Using mathematical programming to solve factored Markov decision processes with imprecise probabilities (2011)
- Authors:
- USP affiliated authors: DELGADO, KARINA VALDIVIA - EACH ; BARROS, LELIANE NUNES DE - IME ; COZMAN, FABIO GAGLIARDI - EP
- Unidades: EACH; IME; EP
- DOI: 10.1016/j.ijar.2011.04.002
- Assunto: PROGRAMAÇÃO MATEMÁTICA
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título do periódico: International Journal of Approximate Reasoning
- ISSN: 0888-613X
- Volume/Número/Paginação/Ano: v. 52, p. 1000-1017, 2011
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
ABNT
DELGADO, Karina Valdivia et al. Using mathematical programming to solve factored Markov decision processes with imprecise probabilities. International Journal of Approximate Reasoning, v. 52, p. 1000-1017, 2011Tradução . . Disponível em: https://doi.org/10.1016/j.ijar.2011.04.002. Acesso em: 19 abr. 2024. -
APA
Delgado, K. V., Barros, L. N. de, Cozman, F. G., & Sanner, S. (2011). Using mathematical programming to solve factored Markov decision processes with imprecise probabilities. International Journal of Approximate Reasoning, 52, 1000-1017. doi:10.1016/j.ijar.2011.04.002 -
NLM
Delgado KV, Barros LN de, Cozman FG, Sanner S. Using mathematical programming to solve factored Markov decision processes with imprecise probabilities [Internet]. International Journal of Approximate Reasoning. 2011 ; 52 1000-1017.[citado 2024 abr. 19 ] Available from: https://doi.org/10.1016/j.ijar.2011.04.002 -
Vancouver
Delgado KV, Barros LN de, Cozman FG, Sanner S. Using mathematical programming to solve factored Markov decision processes with imprecise probabilities [Internet]. International Journal of Approximate Reasoning. 2011 ; 52 1000-1017.[citado 2024 abr. 19 ] Available from: https://doi.org/10.1016/j.ijar.2011.04.002 - Factored Markov decision process with imprecise transition probabilities
- Representing and solving factored Markov decision processes with imprecise probabilities
- Robust optimization for hybrid MDPs with state-dependent noise
- Symbolic bounded real-time dynamic programming
- Risk-sensitive Markov decision process with limited budget
- Symbolic dynamic programming for discrete and continuous state MDPs
- Learning to program using hierarchical model-based debugging
- B^2RTDP: an efficient solution for bounded-parameter Markov decision process
- ProPAT: a programming ITS based on pedagogical patterns
- Robust topological policy iteration for infinite horizon bounded Markov decision processes
Informações sobre o DOI: 10.1016/j.ijar.2011.04.002 (Fonte: oaDOI API)
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