Monte-Carlo Tree Search for Logistics

Abstract

In this paper we review recent advances of randomized AI search in solving industrially relevant optimization problems. The method we focus on is a sampling-based solution mechanism called Monte-Carlo Tree Search (MCTS), which is extended by the concepts of nestedness and policy adaptation to establish a better trade-off between exploitation and exploration. This method, originating in game playing research, is a general heuristic search technique, for which often less problem-specific knowledge has to be added than in comparable approaches.