Specifying credal sets with probabilistic answer set programming (2023)
- Authors:
- USP affiliated authors: MAUÁ, DENIS DERATANI - IME ; COZMAN, FABIO GAGLIARDI - EP
- Unidades: IME; EP
- Assunto: PROGRAMAÇÃO LÓGICA
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título: Proceedings of Machine Learning Research
- ISSN: 2640-3498
- Volume/Número/Paginação/Ano: v. 215, p. 321-332, 2023
- Conference titles: International Symposium on Imprecise Probability: Theories and Applications - ISIPTA
-
ABNT
MAUÁ, Denis Deratani e COZMAN, Fabio Gagliardi. Specifying credal sets with probabilistic answer set programming. Proceedings of Machine Learning Research. Brookline: Instituto de Matemática e Estatística, Universidade de São Paulo. Disponível em: https://proceedings.mlr.press/v215/maua23a/maua23a.pdf. Acesso em: 09 fev. 2026. , 2023 -
APA
Mauá, D. D., & Cozman, F. G. (2023). Specifying credal sets with probabilistic answer set programming. Proceedings of Machine Learning Research. Brookline: Instituto de Matemática e Estatística, Universidade de São Paulo. Recuperado de https://proceedings.mlr.press/v215/maua23a/maua23a.pdf -
NLM
Mauá DD, Cozman FG. Specifying credal sets with probabilistic answer set programming [Internet]. Proceedings of Machine Learning Research. 2023 ; 215 321-332.[citado 2026 fev. 09 ] Available from: https://proceedings.mlr.press/v215/maua23a/maua23a.pdf -
Vancouver
Mauá DD, Cozman FG. Specifying credal sets with probabilistic answer set programming [Internet]. Proceedings of Machine Learning Research. 2023 ; 215 321-332.[citado 2026 fev. 09 ] Available from: https://proceedings.mlr.press/v215/maua23a/maua23a.pdf - Dealing with cycles in graph-based probabilistic models: the case of Logical Credal Networks
- The joy of probabilistic answer set programming: semantics, complexity, expressivity, inference
- Thirty years of credal networks: specification, algorithms and complexity
- Probabilistic logic programming under the L-Stable semantics
- The complexity of inferences and explanations in probabilistic logic programming
- The descriptive complexity of bayesian network specifications
- Robustifying sum-product networks
- The complexity of Bayesian networks specified by propositional and relational languages
- The finite model theory of bayesian networks: descriptive complexity
- Complexity results for probabilistic answer set programming
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