ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
An algorithm for probabilistic planning
Artificial Intelligence - Special volume on planning and scheduling
Fast planning through planning graph analysis
Artificial Intelligence
Planning and acting in partially observable stochastic domains
Artificial Intelligence
Formalizing sensing actions—a transition function based approach
Artificial Intelligence
Probabilistic Planning in the Graphplan Framework
ECP '99 Proceedings of the 5th European Conference on Planning: Recent Advances in AI Planning
Reasoning about actions in a probabilistic setting
Eighteenth national conference on Artificial intelligence
SAT-based planning in complex domains: concurrency, constraints and nondeterminism
Artificial Intelligence - special issue on planning with uncertainty and incomplete information
Contingent planning under uncertainty via stochastic satisfiability
Artificial Intelligence - special issue on planning with uncertainty and incomplete information
ASSAT: computing answer sets of a logic program by SAT solvers
Artificial Intelligence - Special issue on nonmonotonic reasoning
Answer Set Programming Based on Propositional Satisfiability
Journal of Automated Reasoning
A new approach to hybrid probabilistic logic programs
Annals of Mathematics and Artificial Intelligence
Theory and Practice of Logic Programming
Probabilistic Planning in Hybrid Probabilistic Logic Programs
SUM '07 Proceedings of the 1st international conference on Scalable Uncertainty Management
A Logical Framework to Reinforcement Learning Using Hybrid Probabilistic Logic Programs
SUM '08 Proceedings of the 2nd international conference on Scalable Uncertainty Management
On the Relationship between Hybrid Probabilistic Logic Programs and Stochastic Satisfiability
SUM '08 Proceedings of the 2nd international conference on Scalable Uncertainty Management
A Logical Approach to Qualitative and Quantitative Reasoning
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Automatic SAT-compilation of planning problems
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
The computational complexity of probabilistic planning
Journal of Artificial Intelligence Research
Towards the computation of stable probabilistic model semantics
KI'06 Proceedings of the 29th annual German conference on Artificial intelligence
Pushing the envelope: planning, propositional logic, and stochastic search
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Incomplete knowledge in hybrid probabilistic logic programs
JELIA'06 Proceedings of the 10th European conference on Logics in Artificial Intelligence
Probabilistic reasoning about actions in nonmonotonic causal theories
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Towards a more practical hybrid probabilistic logic programming framework
PADL'05 Proceedings of the 7th international conference on Practical Aspects of Declarative Languages
ECSQARU'11 Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty
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In a series of papers we have shown that fundamental probabilistic reasoning problems can be encoded as hybrid probabilistic logic programs with probabilistic answer set semantics described in [24]. These probabilistic reasoning problems include, but not limited to, probabilistic planning [28], probabilistic planning with imperfect sensing actions [29], reinforcement learning [30], and Bayes reasoning [25]. Moreover, in [31] we also proved that stochastic satisfiability (SSAT) can be modularly encoded as hybrid probabilistic logic program with probabilistic answer set semantics, therefore, the applicability of SSAT to variety of fundamental probabilistic reasoning problems also carry over to hybrid probabilistic logic programs with probabilistic answer set semantics. The hybrid probabilistic logic programs encoding of these probabilistic reasoning problems is related to and can be translated into SAT, hence, state-of-the-art SAT solver can be used to solve these problems. This paper establishes the foundation of using SAT solvers for reasoning about variety of fundamental probabilistic reasoning problems. In this paper, we show that fundamental probabilistic reasoning problems that include probabilistic planning, probabilistic contingent planning, reinforcement learning, and Bayesian reasoning can be directly encoded as SAT formulae, hence state-of-the-art SAT solver can be used to solve these problems efficiently. We emphasize on SAT encoding for probabilistic planning and probabilistic contingent planning, as similar encoding carry over to reinforcement learning and Bayesian reasoning.