The History Heuristic and Alpha-Beta Search Enhancements in Practice
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automated Planning: Theory & Practice
Automated Planning: Theory & Practice
Heuristics for Planning with Action Costs Revisited
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
The FF planning system: fast plan generation through heuristic search
Journal of Artificial Intelligence Research
The metric-FF planning system: translating "Ignoring delete lists" to numeric state variables
Journal of Artificial Intelligence Research
Macro-FF: improving AI planning with automatically learned macro-operators
Journal of Artificial Intelligence Research
Marvin: a heuristic search planner with online macro-action learning
Journal of Artificial Intelligence Research
Probabilistic planning via heuristic forward search and weighted model counting
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
Conformant planning via heuristic forward search: A new approach
Artificial Intelligence
Sampling strategies and variable selection in weighted degree heuristics
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
Planning by guided hill-climbing
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
Automated service composition using heuristic search
BPM'06 Proceedings of the 4th international conference on Business Process Management
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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.