The computational complexity of propositional STRIPS planning
Artificial Intelligence
Fast planning through planning graph analysis
Artificial Intelligence
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
A Computing Procedure for Quantification Theory
Journal of the ACM (JACM)
Artificial Intelligence
A machine program for theorem-proving
Communications of the ACM
Artificial Intelligence - Special issue on heuristic search in artificial intelligence
Polynomial-Length Planning Spans the Polynomial Hierarchy
JELIA '02 Proceedings of the European Conference on Logics in Artificial Intelligence
Planning as Satisfiability in Nondeterministic Domains
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Combining the Expressivity of UCPOP with the Efficiency of Graphplan
ECP '97 Proceedings of the 4th European Conference on Planning: Recent Advances in AI Planning
A logic programming approach to knowledge-state planning, II: the DLVk system
Artificial Intelligence
Conformant planning via symbolic model checking and heuristic search
Artificial Intelligence
Conformant planning via symbolic model checking
Journal of Artificial Intelligence Research
The FF planning system: fast plan generation through heuristic search
Journal of Artificial Intelligence Research
Constructing conditional plans by a theorem-prover
Journal of Artificial Intelligence Research
Heuristic search + symbolic model checking = efficient conformant planning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
A simplifier for propositional formulas with many binary clauses
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Continual planning and acting in dynamic multiagent environments
Autonomous Agents and Multi-Agent Systems
Conformant plans and beyond: Principles and complexity
Artificial Intelligence
Set-structured and cost-sharing heuristics for classical planning
Annals of Mathematics and Artificial Intelligence
A semantically enabled service oriented architecture
WImBI'06 Proceedings of the 1st WICI international conference on Web intelligence meets brain informatics
Deterministic POMDPs revisited
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Fast forward planning by guided enforced hill climbing
Engineering Applications of Artificial Intelligence
Finding first-order minimal unsatisfiable cores with a heuristic depth-first-search algorithm
IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
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
Replanning in domains with partial information and sensing actions
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Replanning in domains with partial information and sensing actions
Journal of Artificial Intelligence Research
International Journal of Approximate Reasoning
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Conformant planning is the task of generating plans given uncertainty about the initial state and action effects, and without any sensing capabilities during plan execution. The plan should be successful regardless of which particular initial world we start from. It is well known that conformant planning can be transformed into a search problem in belief space, the space whose elements are sets of possible worlds. We introduce a new representation of that search space, replacing the need to store sets of possible worlds with a need to reason about the effects of action sequences. The reasoning is done by implication tests on propositional formulas in conjunctive normal form (CNF) that capture the action sequence semantics. Based on this approach, we extend the classical heuristic forward-search planning system FF to the conformant setting. The key to this extension is an appropriate extension of the relaxation that underlies FF's heuristic function, and of FF's machinery for solving relaxed planning problems: the extended machinery includes a stronger form of the CNF implication tests that we use to reason about the effects of action sequences. Our experimental evaluation shows the resulting planning system to be superior to the state-of-the-art conformant planners MBP, KACMBP, and GPT in a variety of benchmark domains.