AsistO: A Qualitative MDP-based Recommender System for Power Plant Operation
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| Format: | Artículo científico |
| Language: | en |
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Instituto Politécnico Nacional
2009
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| _version_ | 1876465839112192000 |
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| author | Alberto Reyes |
| author_facet | Alberto Reyes |
| contents | AsistO: A Qualitative MDP-based Recommender System for Power Plant Operation Alberto Reyes L. Enrique Sucar Eduardo F. Morales Computación power plants abstractions Recommender systems Markov decision processes This paper proposes a novel and practical model-based learning approach with iterative refinement for solving continuous (and hybrid) Markov decision processes. Initially, an approximate model is learned using conventional sampling methods and solved to obtain a policy. Iteratively, the approximate model is refined using variance in the utility values as partition criterion. In the learning phase, initial reward and transition functions are obtained by sampling the state-action space. The samples are used to induce a decision tree predicting reward values from which an initial partition of the state space is built. The samples are also used to induce a factored MDP. The state abstraction is then refined by splitting states only where the split is locally important. The main contributions of this paper are the use of sampling to construct an abstraction, and a local refinement process of the state abstraction based on utility variance. The proposed technique was tested in AsistO, an intelligent recommender system for power plant operation, where we solved two versions of a complex hybrid continuous-discrete problem. We show how our technique approximates a solution even in cases where standard methods explode computationally. 2009 artículo científico 1405-5546 https://www.redalyc.org/articulo.oa?id=61513250002 en http://www.redalyc.org/revista.oa?id=615 Computación y Sistemas application/pdf Instituto Politécnico Nacional Computación y Sistemas (México) Num.1 Vol.13 |
| format | Artículo científico |
| id | redalyc_61513250002 |
| institution | Redalyc |
| language | en |
| publishDate | 2009 |
| publisher | Instituto Politécnico Nacional |
| spellingShingle | AsistO: A Qualitative MDP-based Recommender System for Power Plant Operation Alberto Reyes Computación power plants abstractions Recommender systems Markov decision processes AsistO: A Qualitative MDP-based Recommender System for Power Plant Operation Alberto Reyes L. Enrique Sucar Eduardo F. Morales Computación power plants abstractions Recommender systems Markov decision processes This paper proposes a novel and practical model-based learning approach with iterative refinement for solving continuous (and hybrid) Markov decision processes. Initially, an approximate model is learned using conventional sampling methods and solved to obtain a policy. Iteratively, the approximate model is refined using variance in the utility values as partition criterion. In the learning phase, initial reward and transition functions are obtained by sampling the state-action space. The samples are used to induce a decision tree predicting reward values from which an initial partition of the state space is built. The samples are also used to induce a factored MDP. The state abstraction is then refined by splitting states only where the split is locally important. The main contributions of this paper are the use of sampling to construct an abstraction, and a local refinement process of the state abstraction based on utility variance. The proposed technique was tested in AsistO, an intelligent recommender system for power plant operation, where we solved two versions of a complex hybrid continuous-discrete problem. We show how our technique approximates a solution even in cases where standard methods explode computationally. 2009 artículo científico 1405-5546 https://www.redalyc.org/articulo.oa?id=61513250002 en http://www.redalyc.org/revista.oa?id=615 Computación y Sistemas application/pdf Instituto Politécnico Nacional Computación y Sistemas (México) Num.1 Vol.13 |
| title | AsistO: A Qualitative MDP-based Recommender System for Power Plant Operation |
| topic | Computación power plants abstractions Recommender systems Markov decision processes |
| url | https://www.redalyc.org/articulo.oa?id=61513250002 |