A Monte-Carlo tree search in argumentation

Riveret, Regis, Browne, Cameron, Busquets, Didac, & Pitt, Jeremy (2014) A Monte-Carlo tree search in argumentation. In Proceedings of the Eleventh International Workshop on Argumentation in Multi-Agent Systems (ArgMAS 2014), Paris, France.

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Monte-Carlo Tree Search (MCTS) is a heuristic to search in large trees. We apply it to argumentative puzzles where MCTS pursues the best argumentation with respect to a set of arguments to be argued. To make our ideas as widely applicable as possible, we integrate MCTS to an abstract setting for argumentation where the content of arguments is left unspecified. Experimental results show the pertinence of this integration for learning argumentations by comparing it with a basic reinforcement learning.

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ID Code: 84997
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Deposited On: 25 Jun 2015 23:17
Last Modified: 18 Mar 2016 02:32

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