Learning to Understand Contractual Situations
Proceedings of the Ninth International Joint Conference on Artificial Intelligence. Cognitive Modelling. Pages 291-292.
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, 1tare deci1i1 which refers to the principle of using past cases to decide current disputes). STARE will read descriptions of contractual situations a nd attempt to predict the decision based on its general commonsense knowledge of agreements, the previous cases st ored in an episodic memory, and knowledge of some basic legal concepts.
@inproceedings{ijcai1985p54,
title = {Learning to Understand Contractual Situations},
author = {Goldman, Seth R. and Dyer, Michael G. and Flowers, Margot},
booktitle = {Proceedings of the Ninth International Joint Conference on
Artificial Intelligence, {IJCAI-85}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Aravind Joshi},
pages = {291--292},
year = {1985},
note = {Cognitive Modelling},
url = {https://ijcai.org/proceedings/1985/54},
}
TY - CPAPER TI - Learning to Understand Contractual Situations AU - Goldman, Seth R. AU - Dyer, Michael G. AU - Flowers, Margot T2 - Proceedings of the Ninth International Joint Conference on Artificial Intelligence, IJCAI-85 PB - International Joint Conferences on Artificial Intelligence PY - 1985 SP - 291 EP - 292 UR - https://ijcai.org/proceedings/1985/54 ER -Copied
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