Handbook of theoretical computer science (vol. B)
Planning control rules for reactive agents
Artificial Intelligence
Using temporal logics to express search control knowledge for planning
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
Model-based programming of fault-aware systems
AI Magazine
Forward-chaining planning in nondeterministic domains
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Constructing conditional plans by a theorem-prover
Journal of Artificial Intelligence Research
Towards an integration of Golog and planning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Planning as model checking for extended goals in non-deterministic domains
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Automated composition of web services by planning at the knowledge level
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Automated composition of Web services via planning in asynchronous domains
Artificial Intelligence
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While in most planning approaches goals and plans are different objects, it is often useful to specify goals that combine declarative conditions with procedural plans. In this paper, we propose a novel language for expressing temporally extended goals for planning in nondeterministic domains. The key feature of this language is that it allows for an arbitrary combination of declarative goals expressed in temporal logic and procedural goals expressed as plan fragments. We provide a formal definition of the language and its semantics, and we propose an approach to planning with this language in nondeterministic domains. We implement the planning framework and perform a set of experimental evaluations that show the potentialities of our approach.