Heuristics: intelligent search strategies for computer problem solving
Heuristics: intelligent search strategies for computer problem solving
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A model for reasoning about persistence and causation
Computational Intelligence
Modeling a dynamic and uncertain world I: symbolic and probabilistic reasoning about change
Artificial Intelligence
An algorithm for probabilistic planning
Artificial Intelligence - Special volume on planning and scheduling
On the hardness of approximate reasoning
Artificial Intelligence
Fast planning through planning graph analysis
Artificial Intelligence
Using regression-match graphs to control search in planning
Artificial Intelligence
Bucket elimination: a unifying framework for reasoning
Artificial Intelligence
Artificial Intelligence - special issue on computational tradeoffs under bounded resources
Artificial Intelligence - Special issue on heuristic search in artificial intelligence
Introduction to Bayesian Networks
Introduction to Bayesian Networks
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Contingent planning under uncertainty via stochastic satisfiability
Artificial Intelligence - special issue on planning with uncertainty and incomplete information
Conformant planning via heuristic forward search: a new approach
Artificial Intelligence
Model counting: a new strategy for obtaining good bounds
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Factored planning: how, when, and when not
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Performing Bayesian inference by weighted model counting
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Prottle: a probabilistic temporal planner
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
The FF planning system: fast plan generation through heuristic search
Journal of Artificial Intelligence Research
Engineering a conformant probabilistic planner
Journal of Artificial Intelligence Research
The computational complexity of probabilistic planning
Journal of Artificial Intelligence Research
From sampling to model counting
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Compiling Bayesian networks with local structure
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Context-specific independence in Bayesian networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Online Rule Learning via Weighted Model Counting
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Set-structured and cost-sharing heuristics for classical planning
Annals of Mathematics and Artificial Intelligence
Fast forward planning by guided enforced hill climbing
Engineering Applications of Artificial Intelligence
Planning with noisy probabilistic relational rules
Journal of Artificial Intelligence Research
An algorithm to find optimal attack paths in nondeterministic scenarios
Proceedings of the 4th ACM workshop on Security and artificial intelligence
A translation based approach to probabilistic conformant planning
ADT'11 Proceedings of the Second international conference on Algorithmic decision theory
Stochastic enforced hill-climbing
Journal of Artificial Intelligence Research
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We present a new algorithm for probabilistic planning with no observability. Our algorithm, called Probabilistic-FF, extends the heuristic forward-searchmachinery of Conformant-FF to problems with probabilistic uncertainty about both the initial state and action effects. Specifically, Probabilistic-FF combines Conformant-FF's techniques with a powerful machinery for weighted model counting in (weighted) CNFs, serving to elegantly define both the search space and the heuristic function. Our evaluation of Probabilistic-FF shows its fine scalability in a range of probabilistic domains, constituting a several orders of magnitude improvement over previous results in this area. We use a problematic case to point out the main open issue to be addressed by further research.