LAO: a heuristic search algorithm that finds solutions with loops
Artificial Intelligence - Special issue on heuristic search in artificial intelligence
Parametric shape analysis via 3-valued logic
ACM Transactions on Programming Languages and Systems (TOPLAS)
Weak, strong, and strong cyclic planning via symbolic model checking
Artificial Intelligence - special issue on planning with uncertainty and incomplete information
Hierarchical planning in BDI agent programming languages: a formal approach
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
A model of contingent planning for agent programming languages
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
Learning generalized plans using abstract counting
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
The FF planning system: fast plan generation through heuristic search
Journal of Artificial Intelligence Research
Planning graph heuristics for belief space search
Journal of Artificial Intelligence Research
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
A new representation and associated algorithms for generalized planning
Artificial Intelligence
Applicability conditions for plans with loops: Computability results and algorithms
Artificial Intelligence
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We present a new approach for finding generalized contingent plans with loops and branches in situations where there is uncertainty in state properties and object quantities, but lack of probabilistic information about these uncertainties. We use a state abstraction technique from static analysis of programs, which uses 3-valued logic to compactly represent belief states with unbounded numbers of objects. Our approach for finding plans is to incrementally generalize and merge input example plans which can be generated by classical planners. The expressiveness and scope of this approach are demonstrated using experimental results on common benchmark domains.