Least-cost flaw repair: a plan refinement strategy for partial-order planning
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Plan-Refinement Strategies and Search-Space Size
ECP '97 Proceedings of the 4th European Conference on Planning: Recent Advances in AI Planning
Accelerating Partial Order Planners by Improving Plan and Goal Choices
TAI '95 Proceedings of the Seventh International Conference on Tools with Artificial Intelligence
A unifying framework for hybrid planning and scheduling
KI'06 Proceedings of the 29th annual German conference on Artificial intelligence
Hybrid planning using flexible strategies
KI'05 Proceedings of the 28th annual German conference on Advances in Artificial Intelligence
Plan Repair in Hybrid Planning
KI '08 Proceedings of the 31st annual German conference on Advances in Artificial Intelligence
Landmarks in Hierarchical Planning
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Advanced user assistance based on AI planning
Cognitive Systems Research
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This paper describes a system for the systematic construction and evaluation of planning strategies. It is based on a proper formal account of refinement planning and allows to decouple plan-deficiency detection, refinement computation, and search control. In adopting this methodology, planning strategies can be explicitly described and easily deployed in various system configurations.We introduce novel domain-independent planning strategies that are applicable to a wide range of planning capabilities and methods. These so-called HotSpotstrategies are guided by information about current plan defects and solution options. The results of a first empirical performance evaluation are presented in the context of hybrid planning.