Practical planning: extending the classical AI planning paradigm
Practical planning: extending the classical AI planning paradigm
Automatically abstracting the effects of operators
Proceedings of the first international conference on Artificial intelligence planning systems
A critical look at Koblock's hierarchy mechanism
Proceedings of the first international conference on Artificial intelligence planning systems
Automatically generating abstractions for planning
Artificial Intelligence
STRPLAN: A Distributed Planner for Object-Centred Application Domains
Applied Intelligence
Partitioning of Temporal Planning Problems in Mixed Space Using the Theory of Extended Saddle Points
ICTAI '03 Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence
AI Communications
Journal of Artificial Intelligence Research
SHOP: simple hierarchical ordered planner
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Conditional progressive planning under uncertainty
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Scope and abstraction: two criteria for localized planning
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Learning abstraction hierarchies for problem solving
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 2
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Intelligent agents acting in real world environments need to synthesize their course of action based on multiple sources of knowledge. They also need to generate plans that smoothly integrate actions from different domains. In this paper we present a generic approach to synthesize plans for solving planning problems involving multiple domains. The proposed approach performs search hierarchically by starting planning in one domain and considering subgoals related to the other domains as abstract tasks to be planned for later when their respective domains are considered. To plan in each domain, a domain-dependent planner can be used, making it possible to integrate different planners, possibly with different specializations. We outline the algorithm, and the assumptions underlying its functionality. We also demonstrate through a detailed example, how the proposed framework compares to planning in one global domain.