ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
Complexity and expressive power of logic programming
ACM Computing Surveys (CSUR)
Extending and implementing the stable model semantics
Artificial Intelligence
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
The DLV system for knowledge representation and reasoning
ACM Transactions on Computational Logic (TOCL)
Maintenance goals of agents in a dynamic environment: Formulation and policy construction
Artificial Intelligence
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We show that a Horn SAT and logic programming approach to obtain polynomial time algorithms for problem solving can be fruitfully applied to finding plans for various kinds of goals in a non-deterministic domain. We particularly focus on finding weak, strong, and strong cyclic plans for planning problems, as they are the most studied ones in the literature. We describe new algorithms for these problems and show how non-monotonic logic programming can be used to declaratively compute strong cyclic plans. As a further benefit, preferred plans among alternative candidate plans may be singled out this way. We give complexity results for weak. strong, and strong cyclic planning. Finally, we briefly discuss some of the kinds of goals in non-deterministic domains for which the approach in the paper can be used.