Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
An algorithm for probabilistic planning
Artificial Intelligence - Special volume on planning and scheduling
The computational complexity of probabilistic planning
Journal of Artificial Intelligence Research
Pushing the envelope: planning, propositional logic, and stochastic search
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Probabilistic propositional planning: representations and complexity
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Complexity of finite-horizon Markov decision process problems
Journal of the ACM (JACM)
Contingent planning under uncertainty via stochastic satisfiability
Artificial Intelligence - special issue on planning with uncertainty and incomplete information
When plans distinguish Bayes nets
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
A dynamic compiler for embedded Java virtual machines
Proceedings of the 3rd international symposium on Principles and practice of programming in Java
A selective dynamic compiler for embedded Java virtual machines targeting ARM processors
Science of Computer Programming - Special issue: Principles and practices of programming in Java (PPPJ 2004)
Performing Bayesian inference by weighted model counting
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Journal of Artificial Intelligence Research
The computational complexity of probabilistic planning
Journal of Artificial Intelligence Research
DPLL with a trace: from SAT to knowledge compilation
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
ACM Transactions on Computation Theory (TOCT)
Algorithms for memory hierarchies: advanced lectures
Algorithms for memory hierarchies: advanced lectures
Heuristics for fast exact model counting
SAT'05 Proceedings of the 8th international conference on Theory and Applications of Satisfiability Testing
Hi-index | 0.00 |
Probabilistic planning algorithms seek effective plans for large, stochastic domains. MAXPLAN is a recently developed algorithm that converts a planning problem into an E-MAJSAT problem, an NpPP-complete problem that is essentially a probabilistic version of SAT, and draws on techniques from Boolean satisfiability and dynamic programming to solve the E-MAJSAT problem. This solution method is able to solve planning problems at state-of-the-art speeds, but it depends on the ability to store a value for each CNF subformula encountered in the solution process and is therefore quite memory intensive; searching for moderate-size plans even on simple problems can exhaust memory. This paper presents two techniques, based on caching, that overcome this problem without significant performance degradation. The first technique uses an LRU cache to store a fixed number of subformula values. The second technique uses a heuristic based on a measure of subformula difficulty to selectively save the values of only those subformulas whose values are sufficiently difficult to compute and are likely to be reused later in the solution process. We report results for both techniques on a stochastic test problem.