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HYPO is a computer program that models reasoning with cases and hypotheticals in the legal domain. It is the first of its kind and the most sophisticated of the case-based legal reasoners, which was designed by Ashley for his Ph.D dissertation in 1987 at the University of Massachusetts Amherst under the supervision of Rissland. HYPO’s design represents a hybrid generalization/comparative evaluation method appropriate for a domain with a weak analytical theory and applies to tasks that rarely involve just one right answer.〔Ashley, K.D., Reasoning with cases and hypotheticals in HYPO, (1991), International Journal Man-Machine St. 34(6), pp. 753-796〕 The domain covers US trade secret law, and is substantially a "common law" domain. Since the Anglo-American law is operated under the doctrine of precedent, the definitive way of interpreting problems is of necessity and case-based.〔Rissland, E.L. and Skalak, D.B., Case-Based Reasoning in a Rule-Governed Domain, (1989) In Proceedings of the Fifth IEEE Conference on Artificial Intelligence Applications 1989, Institute of Electrical and Electronic Engineers Inc.〕 Thus, HYPO did not involve the analysis of a Statute, as required by the PROLOG program. Rissland and Ashley (1987) envisioned HYPO as employing the key tasks performed by lawyers when analyzing case law for precedence to generate arguments for the prosecution or the defence.〔Delgado P. Survey of Case-Based Reasoning as Applied to the Legal Domain〕 HYPO was a successful example of a general category of legal expert systems (LESs), it applied artificial intelligence (A.I.) techniques to the domain of legal reasoning in patent law, implementing a case-based reasoning (CBR) system, in contradiction to rule based systems like MYCIN, or mixed-paradigm systems integrating CBR with rule-based or model-based reasoning like IKBALS II. A legal case-based reasoning essentially reasons from previously tried cases, comparing the contextual information in the current input case with that of cases previously tried and entered into the system.〔Vossos, G., Zeleznikow, J., Dillon, T., Vossos, V., An example of Integrating Legal Case Based Reasoning with Object-Oriented Rule-Based Systems: IKBALS II , (1991) In Proceedings of the Third International Conference on Artificial Intelligence and Law, 31-41, Oxford, England〕 As noted by Ashley and Rissland (1988) CBR is used to "... capture expertise in domains where rules are ill-defined, incomplete or inconsistent".〔Kolodner, J.L., An Introduction to Case-Based Reasoning, (1992), Artificial Intelligence Review 6, pp.3-34. O’ Leary, D.E. Verification and Validation of Case-Based Systems, (1993), Expert Systems with Applications 6, pp.57-66〕 The HYPO project set out to model the creation of hypotheticals in law, where no case matches well enough. HYPO uses hypotheticals for a variety of tasks necessary for good interpretation: "to redefine old situations in terms of new dimensions, to create new standard cases when an appropriate one doesn’t exist, to explore and test the limits of a concept, to refocus a case by excluding some issues and to organize or cluster cases".〔Ashley, K.D. and Rissland E.L., A case-based approach to modeling legal expertise, (1988), IEEE Expert 3, pp. 70-77.〕 Hypotheticals can include facts that support two conflicting lines of reasoning. So, it makes and responds to arguments from competing viewpoints about who should win the dispute. HYPO use heuristics such as making a case weaker or stronger, making a case extreme, enabling a "near-miss", disabling a "near-hit" to generate hypotheticals in the context of an argument by using the dimensions mechanism.〔Rissland, E.L. and Ashley, K.D., A case-based system for trade secrets law, (1987) In Proceedings 1987 ACM International Conference on Artificial Intelligence and Law〕 Dimensions have a range of values, along which the supportive strength that may shift from one side to the other.〔Zeng, Y., Wang, R. , Zeleznikow, J., Kemp, E., A Knowledge Representation model for the intelligent retrieval of legal cases, (2007), International Journal of Law and Information Technology 15(3), pp. 299-319〕 What differentiated this expert system from others was its facility not only to return a primary to best-case response but to return near-best-fit responses as well. ==Components== Legal knowledge in HYPO is contained in: the case-knowledge-base (CKB) and the library of dimensions. The CKB contains HYPO’s base of known cases that are highly structured objects and sub-objects both real and hypothetical in the area of trade secret law. Each case is represented as a hierarchical set of frames whose slots are important facets of the case (e.g. Plaintiff, defendant, secret knowledge, employer/employee data).〔Ashley’s HYPO system used a database of thirty cases in the area indexed by thirteen dimensions. A key mechanism in HYPO is a dimension i.e. a mechanism to allow retrieval from the CKB, in order to represent legal cases. Ashley’s dimensions are composed of (i) prerequisites, which are a set of factual predicates that must be satisfied for the dimension to apply (ii) focal slots, which accommodate one or two of the dimension’s prerequisites designated as being indicative of the case’s strength along that dimension and (iii) range information, which tells how a change in focal slot value effects the strength of a party’s case along a given dimension.〔 Dimensions focus attention on important aspects of cases. In HYPO’s domain of misappropriation of trade secrets the dimension called “secrets voluntary disclosed” captures the idea that the more disclosures the plaintiff has made of his/her putative secret, the less convincing is his/her argument that the defendant is responsible for letting the secret.〔Rissland, E.L., A.I. and Similarity, (2006), IEEE Intelligent Systems, 21(3), pp. 39-49〕 HYPO, like any other CBR system has also the following components: * Similarity/relevancy metrics: that is, standards by which to evaluate the closeness of cases, judge their relevancy to the instant case, and select “most on point” cases. * Half-Order Theory of the Application Domain: that is, hierarchies and taxonomies of knowledge, especially regarding the application domain. * Precedent-based argumentation abilities: that is, capabilities to generate and evaluate precedent-based arguments. * Knowledge to generate hypotheticals: that is, the ability to generate hypothetical cases to deal with various circumstances, like testing the validity of an interpretation or argument by providing ''gedanken'' experiments such as test cases or to fill in a weak CKB.〔 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「HYPO CBR」の詳細全文を読む スポンサード リンク
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