An algorithm for probabilistic planning
Artificial Intelligence - Special volume on planning and scheduling
Complexity of finite-horizon Markov decision process problems
Journal of the ACM (JACM)
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Reasoning about actions in a probabilistic setting
Eighteenth national conference on Artificial intelligence
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
Domain-dependent knowledge in answer set planning
ACM Transactions on Computational Logic (TOCL)
A new approach to hybrid probabilistic logic programs
Annals of Mathematics and Artificial Intelligence
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
Probabilistic Planning with Imperfect Sensing Actions Using Hybrid Probabilistic Logic Programs
SUM '09 Proceedings of the 3rd International Conference on Scalable Uncertainty Management
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Symbolic dynamic programming for first-order MDPs
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Planning and acting in partially observable stochastic domains
Artificial Intelligence
Pushing the envelope: planning, propositional logic, and stochastic search
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
The frame problem and knowledge-producing actions
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
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
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We present a probabilistic logic programming framework to reinforcement learning, by integrating reinforcement learning, in POMDP environments, with normal hybrid probabilistic logic programs with probabilistic answer set semantics, that is capable of representing domain-specific knowledge. We formally prove the correctness of our approach. We show that the complexity of finding a policy for a reinforcement learning problem in our approach is NP-complete. In addition, we show that any reinforcement learning problem can be encoded as a classical logic program with answer set semantics. We also show that a reinforcement learning problem can be encoded as a SAT problem. We present a new high level action description language that allows the factored representation of POMDP. Moreover, we modify the original model of POMDP so that it be able to distinguish between knowledge producing actions and actions that change the environment.