Markov Decision Processes Specified by Probabilistic Logic Programming: Representation and Solution

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

doi:10.1109/BRACIS.2016.068

Markov Decision Processes Specified by Probabilistic Logic Programming: Representation and Solution

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

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

2 Citas (Scopus)

Resumen

© 2016 IEEE. Probabilistic logic programming combines logic and probability, so as to obtain a rich modeling language. In this work, we extend ProbLog, a popular probabilistic logic programming language, with new constructs that allow the representation of (infinite-horizon) Markov decision processes. This new language can represent relational statements, including symmetric and transitive definitions, an advantage over other planning domain languages such as RDDL. We show how to exploit the logic structure in the language to perform Value Iteration. Preliminary experiments demonstrate the effectiveness of our framework.

Idioma original	Inglés estadounidense
Páginas	337-342
Número de páginas	6
DOI	https://doi.org/10.1109/BRACIS.2016.068
Estado	Publicada - 1 feb 2017
Publicado de forma externa	Sí
Evento	Proceedings - 2016 5th Brazilian Conference on Intelligent Systems, BRACIS 2016 - Duración: 1 feb 2017 → …

Conferencia

Conferencia	Proceedings - 2016 5th Brazilian Conference on Intelligent Systems, BRACIS 2016
Período	1/02/17 → …

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10.1109/BRACIS.2016.068

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Markov Decision Processes Specified by Probabilistic Logic Programming: Representation and Solution. / Bueno, Thiago P.; Maua, Denis D.; De Barros, Leliane N.; Cozman, Fabio G.

2017. 337-342 Papel presentado en Proceedings - 2016 5th Brazilian Conference on Intelligent Systems, BRACIS 2016, .

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

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