An algorithm for probabilistic planning
Artificial Intelligence - Special volume on planning and scheduling
Fast planning through planning graph analysis
Artificial Intelligence
Formalizing sensing actions—a transition function based approach
Artificial Intelligence
Contingency Selection in Plan Generation
ECP '97 Proceedings of the 4th European Conference on Planning: Recent Advances in AI Planning
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
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 Approach to Qualitative and Quantitative Reasoning
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
The computational complexity of probabilistic planning
Journal of Artificial Intelligence Research
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
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
SUM'11 Proceedings of the 5th international conference on Scalable uncertainty management
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Effective planning in uncertain environment is important to agents and multi-agents systems. In this paper, we introduce a new logic based approach to probabilistic contingent planning (probabilistic planning with imperfect sensing actions), by relating probabilistic contingent planning to normal hybrid probabilistic logic programs with probabilistic answer set semantics [24]. We show that any probabilistic contingent planning problem can be encoded as a normal hybrid probabilistic logic program. We formally prove the correctness of our approach. Moreover, we show that the complexity of finding a probabilistic contingent plan in our approach is NP-complete. In addition, we show that any probabilistic contingent planning problem, $\cal PP$, can be encoded as a classical normal logic program with answer set semantics, whose answer sets corresponds to valid trajectories in $\cal PP$. We show that probabilistic contingent planning problems can be encoded as SAT problems. We present a new high level probabilistic action description language that allows the representation of sensing actions with probabilistic outcomes.