Improving Big Plans

Past research on assessing and improving plans in domainsthat contain uncertainty has focused on analytic techniquesthat are exponentialin the length of the plan. Little workhas beendoneon choosingfrom amongthe manywaysin which a plan can be improved. Wepresent the IMPROVE algorithm which simulatesthe executionof large, probabilistic plans. IMPROVE runs a data miningalgorithm on the execution traces to pinpoint defects in the plan that most often lead to plan failure. Finally, IMPROVE applies qualitative reasoningand plan adaptation algorithms to modifythe plan to correct these defects. Wehave tested IMPROVE on plans containing over 250steps in an evacuation domain, producedby a domain-specific schedulingroutine. In these experiments, the modified plans haveover a 15% higher probability of achieving their goalthan the original plan.

Authors

• Neal Lesh Andrew Garland

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