© 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.
|Original language||American English|
|Number of pages||6|
|State||Published - 1 Feb 2017|
|Event||Proceedings - 2016 5th Brazilian Conference on Intelligent Systems, BRACIS 2016 - |
Duration: 1 Feb 2017 → …
|Conference||Proceedings - 2016 5th Brazilian Conference on Intelligent Systems, BRACIS 2016|
|Period||1/02/17 → …|