In many planning situations, a planner is required to return a diverse set of plans satisfying the same goals which will be used by the external systems collectively. We take a domain-independent approach to solving this problem. We propose different domain independent distance functions among plans that can provide meaningful insights about the diversity in the plan set. We then describe how two representative state-of-the-art domain independent planning approaches -- one based on compilation to CSP, and the other based on heuristic local search -- can be adapted to produce diverse plans. We present empirical evidence demonstrating the effectiveness of our approaches.

Authors

• Biplav Srivastava AI Institute, University of South Carolina
• Subbarao Kambhampati Arizona State University
• Tuan A. Nguyen
• Minh Binh Do
• Alfonso Gerevini University of Brescia
• Ivan Serina Paolo Toninelli

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