| Texto completo | |
| Autor(es): |
Cozman, Fabio Gagliardi
;
DeBock, J
;
DeCampos, CP
;
DeCooman, G
;
Quaeghebeur, E
;
Wheeler, G
Número total de Autores: 6
|
| Tipo de documento: | Artigo Científico |
| Fonte: | INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 162; v. 103, p. 11-pg., 2019-01-01. |
| Resumo | |
Probabilistic answer set programming (PASP) combines rules, facts, and independent probabilistic facts. Often one restricts such programs so that every query yields a sharp probability value. The purpose of this paper is to argue that a very useful modeling language is obtained by adopting a particular credal semantics for PASP, where one associates with each consistent program a credal set. We examine the basic properties of PASP and present an algorithm to compute (upper) probabilities given a program. (AU) | |
| Processo FAPESP: | 16/18841-0 - Algoritmos para inferência e aprendizado de programas lógicos probabilísticos |
| Beneficiário: | Fabio Gagliardi Cozman |
| Modalidade de apoio: | Auxílio à Pesquisa - Parceria para Inovação Tecnológica - PITE |