Generating Optimal Plans in Highly-Dynamic Domains
This addresses the challenge of robust planning for AI systems in unpredictable domains, though it appears incremental as it builds on existing plan adaptation techniques.
The paper tackles the problem of generating optimal plans in highly dynamic environments where unexpected state changes can invalidate planning efforts, and shows empirically that their approach can converge and find optimal plans in such challenging settings.
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.