Researchers have developed a new policy called Fisher-SEP to help planners decide when to supplement simulators with real-world experiments. The policy decomposes the simulator's value error into identifiable calibration shifts and unresolvable parametric residuals. It also distinguishes between local and reachability components of the value gap between simulator-optimal and true optimal policies. Two case studies demonstrate Fisher-SEP's effectiveness in optimizing experimental strategies for supply chains and public health interventions. AI
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IMPACT Provides a framework for improving the reliability of AI planning by integrating simulation with real-world data collection.
RANK_REASON Academic paper detailing a new methodology for AI planning. [lever_c_demoted from research: ic=1 ai=1.0]