Issue spotting in a system for searching interpretation spaces

ABSTRACT

A method for spotting issues is described which uses a system we are developing for searching interpretations spaces and constructing legal arguments. The system is compatible with the legal philosophy known as legal positivism, but does not depend on its notion of clear cases. AI methods applied in the system include an ATMS reason maintenance system, Poole's framework for default reasoning, and an interactive natural deduction theorem prover with a programmable control component for including domain-dependent heuristic knowledge. Our issue spotting method is compared with Gardner's program for identifying the hard and easy issues raised by offer and acceptance law school examination questions.

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INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.1 Applications and Expert Systems
          Subjects: Law

Additional Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.3 Deduction and Theorem Proving
          Subjects: Answer/reason extraction; Uncertainty, "fuzzy," and probabilistic reasoning

General Terms:
Design, Legal Aspects