On the complexity of solving polytree-shaped limited memory influence diagrams with binary variables

DOI10.1016/j.artint.2013.10.002zbMath1334.68202OpenAlexW1991902833WikidataQ62046532 ScholiaQ62046532MaRDI QIDQ490443

Cassio Polpo de Campos, Denis Deratani Mauá, Marco Zaffalon

Publication date: 27 August 2015

Published in: Artificial Intelligence (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.artint.2013.10.002

zbMATH Keywords

computational complexitydecision theoryinfluence diagramsprobabilistic planningdecision networks

Mathematics Subject Classification ID

Analysis of algorithms and problem complexity (68Q25) Decision theory (91B06) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20)

Related Items (3)

Equivalences between maximum a posteriori inference in Bayesian networks and maximum expected utility computation in influence diagrams ⋮ Fast local search methods for solving limited memory influence diagrams ⋮ Robustifying sum-product networks

Cites Work

• Unnamed Item
• Decision-theoretic specification of credal networks: a unified language for uncertain modeling with sets of Bayesian networks
• 2U: an exact interval propagation algorithm for polytrees with binary variables
• Credal networks
• A note about redundancy in influence diagrams
• Solving Limited Memory Influence Diagrams
• Influence Diagrams with Memory States: Representation and Algorithms
• Representing and Solving Decision Problems with Limited Information
• Dynamic programming and influence diagrams
• Bayesian Networks and Decision Graphs

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