Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Local and global relational consistency
Theoretical Computer Science - Special issue: principles and practice of constraint programming
Journal of Artificial Intelligence Research
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Compiling uncertainty away in conformant planning problems with bounded width
Journal of Artificial Intelligence Research
A translation-based approach to contingent planning
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Rao-blackwellised particle filtering for dynamic Bayesian networks
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Bandit based monte-carlo planning
ECML'06 Proceedings of the 17th European conference on Machine Learning
Planning under partial observability by classical replanning: theory and experiments
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
On the effectiveness of CNF and DNF representations in contingent planning
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
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Belief tracking is a basic problem in planning with sensing. While the problem is intractable, it has been recently shown that for both deterministic and non-deterministic systems expressed in compact form, it can be done in time and space that are exponential in the problem width. The width measures the maximum number of state variables that are all relevant to a given precondition or goal. In this work, we extend this result both theoretically and practically. First, we introduce an alternative decomposition scheme and algorithm with the same time complexity but different completeness guarantees, whose space complexity is much smaller: exponential in the causal width of the problem that measures the number of state variables that are causally relevant to a given precondition, goal, or observable. Second, we introduce a fast, meaningful, and powerful approximation that trades completeness by speed, and is both time and space exponential in the problem causal width. It is then shown empirically that the algorithm combined with simple heuristics yields state-of-the-art real-time performance in domains with high widths but low causal widths such as Minesweeper, Battleship, and Wumpus.