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 conferenciaArtículo 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 originalInglés estadounidense
Páginas337-342
Número de páginas6
DOI
EstadoPublicada - 1 feb 2017
Publicado de forma externa
EventoProceedings - 2016 5th Brazilian Conference on Intelligent Systems, BRACIS 2016 -
Duración: 1 feb 2017 → …

Conferencia

ConferenciaProceedings - 2016 5th Brazilian Conference on Intelligent Systems, BRACIS 2016
Período1/02/17 → …

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    Bueno, T. P., Maua, D. D., De Barros, L. N., & Cozman, F. G. (2017). Markov Decision Processes Specified by Probabilistic Logic Programming: Representation and Solution. 337-342. Papel presentado en Proceedings - 2016 5th Brazilian Conference on Intelligent Systems, BRACIS 2016, . https://doi.org/10.1109/BRACIS.2016.068