Monte Carlo Tree Search for high precision manufacturing
Dorina Weichert, Felix Horchler, Alexander Kister, Marcus Trost,, Johannes Hartung, Stefan Risse

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.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsArtificial Intelligence in Games · AI-based Problem Solving and Planning · Sports Analytics and Performance
