A Monte-Carlo tree search in argumentation
Régis Riveret, Cameron Browne, Dídac Busquets, Jeremy V. Pitt · QUT ePrints (Queensland University of Technology) · 2014
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.