David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Ezio Di Nucci
Jack Alan Reynolds
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This game3 was designed to investigate protocols and strategies for resourcebounded disputation. The rules presented here correspond very closely to the problem of controlling search in an actual program. The computer program on which the game is based is LMNOP. It is a LISP system designed to produce arguments and counterarguments from a set of statutory rules and a corpus of precedents, and applied to legal and quasi-legal reasoning. LMNOP was co-designed by a researcher in AI knowledge representation and by a trained computer scientist who was an editor of Washington University Law Review at the time. LMNOP is based on the idea of a non-demonstrative or defeasible rule: i.e., a rule that admits exceptions. It adopts a representational convention that supposes there is an implicit preference of more speciﬁc rules over less speciﬁc rules. In fact, it automatically adjudicates between competing arguments when one argument meets the broader criterion of being more speciﬁc than another. The convention is based on an idea origianlly presented by David Poole , and is embedded in a system of determining which arguments are ultimately warranted, which originally appeared in the literatures of epistemology and ethics, by Pollock . This system evolves from work by the ﬁrst author since 1987; the full statement of the theory is in . Prakken  is one example of the idea’s application to the legal domain. LMNOP also draws heavily on the model of legal reasoning and analogical reasoning put forward by Edwina Rissland and Kevin Ashley [89, 90]. Similarities to their legal casebased reasoning program, HYPO, are no accident; LMNOP seeks to improve on HYPO. A description of LMNOP is forthcoming
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Thomas F. Gordon (1993). The Pleadings Game. Artificial Intelligence and Law 2 (4):239-292.
Jaap C. Hage, Ronald Leenes & Arno R. Lodder (1993). Hard Cases: A Procedural Approach. [REVIEW] Artificial Intelligence and Law 2 (2):113-167.
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