Collaborative interface agents
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Semantic Issues in the Verification of Agent Communication Languages
Autonomous Agents and Multi-Agent Systems
Success chances in argument games: a probabilistic approach to legal disputes
Proceedings of the 2007 conference on Legal Knowledge and Information Systems: JURIX 2007: The Twentieth Annual Conference
Heuristics in Argumentation: A Game-Theoretical Investigation
Proceedings of the 2008 conference on Computational Models of Argument: Proceedings of COMMA 2008
Choosing persuasive arguments for action
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Argumentation strategies for plan resourcing
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
ArgMAS'09 Proceedings of the 6th international conference on Argumentation in Multi-Agent Systems
Probabilistic argumentation frameworks
TAFA'11 Proceedings of the First international conference on Theory and Applications of Formal Argumentation
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A strategy is used by a participant in a persuasion dialogue to select locutions most likely to achieve its objective of persuading its opponent. Such strategies often assume that the participant has a model of its opponents, which may be constructed on the basis of a participant's accumulated dialogue experience. However in most cases the fact that an agent's experience may encode additional information which if appropriately used could increase a strategy's efficiency, is neglected. In this work, we rely on an agent's experience to define a mechanism for augmenting an opponent model with information likely to be dialectally related to information already contained in it. Precise computation of this likelihood is exponential in the volume of related information. We thus describe and evaluate an approximate approach for computing these likelihoods based on Monte-Carlo simulation.