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  1. An integrated approach to solving influence diagrams and finite-horizon partially observable decision processes.Eric A. Hansen - 2021 - Artificial Intelligence 294 (C):103431.
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  • Semi-Supervised Learning of Cartesian Factors: A Top-Down Model of the Entorhinal Hippocampal Complex.András Lőrincz & András Sárkány - 2017 - Frontiers in Psychology 8.
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  • Real-time dynamic programming for Markov decision processes with imprecise probabilities.Karina V. Delgado, Leliane N. de Barros, Daniel B. Dias & Scott Sanner - 2016 - Artificial Intelligence 230 (C):192-223.
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  • A decision network account of reasoning about other people’s choices.Alan Jern & Charles Kemp - 2015 - Cognition 142 (C):12-38.
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  • Complexity results and algorithms for possibilistic influence diagrams.Laurent Garcia & Régis Sabbadin - 2008 - Artificial Intelligence 172 (8-9):1018-1044.
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  • Decision-theoretic planning with generalized first-order decision diagrams.Saket Joshi, Kristian Kersting & Roni Khardon - 2011 - Artificial Intelligence 175 (18):2198-2222.
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  • Rethinking formal models of partially observable multiagent decision making.Vojtěch Kovařík, Martin Schmid, Neil Burch, Michael Bowling & Viliam Lisý - 2022 - Artificial Intelligence 303 (C):103645.
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  • Equivalence notions and model minimization in Markov decision processes.Robert Givan, Thomas Dean & Matthew Greig - 2003 - Artificial Intelligence 147 (1-2):163-223.
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