Artificial Intelligence
Artificial Intelligence
Issue spotting in a system for searching interpretation spaces
ICAIL '89 Proceedings of the 2nd international conference on Artificial intelligence and law
An abductive theory of legal issues
International Journal of Man-Machine Studies - AI and legal reasoning. Part 2
Audiences in argumentation frameworks
Artificial Intelligence
The Carneades model of argument and burden of proof
Artificial Intelligence
Heuristics in Argumentation: A Game-Theoretical Investigation
Proceedings of the 2008 conference on Computational Models of Argument: Proceedings of COMMA 2008
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
Carneades and Abstract Dialectical Frameworks: A Reconstruction
Proceedings of the 2010 conference on Computational Models of Argument: Proceedings of COMMA 2010
Probabilistic Semantics for the Carneades Argument Model Using Bayesian Networks
Proceedings of the 2010 conference on Computational Models of Argument: Proceedings of COMMA 2010
Nonmonotonic tools for argumentation
JELIA'10 Proceedings of the 12th European conference on Logics in artificial intelligence
Analyzing open source license compatibility issues with Carneades
Proceedings of the 13th International Conference on Artificial Intelligence and Law
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When argumentation is conceived as a kind of process, typically a dialogue, for reasoning rationally with limited resources under conditions of incomplete and inconsistent information, arguers need heuristics for controlling the search for arguments to put foward, so as to move from stage to stage in the process in an efficient, goal-directed way. For this purpose, we have developed a formal model of abduction in argument evalution structures. An argument evaluation structure consists of the arguments of a stage, assumptions about audience and an assignment of proof standards to issues. A derivability relation is defined over argument evaluation structures for the literals 'in' a stage. Literals which are not derivable in a stage are 'out'. Abduction is defined as a relation between an argument evaluation structure and sets of literals, called 'positions', which, when the assumptions are revised to include the literals of the position, would make a goal literal in or out, depending of the standpoint of the agent. Soundness, minimiality, consistency and completeness properties of the abduction relation are proven. A heuristic cost function estimating how difficult it is to find or construct arguments pro a literal in the domain can be used to order positions and literals within positions. We compare our work to abduction in propositional logic, in particular the Assumption-Based Truth Maintenance System (ATMS).