Principles of artificial intelligence
Principles of artificial intelligence
Learning to act using real-time dynamic programming
Artificial Intelligence - Special volume on computational research on interaction and agency, part 1
Decision-theoretic troubleshooting
Communications of the ACM
Planning and acting in partially observable stochastic domains
Artificial Intelligence
LAO: a heuristic search algorithm that finds solutions with loops
Artificial Intelligence - Special issue on heuristic search in artificial intelligence
Bounded real-time dynamic programming: RTDP with monotone upper bounds and performance guarantees
ICML '05 Proceedings of the 22nd international conference on Machine learning
Focused real-time dynamic programming for MDPs: squeezing more out of a heuristic
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Bayesian real-time dynamic programming
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Bucket elimination: a unifying framework for probabilistic inference
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
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Iterative Bounding LAO* is a new algorithm for ε-optimal probabilistic planning problems where an absorbing goal state should be reached at a minimum expected cost from a given ini tial state. The algorithm is based on the LAO* algorithm for finding optimal solutions in cyclic AND/OR graphs. The new algorithm uses two heuristics, one upper bound and one lower bound of the optimal cost. The search is guided by the lower bound as in LAO*, while the upper bound is used to prune search branches. The algorithm has a new mechanism for expanding search nodes, and while maintaining the error bounds, it may use weighted heuristics to reduce the size of the explored search space. In empirical tests on benchmark problems, Iterative Bounding LAO* expands fewer search nodes compared to state of the art RTDP variants that also use two-sided bounds.