The well-founded semantics for general logic programs
Journal of the ACM (JACM)
Probabilistic logic programming
Information and Computation
Stable semantics for probabilistic deductive databases
Information and Computation
An algorithm for probabilistic planning
Artificial Intelligence - Special volume on planning and scheduling
Fast planning through planning graph analysis
Artificial Intelligence
The independent choice logic for modelling multiple agents under uncertainty
Artificial Intelligence - Special issue on economic principles of multi-agent systems
Proceedings of the 1999 international conference on Logic programming
Declarative problem-solving in DLV
Logic-based artificial intelligence
Knowledge Representation, Reasoning, and Declarative Problem Solving
Knowledge Representation, Reasoning, and Declarative Problem Solving
Probabilistic Planning in the Graphplan Framework
ECP '99 Proceedings of the 5th European Conference on Planning: Recent Advances in AI Planning
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)
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
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
Hybrid probabilistic programs: algorithms and complexity
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Hybrid probabilistic logic programs with non-monotonic negation
ICLP'05 Proceedings of the 21st international conference on Logic Programming
Incomplete knowledge in hybrid probabilistic logic programs
JELIA'06 Proceedings of the 10th European conference on Logics 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
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 Reasoning by SAT Solvers
ECSQARU '09 Proceedings of the 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Probabilistic Planning with Imperfect Sensing Actions Using Hybrid Probabilistic Logic Programs
SUM '09 Proceedings of the 3rd International Conference on Scalable Uncertainty Management
Extended Fuzzy Logic Programs with Fuzzy Answer Set Semantics
SUM '09 Proceedings of the 3rd International Conference on Scalable Uncertainty Management
ECSQARU'11 Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty
SUM'11 Proceedings of the 5th international conference on Scalable uncertainty management
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In this paper, we present a new approach to probabilistic planning based on logic programming, by relating probabilistic planning to hybrid probabilistic logic programs with probabilistic answer set semantics [32]. We show that any probabilistic planning problem, $\cal P$, can be translated into a hybrid probabilistic logic program whose probabilistic answer sets correspond to trajectories in $\cal P$, with associated probabilities. We formally prove the correctness of our approach. Moreover, we show that the complexity of finding a plan for a probabilistic planning problem in our approach is NP-complete. In addition, we show that any probabilistic planning problem, $\cal P$, can be encoded as a classical logic program with answer set semantics, whose answer sets corresponds to valid trajectories in $\cal P$. We also show that probabilistic planning problems can be encoded as proportional satisfiability problems.