Permissive planning: extending classical planning to uncertain task domains

摘要

Uncertainty, inherent in most real-world domains, can cause failure of apparently sound classical plans. On the other hand, reasoning with representations that explicitly reflect uncertainty can engender significant, even prohibitive, additional computational costs. This paper contributes a novel approach to planning in uncertain domains. The approach is an extension of classical planning. Machine learning is employed to adjust planner bias in response to execution failures. Thus, the classical planner is conditioned towards producing plans that tend to work when executed in the world.