Domain independent approaches for finding diverse plans

Authors:
Biplav Srivastava;Tuan A. Nguyen;Alfonso Gerevini;Subbarao Kambhampati;Minh Binh Do;Ivan Serina
Affiliations:
IBM India Research Laboratory, New Delhi, India and IBM India Research Laboratory, Bangalore, India;University of Natural Sciences, Ho Chi Minh, Vietnam;University of Brescia, Italy;Arizona State University, Tempe, AZ;Palo Alto Research Center;University of Brescia, Italy
Venue:
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Year:
2007

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Abstract

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.