| Full text | |
| Author(s): |
Bueno, Thiago P.
;
Maua, Denis D.
;
de Barros, Leliane N.
;
Cozman, Fabio G.
;
IEEE
Total Authors: 5
|
| Document type: | Journal article |
| Source: | PROCEEDINGS OF 2016 5TH BRAZILIAN CONFERENCE ON INTELLIGENT SYSTEMS (BRACIS 2016); v. N/A, p. 6-pg., 2016-01-01. |
| Abstract | |
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. (AU) | |
| FAPESP's process: | 15/01587-0 - Storage, modeling and analysis of dynamical systems for e-Science applications |
| Grantee: | João Eduardo Ferreira |
| Support Opportunities: | Research Grants - eScience and Data Science Program - Thematic Grants |
| FAPESP's process: | 16/01055-1 - Learning of Tractable Probabilistic Models with Application to Multilabel Classification |
| Grantee: | Denis Deratani Mauá |
| Support Opportunities: | Regular Research Grants |