Concept Learning by a Monte-Carlo Tree Search of Argumentations

ABSTRACT. Monte-Carlo Tree Search (MCTS) is a heuristic to search in large trees. We apply it to argumentation with regard to concept learning where MCTS pursues the best argumentation meant to account for a possibly inconsistent set of training examples. We provide experimental results of our approach with the search variant Upper Confidence Bounds on Tree (UCT)