User:Christinamichael86

Source: Wikipedia, the free encyclopedia.

HYPO is a

prosecution or the defence.[3]

HYPO was a successful example of a general category of legal

tried cases, comparing the contextual information in the current input case with that of cases previously tried and entered into the system[4] . As noted by Ashley and Rissland (1988) CBR is used to “… capture expertise in domains where rules are ill-defined, incomplete or inconsistent.”[5]

The HYPO project set out to model the creation of hypotheticals in

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[7] . Dimensions have a range of values, along which the supportive strength that may shift from one side to the other [8]. 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

secret knowledge, employer/employee data)[9]. 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[10]. 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[11] .


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 [12]
.


Functionality

HYPO’s method of creating an argument and justifying a solution or position has several steps. HYPO begins its processing with the current fact situation (“cfs”) which is direct input by the user into HYPO’s representation framework. Once the user inputs the case, HYPO begins its legal analysis. The cfc is analyzed for relevant factors. Based on these factors HYPO selects the relevant cases and produces a case-analysis-record that records which dimensions apply to the cfc and which nearly apply (i.e. are “near misses”). The combined list of applicable and

heuristic search of the space of all possible cases. Lastly, the Explanation module expands upon the argument skeleton and provides explanation and justification for the different lines of analysis and cases found by HYPO[15]
.


An intelligent legal tutoring system

Legal expert systems are specifically designed to teach an area of law and are useful for

research project to device and test an intelligent, case-based tutorial program for teaching law students how to argue with cases implementing the HYPO program[18]
. Within the tutor system, Ashley and Aleven (1991) proposed to
on-line cases and instructions to make, or respond to, a legal argument about the problem. The student/user will have a set of tools to analyze the problem and fashion an answer comparing it to other cases. Instead of simply generating precedent cases, the system actually functions in such a way as to interpret student responses, comparing them against a list of possibilities and responding to student entries, for example, by citing counterexamples, and providing feedback on a student's problem solving activities with explanations of correctness or giving further hints as to what may be wrong with evaluating a student’s ability to perform legal reasoning and argumentation, examples and follow-up assignments by employing HYPO’s model of case-based structure[20]
.


HYPO’s progeny

The quality of HYPO’s results speak for themselves, in that a number of sequent legal reasoning systems are either directly based upon HYPO’s mechanisms as in the case of Kowalski (1991)[21] , TAX-HYPO, precedent case-based system operating in the statutory domain of tax law (Rissland and Skalak 1989), CABARET, a mixed-paradigm cases and rule system for the income tax law domain, (Skalak and Rissland 1992) , CATO, IBP, developed for argumentation to make predictions based on argumentation concepts (Brüninghaus and Ashley 2003), or their creators at least pay homage to HYPO in their discussions (Henderson and Bench-Capon 2001[22] ).

References

  1. ^ Ashley, K.D., Reasoning with cases and hypotheticals in HYPO, (1991), International Journal Man-Machine St. 34(6), pp. 753-796
  2. ^ 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.
  3. ^ Delgado P. Survey of Case-Based Reasoning as Applied to the Legal Domain
  4. ^ 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
  5. ^ 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
  6. ^ Ashley, K.D. and Rissland E.L., A case-based approach to modeling legal expertise, (1988), IEEE Expert 3, pp. 70-77.
  7. ^ 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
  8. ^ 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
  9. ^ Ibid. n. 7 p. 62
  10. ^ Ibid n.4 pp. 34-35
  11. ^ Rissland, E.L., A.I. and Similarity, (2006), IEEE Intelligent Systems, 21(3), pp. 39-49
  12. ^ Ibid n.2 pp. 49-50
  13. ^ Ibid n. 7 p. 62
  14. ^ Popple, J., A Pragmatic Legal Expert System, Dartmouth Publishing Company Ltd, Aldershod, England (1996), pp.42-43
  15. ^ Ibid n.7 p.62
  16. ^ Zeleznikow, J. and Hunter, D., Rationales for the Continued Development of Legal Expert Systems, (1992), 3, J.L. & Inf. Sci. 94
  17. ^ Ibid n. 10 pp. 40-41
  18. ^ Ashley, K.D. and Aleven, V., Toward an Intelligent Tutoring System for Teaching Law Students to argue with cases, (1991) In Proceedings of the Third International Conference on Artificial Intelligence and Law, 42-52, Oxford, England
  19. ^ Ibid n. 3
  20. ^ Ashley, K.D., Case-Based Reasoning and its Implications for Legal Expert Systems, (1992), Artificial Intelligence and Law 1, pp. 113-208
  21. ^ Kowalski, A., Case-based reasoning and the deep structure approach to knowledge representation, (1991) Proceedings of the 3rd international conference on Artificial intelligence and law, 21-30
  22. ^ Henderson, J. & Bench-Capon, T, Dynamic arguments in a case law domain, (2001) Proceedings of the 8th international conference on Artificial intelligence and law, 60-69.


See also:

Computational model

Hypothesis

Hypothetical syllogism

Janet L. Kolodner

Knowledge-based systems

Rule-based system

Legal information retrieval

Logical reasoning

Persuasive precedent

Problem solving

Shyster (expert system)

Socratic method


External links

Aleven, V., [http://www.sciencedirect.com/science/article/pii/S000437020300105X ], (2003) Artificial Intelligence 50, 183-237

Report Abel Hinkf6230 Cbr, [1],Hypo Km Health Informatics Report

Ashley, K.D., [2], Modeling Legal Argument: Reasoning with cases and hypothetical, MIT Press, Cambridge, 1987. Based on Ashley’s Phd Dissertation COINS Technical Report No. 88-01



For further reading

Case-based reasoning: A review

Edelson, D.C., Learning from cases and questions: The Socratic case-based teaching architecture, (1996), J. Learning Science 5(4), 357-410

Gray, P.N., Artificial Legal Intelligence, Dartmouth Publishing Company Ltd, Aldershod, England 1998

Rissland and Ashley, , “A note on Dimensions and Factors”, (2002), Artificial Intelligence and Law 10, 65-77

Rissland and Skalak, CABARET: Rule Interpretation in a hybrid architecture], (1991), Intern. J. Man-Machine Stud. 34(6), 839-887

Rissland, E.L. and Skalak, D.B., Combining Case-Based and Rule-Based Reasoning: A Heuristic approach] (1989) In Proceedings IJCAI-89 Detroit: International Joint Conference on Artificial Intelligence

Popple, J. 1993. SHYSTER: A Pragmatic Legal Expert System. Ph.D. Dissertation ,Australian National University, Canberra, Australia

Smith, J.C., Gelbart, D. and Graham, D., Building Expert System in Case-Based Law, (1992). Expert Systems with Applications 4, 335-342

Susskind, R.E., Expert Systems in Law: a Jurisprudential Inquiry, (OUP, Oxford,1987)

Retrieved from "https://en.wikipedia.org/w/index.php?title=User:Christinamichael86&oldid=1169951512"