Learning to understand contractual situations

  • Authors:

  • Seth R. Goldman;Michael G. Dyer;Margot Flowers

  • Affiliations:

  • Artificial Intelligence Laboratory, Computer Science Department, University of California, Los Angeles, CA;Artificial Intelligence Laboratory, Computer Science Department, University of California, Los Angeles, CA;Artificial Intelligence Laboratory, Computer Science Department, University of California, Los Angeles, CA

  • Venue:
  • IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 1
  • Year:
  • 1985

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Abstract

In the field of law, decisions in previous cases often play a significant role in the presentation and outcome of new cases. Lawyers are constantly recalling old cases to aid them in preparing their own briefs. How do lawyers remember cases? What are the features they use to organize and retrieve past decisions? How do lawyers learn which features are important? To address these Questions we are constructing a model of legal novices (i.e. first year law student) and the processes by which they learn contract law. Our model is embodied in a computer program called STARE (from the latin, stare decisis which refers to the principle of using past cases to decide current disputes). STARE will read descriptions of contractual situations and attempt to predict the decision based on its general commonsense knowledge of agreements, the previous cases stored in an episodic memory, and knowledge of some basic legal concepts.