Modeling Markov decision processes with imprecise probabilities using probabilistic logic programming

Thiago P. Bueno; Denis D. Mauá; Leliane N. De Barros; Fabio G. Cozman

Modeling Markov decision processes with imprecise probabilities using probabilistic logic programming

Thiago P. Bueno, Denis D. Mauá, Leliane N. De Barros, Fabio G. Cozman

Resultado de la investigación: Contribución a una conferencia › Artículo de conferencia

1 Cita (Scopus)

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
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
Período	1/01/19 → …

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Modeling Markov decision processes with imprecise probabilities using probabilistic logic programming. / Bueno, Thiago P.; Mauá, Denis D.; De Barros, Leliane N.; Cozman, Fabio G.

2019. 49-60 Papel presentado en Proceedings of the 10th International Symposium on Imprecise Probability: Theories and Applications, ISIPTA 2017, .

Resultado de la investigación: Contribución a una conferencia › Artículo de conferencia

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AB - 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.

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