Monte Carlo Tree Search for high precision manufacturing
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...
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| creator | Weichert, Dorina Horchler, Felix Kister, Alexander Trost, Marcus Hartung, Johannes Risse, Stefan |
| description | 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. |
| doi_str_mv | 10.48550/arxiv.2108.01789 |
| format | Article |
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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
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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
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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.</abstract><doi>10.48550/arxiv.2108.01789</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Science - Artificial Intelligence Computer Science - Learning |
| title | Monte Carlo Tree Search for high precision manufacturing |
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