Fast forward planning by guided enforced hill climbing |
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| Authors: | S.A. Akramifar G. Ghassem-Sani |
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| Affiliation: | 1. School of Ocean Sciences, Bangor University, Menai Bridge, Wales United Kingdom;2. Department of Earth and Environmental Sciences, University of Michigan, Ann Arbor, MI, USA;3. Institute for Geophysics, University of Texas at Austin, Austin, TX, USA;4. National Environmental Satellite, Data, and Information Service, National Oceanic and Atmospheric Administration, Silver Spring, MD, USA;5. LEGOS, University of Toulouse, CNES, CNRS, IRD, UPS, Toulouse, France;6. Mercator-Océan, Ramonville St-Agne, France;7. Oceanography Division, Naval Research Laboratory, Stennis Space Center, MS, USA |
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| Abstract: | In recent years, a number of new heuristic search methods have been developed in the field of automated planning. Enforced hill climbing (EHC) is one such method which has been frequently used in a number of AI planning systems. Despite certain weaknesses, such as getting trapped in dead-ends in some domains, this method is more competitive than several other methods in many planning domains. In order to enhance the efficiency of ordinary enforced hill climbing, a new form of enforced hill climbing, called guided enforced hill climbing, is introduced in this paper. An adaptive branch ordering function is the main feature that guided enforced hill climbing has added to EHC. Guided enforced hill climbing expands successor states in the order recommended by the ordering function. Our experimental results in several planning domains show a significant improvement in the efficiency of the enforced hill climbing method, especially when applied to larger problems. |
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