Resumen
Copyright © PMLR 2017. All rights reserved. We study languages that specify Markov Decision Processes with Imprecise Probabilities (MDPIPs) by mixing probabilities and logic programming. We propose a novel language that can capture MDPIPs and Markov Decision Processes with Set-valued Transitions (MDPSTs); we then obtain the complexity of one-step inference for the resulting MDPIPs and MDPSTs. We also present results of independent interest on the complexity of inference with probabilistic logic programs containing interval-valued probabilistic assessments. Finally, we also discuss policy generation techniques.
Idioma original | Inglés estadounidense |
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Páginas | 49-60 |
Número de páginas | 12 |
Estado | Publicada - 1 ene. 2019 |
Publicado de forma externa | Sí |
Evento | Proceedings of the 10th International Symposium on Imprecise Probability: Theories and Applications, ISIPTA 2017 - Duración: 1 ene. 2019 → … |
Conferencia
Conferencia | Proceedings of the 10th International Symposium on Imprecise Probability: Theories and Applications, ISIPTA 2017 |
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Período | 1/01/19 → … |