Monte Carlo Tree Search for high precision manufacturing

TL;DR

This paper demonstrates the application of Monte Carlo Tree Search to optimize a complex, stochastic, and partially observable high-precision manufacturing process using an expert-knowledge-based simulator.

Contribution

It introduces a novel application of MCTS in real-world industrial manufacturing, addressing challenges with process complexity and partial observability.

Findings

Effective optimization of manufacturing process using MCTS

Adaptation of MCTS policy for complex manufacturing rules

Successful integration of expert-knowledge-based simulator

Abstract

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.