Solving Limited Memory Influence Diagrams
2012 ◽
Vol 44
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pp. 97-140
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Keyword(s):
Np Hard
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We present a new algorithm for exactly solving decision making problems represented as influence diagrams. We do not require the usual assumptions of no forgetting and regularity; this allows us to solve problems with simultaneous decisions and limited information. The algorithm is empirically shown to outperform a state-of-the-art algorithm on randomly generated problems of up to 150 variables and 10^64 solutions. We show that these problems are NP-hard even if the underlying graph structure of the problem has low treewidth and the variables take on a bounded number of states, and that they admit no provably good approximation if variables can take on an arbitrary number of states.
2016 ◽
Vol 6
(2)
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pp. 205-215
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Keyword(s):
2010 ◽
Vol 17
(2)
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pp. 197-206
2008 ◽
Vol 186
(1)
◽
pp. 261-275
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2016 ◽
Vol 113
(31)
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pp. E4531-E4540
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2016 ◽
Vol 68
◽
pp. 230-245
Keyword(s):