Reasoning with portions of precedents
ICAIL '91 Proceedings of the 3rd international conference on Artificial intelligence and law
Artificial intelligence (3rd ed.)
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Rethinking the ownership of information in the21st century: Ethical implications
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Teaching case-based argumentation through a model and examples
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A model of legal reasoning with cases incorporating theories and values
Artificial Intelligence - Special issue on AI and law
Developing legal knowledge based systems through theory construction
ICAIL '03 Proceedings of the 9th international conference on Artificial intelligence and law
Predicting outcomes of case based legal arguments
ICAIL '03 Proceedings of the 9th international conference on Artificial intelligence and law
AGATHA: automated construction of case law theories through heuristic search
ICAIL '05 Proceedings of the 10th international conference on Artificial intelligence and law
An empirical investigation of reasoning with legal cases through theory construction and application
Artificial Intelligence and Law
An ontology in OWL for legal case-based reasoning
Artificial Intelligence and Law
Argument Based Moderation of Benefit Assessment
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Arguments from Experience: The PADUA Protocol
Proceedings of the 2008 conference on Computational Models of Argument: Proceedings of COMMA 2008
PADUA: a protocol for argumentation dialogue using association rules
Artificial Intelligence and Law
Towards formalising argumentation about legal cases
Proceedings of the 13th International Conference on Artificial Intelligence and Law
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In this paper we describe AGATHA, a program designed to automate the process of theory construction in case based domains. Given a seed case and a number of precedent cases, the program uses a set of argument moves to generate a search space for a dialogue between the parties to the dispute. Each move is associated with a set of theory constructors, and thus each point in the space can be associated with a theory intended to explain the seed case and the other cases in the domain. The space is large and so an heuristic search method is needed. This paper describes two methods based on A* and alpha/beta pruning and also a series of experiments designed to explore the appropriateness of different evaluation functions, the most useful precedents to use as seed cases and the quality of the resulting theories.