Modeling Markov decision processes with imprecise probabilities using probabilistic logic programming (2017)
- Authors:
- USP affiliated authors: MAUÁ, DENIS DERATANI - IME ; BARROS, LELIANE NUNES DE - IME ; COZMAN, FABIO GAGLIARDI - EP
- Unidades: IME; EP
- Subjects: PROCESSOS DE MARKOV; PROGRAMAÇÃO LÓGICA; MODELOS PARA PROCESSOS ESTOCÁSTICOS
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título: PMLR: Proceedings of Machine Learning Research
- ISSN: 1938-7228
- Volume/Número/Paginação/Ano: n. 62, p. 49-60, 2017
- Conference titles: International Symposium on Imprecise Probability: Theories and Applications - ISIPTA
-
ABNT
BUENO, Thiago Pereira et al. Modeling Markov decision processes with imprecise probabilities using probabilistic logic programming. PMLR: Proceedings of Machine Learning Research. Brookline: Instituto de Matemática e Estatística, Universidade de São Paulo. Disponível em: http://proceedings.mlr.press/v62/bueno17a.html. Acesso em: 01 abr. 2025. , 2017 -
APA
Bueno, T. P., Mauá, D. D., Barros, L. N. de, & Cozman, F. G. (2017). Modeling Markov decision processes with imprecise probabilities using probabilistic logic programming. PMLR: Proceedings of Machine Learning Research. Brookline: Instituto de Matemática e Estatística, Universidade de São Paulo. Recuperado de http://proceedings.mlr.press/v62/bueno17a.html -
NLM
Bueno TP, Mauá DD, Barros LN de, Cozman FG. Modeling Markov decision processes with imprecise probabilities using probabilistic logic programming [Internet]. PMLR: Proceedings of Machine Learning Research. 2017 ;( 62): 49-60.[citado 2025 abr. 01 ] Available from: http://proceedings.mlr.press/v62/bueno17a.html -
Vancouver
Bueno TP, Mauá DD, Barros LN de, Cozman FG. Modeling Markov decision processes with imprecise probabilities using probabilistic logic programming [Internet]. PMLR: Proceedings of Machine Learning Research. 2017 ;( 62): 49-60.[citado 2025 abr. 01 ] Available from: http://proceedings.mlr.press/v62/bueno17a.html - Markov decision processes specified by probabilistic logic programming: representation and solution
- When a robot reaches out for human help
- Specifying credal sets with probabilistic answer set programming
- On the complexity of propositional and relational credal networks
- On the semantics and complexity of probabilistic logic programs
- Credal sum-product networks
- DL-Lite Bayesian networks: a tractable probabilistic graphical model
- The joy of probabilistic answer set programming: semantics, complexity, expressivity, inference
- The finite model theory of Bayesian network specifications: Descriptive complexity and zero/one laws
- The complexity of Bayesian networks specified by propositional and relational languages
Download do texto completo
| Tipo | Nome | Link | |
|---|---|---|---|
 |
2875790.pdf |
How to cite
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
