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In this paper we explore the challenges surrounding searching effectively in problems with preferences. These problems are characterized by a relative abundance of goal states: at one extreme, if every goal is soft, every state is a goal state. We present techniques for planning in such search spaces, managing the sometimes-conflicting aims of intensifying search around states on the open list that are heuristically close to new, better goal states; and ensuring search is sufficiently diverse to find new low-cost areas of the search space, avoiding local minima. Our approach uses a novel cost-bound-sensitive heuristic, based on finding several heuristic distance-to-go estimates in each state, each satisfying a different subset of preferences. We present results comparing our new techniques to the current state-of-the-art and demonstrating their effectiveness on a wide range of problems from recent International Planning Competitions.
|Title of host publication
|Proceedings of the Twenty Third International Conference on Automated Planning and Scheduling (ICAPS 2013)
|Daniel Borrajo, Subbarao Kambhampati, Angela Oddi, Simone Fratini
|Number of pages
|Published - Aug 2013
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- 1 Finished
1/11/2011 → 30/04/2013