Modeling Markov Decision Processes with Imprecise Probabilities Using Probabilistic Logic Programming

Thiago P. Bueno, Denis D. Mauá, Leliane N. Barros, Fabio G. Cozman
Proceedings of the Tenth International Symposium on Imprecise Probability: Theories and Applications, PMLR 62:49-60, 2017.

Abstract

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v62-bueno17a, title = {Modeling {M}arkov Decision Processes with Imprecise Probabilities Using Probabilistic Logic Programming}, author = {Bueno, Thiago P. and Mauá, Denis D. and Barros, Leliane N. and Cozman, Fabio G.}, booktitle = {Proceedings of the Tenth International Symposium on Imprecise Probability: Theories and Applications}, pages = {49--60}, year = {2017}, editor = {Antonucci, Alessandro and Corani, Giorgio and Couso, Inés and Destercke, Sébastien}, volume = {62}, series = {Proceedings of Machine Learning Research}, month = {10--14 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v62/bueno17a/bueno17a.pdf}, url = {https://proceedings.mlr.press/v62/bueno17a.html}, abstract = {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.} }
Endnote
%0 Conference Paper %T Modeling Markov Decision Processes with Imprecise Probabilities Using Probabilistic Logic Programming %A Thiago P. Bueno %A Denis D. Mauá %A Leliane N. Barros %A Fabio G. Cozman %B Proceedings of the Tenth International Symposium on Imprecise Probability: Theories and Applications %C Proceedings of Machine Learning Research %D 2017 %E Alessandro Antonucci %E Giorgio Corani %E Inés Couso %E Sébastien Destercke %F pmlr-v62-bueno17a %I PMLR %P 49--60 %U https://proceedings.mlr.press/v62/bueno17a.html %V 62 %X 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.
APA
Bueno, T.P., Mauá, D.D., Barros, L.N. & Cozman, F.G.. (2017). Modeling Markov Decision Processes with Imprecise Probabilities Using Probabilistic Logic Programming. Proceedings of the Tenth International Symposium on Imprecise Probability: Theories and Applications, in Proceedings of Machine Learning Research 62:49-60 Available from https://proceedings.mlr.press/v62/bueno17a.html.

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