| Presentation | 2021-03-03 Markov Decision Processes for Simultaneous Control of Multiple Objects with Different State Transition Probabilities in Each Cluster Yuto Motomura, Akira Kamatsuka, Koki Kazama, Toshiyasu Matsushima, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | In this study, we propose an extended MDP model, which is a Markov decision process model with multiple control objects and different state transition probabilities for each cluster. In the model, we consider problems of deriving policies from historical data of each objects which are collected in advance. We formulate the problems based on the Bayesian decision theory for both known and unknown state transition probabilities. Then we derive the optimal solutions of the problems. Finally, we propose calculation algorithms for the optimal solutions. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Markov decision processes / Bayesian decision theory / machine learning / collective control / recommender system / recommendation system |
| Paper # | IBISML2020-49 |
| Date of Issue | 2021-02-23 (IBISML) |
| Conference Information | |
| Committee | IBISML |
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| Conference Date | 2021/3/2(3days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Organized and general sessions on machine learning |
| Chair | Ichiro Takeuchi(Nagoya Inst. of Tech.) |
| Vice Chair | Masashi Sugiyama(Univ. of Tokyo) / Koji Tsuda(Univ. of Tokyo) |
| Secretary | Masashi Sugiyama(AIST) / Koji Tsuda(NTT) |
| Assistant | Atsuyoshi Nakamura(Hokkaido Univ.) / Shigeyuki Oba(Miidas) |
| Paper Information | |
| Registration To | Technical Committee on Infomation-Based Induction Sciences and Machine Learning |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Markov Decision Processes for Simultaneous Control of Multiple Objects with Different State Transition Probabilities in Each Cluster |
| Sub Title (in English) | |
| Keyword(1) | Markov decision processes |
| Keyword(2) | Bayesian decision theory |
| Keyword(3) | machine learning |
| Keyword(4) | collective control |
| Keyword(5) | recommender system |
| Keyword(6) | recommendation system |
| 1st Author's Name | Yuto Motomura |
| 1st Author's Affiliation | Waseda University(Waseda Univ.) |
| 2nd Author's Name | Akira Kamatsuka |
| 2nd Author's Affiliation | Waseda University(Waseda Univ.) |
| 3rd Author's Name | Koki Kazama |
| 3rd Author's Affiliation | Waseda University(Waseda Univ.) |
| 4th Author's Name | Toshiyasu Matsushima |
| 4th Author's Affiliation | Waseda University(Waseda Univ.) |
| Date | 2021-03-03 |
| Paper # | IBISML2020-49 |
| Volume (vol) | vol.120 |
| Number (no) | IBISML-395 |
| Page | pp.pp.47-54(IBISML), |
| #Pages | 8 |
| Date of Issue | 2021-02-23 (IBISML) |