HYPO CBR

Last updated
HYPO
Original author(s)
Initial release1987;37 years ago (1987)
Written in Prolog
Available inEnglish
Type Legal expert system

HYPO is a computer program, an expert system, 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 Kevin Ashley for his Ph.D dissertation in 1987 at the University of Massachusetts Amherst under the supervision of Edwina 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. [1] The domain covers US trade secret law, and is substantially a common law domain. Since Anglo-American common law operates under the doctrine of precedent, the definitive way of interpreting problems is of necessity and case-based. [2] 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. [3]

Contents

HYPO was a successful example of a general category of legal expert systems (LESs), it applies artificial intelligence (A.I.) techniques to the domain of legal reasoning in patent law, implementing a case-based reasoning (CBR) system, in contrast 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 prior 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 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". [6] 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. [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 also.

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). [7] : 62 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. [4] : 34–35  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. [9]

HYPO, like any other CBR system has also the following components:

Functions

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 near miss dimensions is called the D-list. At this point the fact gathered module may request additional information from the user in order to draw a legal conclusion. Once all the facts are in the case-positioner module it uses the case-analysis record to create the claim lattice. This is a technique that organizes the relevant retrieved cases from the point of view of the cfc and makes it easy for HYPO to ascertain the most-on point cases (mopc) and to least on-point-cases. [7] : 62  HYPO's arguments are 3ply, leading to the construction of the skeleton of an argument: it makes a point for one side, drawing the analogy between the problem and the precedent, responds with an argument for the opponent side, endeavoring to differentiate the cited case and citing other cases as counterarguments. Then it makes a final rebuttal, attempting to differentiate the counterarguments. [10] The claim lattice also enables the HYPO-generator module to produce legally hypotheticals. With its use of dimension-based heuristics, the HYPO-generator does a 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. [7] : 62 

Legal expert systems are specifically designed to teach an area of law and are useful for pedagogical purposes. Ashley's work was mainly concerned to build tools to help students understand legal reasoning. [11] Explanation and argument are the bases of the case method used in many professional schools in the U.S., first introduced by the Dean of the Harvard Law School, Christopher Columbus Langdell in 1870. The case method focuses on close readings of cases and principles; it involves students in pointed Socratic dialogue and makes strong use of hypotheticals (hypos). [4] : 40–41  Thus, CATO (Aleven 1997) [12] was a 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. [13] Within the tutor system, Ashley and Aleven (1991) [14] proposed to leverage an understanding of legal reasoning against the standard case-based tutoring methodology. What makes this tutoring system stand out is the additional levels of abstraction involved in its results. [3] The system presents exercises, including the facts of a problem and a set of 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 works 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 argument, examples and follow-up assignments by employing HYPO's model of case-based structure. [15]

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), [16] 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 [17] ).

See also

Related Research Articles

<span class="mw-page-title-main">Douglas Lenat</span> Computer scientist and AI pioneer

Douglas Bruce Lenat was an American computer scientist and researcher in artificial intelligence who was the founder and CEO of Cycorp, Inc. in Austin, Texas.

<span class="mw-page-title-main">Case-based reasoning</span> Process of solving new problems based on the solutions of similar past problems

In artificial intelligence and philosophy, case-based reasoning (CBR), broadly construed, is the process of solving new problems based on the solutions of similar past problems.

<span class="mw-page-title-main">Symbolic artificial intelligence</span> Methods in artificial intelligence research

In artificial intelligence, symbolic artificial intelligence is the term for the collection of all methods in artificial intelligence research that are based on high-level symbolic (human-readable) representations of problems, logic and search. Symbolic AI used tools such as logic programming, production rules, semantic nets and frames, and it developed applications such as knowledge-based systems, symbolic mathematics, automated theorem provers, ontologies, the semantic web, and automated planning and scheduling systems. The Symbolic AI paradigm led to seminal ideas in search, symbolic programming languages, agents, multi-agent systems, the semantic web, and the strengths and limitations of formal knowledge and reasoning systems.

Roger Carl Schank was an American artificial intelligence theorist, cognitive psychologist, learning scientist, educational reformer, and entrepreneur. Beginning in the late 1960s, he pioneered conceptual dependency theory and case-based reasoning, both of which challenged cognitivist views of memory and reasoning. He began his career teaching at Yale University and Stanford University. In 1989, Schank was granted $30 million in a ten-year commitment to his research and development by Andersen Consulting, through which he founded the Institute for the Learning Sciences (ILS) at Northwestern University in Chicago.

Legal informatics is an area within information science.

In philosophy of logic, defeasible reasoning is a kind of provisional reasoning that is rationally compelling, though not deductively valid. It usually occurs when a rule is given, but there may be specific exceptions to the rule, or subclasses that are subject to a different rule. Defeasibility is found in literatures that are concerned with argument and the process of argument, or heuristic reasoning.

Norms can be considered from different perspectives in artificial intelligence to create computers and computer software that are capable of intelligent behaviour.

Split Up is an intelligent decision support system, which makes predictions about the distribution of marital property following divorce in Australia. It is designed to assist judges, registrars of the Family Court of Australia, mediators and lawyers. Split Up operates as a hybrid system, combining rule – based reasoning with neural network theory. Rule based reasoning operates within strict parameters, in the form:

Legal information retrieval is the science of information retrieval applied to legal text, including legislation, case law, and scholarly works. Accurate legal information retrieval is important to provide access to the law to laymen and legal professionals. Its importance has increased because of the vast and quickly increasing amount of legal documents available through electronic means. Legal information retrieval is a part of the growing field of legal informatics.

SHYSTER is a legal expert system developed at the Australian National University in Canberra in 1993. It was written as the doctoral dissertation of James Popple under the supervision of Robin Stanton, Roger Clarke, Peter Drahos, and Malcolm Newey. A full technical report of the expert system, and a book further detailing its development and testing have also been published.

Dr. Robert L. Simpson Jr. was a computer scientist whose primary research interest was applied artificial intelligence. He served as Chief Scientist at Applied Systems Intelligence, Inc. (ASI) working with Dr. Norman D. Geddes, CEO. Dr. Simpson was responsible for the creation of the ASI core technology PreAct. ASI has since changed its name to Veloxiti Inc.

In information technology a reasoning system is a software system that generates conclusions from available knowledge using logical techniques such as deduction and induction. Reasoning systems play an important role in the implementation of artificial intelligence and knowledge-based systems.

Trevor Bench-Capon was a British computer scientist and an Honorary Visiting Professor of computer science at the University of Liverpool, where he taught from 1987 until his retirement in 2012. He is the author of work on computer science and ontology and is one of the editors in chief of the Artificial Intelligence and Law Journal.

A legal expert system is a domain-specific expert system that uses artificial intelligence to emulate the decision-making abilities of a human expert in the field of law. Legal expert systems employ a rule base or knowledge base and an inference engine to accumulate, reference and produce expert knowledge on specific subjects within the legal domain.

Vincent Aleven is a professor of human-computer interaction and director of the undergraduate program at Carnegie Mellon University's Human–Computer Interaction Institute.

Computational Law is the branch of legal informatics concerned with the automation of legal reasoning. What distinguishes Computational Law systems from other instances of legal technology is their autonomy, i.e. the ability to answer legal questions without additional input from human legal experts.

Argument technology is a sub-field of collective intelligence and artificial intelligence that focuses on applying computational techniques to the creation, identification, analysis, navigation, evaluation and visualisation of arguments and debates.

<span class="mw-page-title-main">Bruce M. McLaren</span> American researcher, academic and author (born 1959)

Bruce Martin McLaren is an American researcher, scientist and author. He is a Full Professor at Carnegie Mellon University in the Human-Computer Interaction Institute and a former President of the International Artificial Intelligence in Education Society (2017-2019).

Katie Marie Atkinson is a professor of computer science and the Dean of the School of Electrical Engineering, Electronics and Computer Science at the University of Liverpool. She works on researching and building artificial intelligence tools to help judges and lawyers. Atkinson previously served as the President of the International Association for AI and Law.

Edwina Luane Rissland is a retired American mathematician and computer scientist. Initially focusing on knowledge representation and the philosophy of mathematics, her later research in artificial intelligence included work on case-based reasoning and the applications of AI in legal work. She is a professor emerita in the Manning College of Information & Computer Sciences at the University of Massachusetts Amherst. In her retirement she has worked as a photographer and art curator.

References

  1. ↑ Ashley, K.D., Reasoning with cases and hypotheticals in HYPO, (1991), International Journal Man-Machine St. 34(6), pp. 753-796
  2. 1 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 Electronics Engineers.
  3. 1 2 Delgado P. Survey of Case-Based Reasoning as Applied to the Legal Domain
  4. 1 2 3 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. 1 2 3 4 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. ↑ Rissland, E.L., A.I. and Similarity, (2006), IEEE Intelligent Systems, 21(3), pp. 39-49
  10. ↑ Popple, J. (1996). A Pragmatic Legal Expert System (PDF). Applied Legal Philosophy Series. Dartmouth (Ashgate). pp. 42–43. ISBN   978-1-85521-739-3. Archived (PDF) from the original on 25 September 2006. Retrieved 7 August 2014. Alt URL Available at Open Library . Also available at Google Books.
  11. ↑ Zeleznikow, J. and Hunter, D., Rationales for the Continued Development of Legal Expert Systems, (1992), 3, J.L. & Inf. Sci. 94
  12. ↑ Aleven, V., and Ashley, K.D., 1997, Evaluating a Learning Environment for Case Based Argumentation Skills, in Proceedings of the Sixth International Conference on Artificial Intelligence and Law, Melbourne, ACM Press, New York. pp 170-179
  13. ↑ 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
  14. ↑ Ashley-Aleven-1991
  15. ↑ Ashley, K.D., Case-Based Reasoning and its Implications for Legal Expert Systems, (1992), Artificial Intelligence and Law 1, pp. 113-208
  16. ↑ 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
  17. ↑ 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.

Further reading

This page is based on this Wikipedia article
Text is available under the CC BY-SA 4.0 license; additional terms may apply.
Images, videos and audio are available under their respective licenses.