Verification of synchronous sequential machines based on symbolic execution
Proceedings of the international workshop on Automatic verification methods for finite state systems
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
Fast planning through planning graph analysis
Artificial Intelligence
Inferring state constraints for domain-independent planning
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Guarded commands, nondeterminacy and formal derivation of programs
Communications of the ACM
An axiomatic basis for computer programming
Communications of the ACM
A Heuristic Approach to the Discovery of Macro-Operators
Machine Learning
Discovering State Constraints in DISCOPLAN: Some New Results
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
On the compilability and expressive power of propositional planning formalisms
Journal of Artificial Intelligence Research
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Heuristics for planning with SAT
CP'10 Proceedings of the 16th international conference on Principles and practice of constraint programming
Planning as satisfiability: Heuristics
Artificial Intelligence
Computing upper bounds on lengths of transition sequences
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Many forms of reasoning about actions and planning can be reduced to regression, the computation of the weakest precondition a state has to satisfy to guarantee the satisfaction of another condition in the successor state. In this work we formalize a general syntactic regression operation for ground PDDL operators, show its correctness, and define a composition operation based on regression. As applications we present a very simple yet powerful algorithm for computing invariants, as well as a generalization of the hn heuristic of Haslum and Geffner to PDDL.