An algorithm for probabilistic planning
Artificial Intelligence - Special volume on planning and scheduling
Initial experiments in stochastic satisfiability
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
A machine program for theorem-proving
Communications of the ACM
Stochastic Boolean Satisfiability
Journal of Automated Reasoning
Any-Space Probabilistic Inference
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Projecting plans for uncertain worlds
Projecting plans for uncertain worlds
Solving MAP exactly by searching on compiled arithmetic circuits
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Complexity results and approximation strategies for MAP explanations
Journal of Artificial Intelligence Research
The computational complexity of probabilistic planning
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
MAP complexity results and approximation methods
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Bucket elimination: a unifying framework for probabilistic inference
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Solving MAP exactly using systematic search
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Constraint-based optimal testing using DNNF graphs
CP'09 Proceedings of the 15th international conference on Principles and practice of constraint programming
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We present a new algorithm for computing upper bounds for an optimization version of the EMAJSAT problem called functional E-MAJSAT. The algorithm utilizes the compilation language d-DNNF which underlies several state-of-the-art algorithms for solving related problems. This bound computation can be used in a branch-and-bound solver for solving functional E-MAJSAT. We then present a technique for pruning values from the branch-and-bound search tree based on the information available after each bound computation. We evaluated the proposed techniques in a MAP solver and a probabilistic conformant planner. In both cases, our experiments showed that the new techniques improved the efficiency of state-of-the-art solvers by orders of magnitude.