Monte Carlo Tree Search for high precision manufacturing
This work addresses the challenge of applying MCTS to real-world industrial processes, which is an incremental step in bridging the gap between theoretical methods and practical manufacturing applications.
The paper tackled the problem of applying Monte Carlo Tree Search (MCTS) to optimize a high-precision manufacturing process with stochastic and partially observable outcomes, achieving results by adapting the MCTS default policy using an expert-knowledge-based simulator.
Monte Carlo Tree Search (MCTS) has shown its strength for a lot of deterministic and stochastic examples, but literature lacks reports of applications to real world industrial processes. Common reasons for this are that there is no efficient simulator of the process available or there exist problems in applying MCTS to the complex rules of the process. In this paper, we apply MCTS for optimizing a high-precision manufacturing process that has stochastic and partially observable outcomes. We make use of an expert-knowledge-based simulator and adapt the MCTS default policy to deal with the manufacturing process.