Plan reuse versus plan generation: a theoretical and empirical analysis
Artificial Intelligence - Special volume on planning and scheduling
Knowlege in action: logical foundations for specifying and implementing dynamical systems
Knowlege in action: logical foundations for specifying and implementing dynamical systems
FLUCAP: a heuristic search planner for first-order MDPs
Journal of Artificial Intelligence Research
A domain-independent algorithm for plan adaptation
Journal of Artificial Intelligence Research
The fringe-saving A* search algorithm: a feasibility study
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Symbolic dynamic programming for first-order MDPs
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Monitoring the generation and execution of optimal plans
Monitoring the generation and execution of optimal plans
Integrated task and motion planning in belief space
International Journal of Robotics Research
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Generating optimal plans in highly dynamic environments is challenging. Plans are predicated on an assumed initial state, but this state can change unexpectedly during plan generation, potentially invalidating the planning effort. In this paper we make three contributions: (1) We propose a novel algorithm for generating optimal plans in settings where frequent, unexpected events interfere with planning. It is able to quickly distinguish relevant from irrelevant state changes, and to update the existing planning search tree if necessary. (2) We argue for a new criterion for evaluating plan adaptation techniques: the relative running time compared to the "size" of changes. This is significant since during recovery more changes may occur that need to be recovered from subsequently, and in order for this process of repeated recovery to terminate, recovery time has to converge. (3) We show empirically that our approach can converge and find optimal plans in environments that would ordinarily defy planning due to their high dynamics.