Handbook of theoretical computer science (vol. B)
STOC '94 Proceedings of the twenty-sixth annual ACM symposium on Theory of computing
Hierarchical task network planning: formalization, analysis, and implementation
Hierarchical task network planning: formalization, analysis, and implementation
Automatic OBDD-based generation of universal plans in non-deterministic domains
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Fast approximate graph partitioning algorithms
SODA '97 Proceedings of the eighth annual ACM-SIAM symposium on Discrete algorithms
Complexity and Approximation: Combinatorial Optimization Problems and Their Approximability Properties
Planning with Sensing for a Mobile Robot
ECP '97 Proceedings of the 4th European Conference on Planning: Recent Advances in AI Planning
Strong Cyclic Planning Revisited
ECP '99 Proceedings of the 5th European Conference on Planning: Recent Advances in AI Planning
Planning with a language for extended goals
Eighteenth national conference on Artificial intelligence
Weak, strong, and strong cyclic planning via symbolic model checking
Artificial Intelligence - special issue on planning with uncertainty and incomplete information
SAT-based planning in complex domains: concurrency, constraints and nondeterminism
Artificial Intelligence - special issue on planning with uncertainty and incomplete information
Heuristic search + symbolic model checking = efficient conformant planning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
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PKS is the framework for planning with incomplete information and sensing recently introduced by Bacchus and Petrick [Proc. KR'98, pp. 432-443]. The fact that PKS generalizes STRIPS to domains with incomplete information and sensing opens up the possibility of proposing it as a reference for comparisons with other formalisms that approach the problem from different perspectives.To this end we first provide a formal semantics for PKS, then analyze and extend it. The formal definition of the extended PKS entails the identification of a number of properties of this planning framework. In particular, we prove that for any finite instance of the PKS planning problem the reachable states are finite; on the basis of this result we propose an improved planning algorithm that is not only sound, as the one proposed by Petrick and Bacchus [Proc. AIPS'02, pp. 212-221], but also complete.We extend PKS to include conditional plans with cycles and introduce the distinction between different classes of solutions: strong, strong cyclic, weak acyclic and weak cyclic. In contrast with current belief, we prove that some weak acyclic solutions are more likely to succeed for a limited execution than some strong cyclic solutions, revealing the lack of a method for judging the quality of different solutions. Finally, we introduce a quality measure for solutions of any class, and a quantitative method for comparing them.