Probabilistic reasoning with answer sets
| dc.creator | Baral, Chitta | |
| dc.creator | Gelfond, Michael | |
| dc.creator | Rushton, Nelson | |
| dc.date | 2008-12-03 | |
| dc.date.accessioned | 2026-07-07T12:08:44Z | |
| dc.date.available | 2026-07-07T12:08:44Z | |
| dc.description | This paper develops a declarative language, P-log, that combines logical and probabilistic arguments in its reasoning. Answer Set Prolog is used as the logical foundation, while causal Bayes nets serve as a probabilistic foundation. We give several non-trivial examples and illustrate the use of P-log for knowledge representation and updating of knowledge. We argue that our approach to updates is more appealing than existing approaches. We give sufficiency conditions for the coherency of P-log programs and show that Bayes nets can be easily mapped to coherent P-log programs. | |
| dc.description | 77 pages. To appear in Theory and Practice of Logic Programming (TPLP) | |
| dc.identifier | https://arxiv.org/abs/0812.0659 | |
| dc.identifier | http://arxiv.org/abs/0812.0659 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/209422 | |
| dc.subject | Artificial Intelligence | |
| dc.subject | Logic in Computer Science | |
| dc.title | Probabilistic reasoning with answer sets | |
| dc.type | text |