Automated Inference of Rules with Exception from Past Legal Cases Using ASP

In legal reasoning, different assumptions are often considered when reaching a final verdict and judgement outcomes strictly depend on these assumptions. In this paper, we propose an approach for generating a declarative model of judgements from past legal cases, that expresses a legal reasoning structure in terms of principle rules and exceptions. Using a logic-based reasoning technique, we are able to identify from given past cases different underlying defaults (legal assumptions) and compute judgements that cover all possible cases (including past cases) within a given set of relevant factors. The extracted declarative model of judgements can then be used to make deterministic automated inference on future judgements, as well as generate explanations of legal decisions.

[1]  Ken Satoh.  Statutory Interpretation by Case-Based Reasoning through Abductive Logic Programming , 1997, J. Adv. Comput. Intell. Intell. Informatics.

[2]  John F. Horty,et al.  Reasons and precedent , 2011, ICAIL.

[3]  Francesca Toni,et al.  Argumentation and answer set programming , 2011 .

[4]  Marius Thomas Lindauer,et al.  Potassco: The Potsdam Answer Set Solving Collection , 2011, AI Commun..

[5]  Phan Minh Dung,et al.  On the Acceptability of Arguments and its Fundamental Role in Nonmonotonic Reasoning, Logic Programming and n-Person Games , 1995, Artif. Intell..

[6]  Ken Satoh,et al.  Translating Case-Based Reasoning into Abductive Logic Programming , 1996, ECAI.

[7]  Trevor J. M. Bench-Capon,et al.  Argumentation in Legal Reasoning , 2009, Argumentation in Artificial Intelligence.

[8]  Trevor J. M. Bench-Capon,et al.  Hierarchical Formalizations , 1991, Int. J. Man Mach. Stud..

[9]  Ken Satoh,et al.  Translating the Japanese Presupposed Ultimate Fact Theory into Logic Programming , 2009, JURIX.

[10]  Noboru Babaguchi,et al.  Determination of General Concept in Learning Default Rules , 2000, PRICAI.

[11]  Ken Satoh,et al.  Analysis of Case-Based Representability of Boolean Functions by Monotone Theory , 1998, ALT.

[12]  Antonis C. Kakas,et al.  Learning Non-Monotonic Logic Programs: Learning Exceptions , 1995, ECML.

[13]  Kevin D. Ashley.  Reasoning with Cases and Hypotheticals in HYPO , 1991, Int. J. Man Mach. Stud..

[14]  Ken Satoh,et al.  PROLEG: An Implementation of the Presupposed Ultimate Fact Theory of Japanese Civil Code by PROLOG Technology , 2010, JSAI-isAI Workshops.