AUTOPROLOG system to generate automatically a legal expert system is described in this chapter. The interpretation of a statutory or other legal rule by one expert (or by the consensus of a group of experts) expressed in a normalized form is the only input needed by the AUTOPROLOG system (which includes Turbo Prolog, the AUTOPRO program, and some data files) to produce automatically a computer program that is an expert system for that legal rule. The process for producing a legal expert system for Section 213.1 of the Modal Penal Code, which deals with rape and related offenses, by using the AUTOPROLOG system is described and the resulting legal expert system is illustrated