Solving Limited Memory Influence Diagrams

D. D. Maua, C. P. De Campos, M. Zaffalon
2012 The Journal of Artificial Intelligence Research  
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
more » ... 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.
doi:10.1613/jair.3625 fatcat:sfgkiovfdjbgpcihpt3z2lvima