Annals of Operations Research - Special issue on Tabu search
Modern heuristic techniques for combinatorial problems
Noise strategies for improving local search
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Fast planning through planning graph analysis
Artificial Intelligence
Fast planning through greedy action graphs
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Artificial Intelligence - Special issue on heuristic search in artificial intelligence
A Discrete Lagrangian-Based Global-SearchMethod for Solving Satisfiability Problems
Journal of Global Optimization
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
The Theory of Discrete Lagrange Multipliers for Nonlinear Discrete Optimization
CP '99 Proceedings of the 5th International Conference on Principles and Practice of Constraint Programming
Extending Planning Graphs to an ADL Subset
ECP '97 Proceedings of the 4th European Conference on Planning: Recent Advances in AI Planning
On Plan Adaption through Planning Graph Analysis
AI*IA '99 Proceedings of the 6th Congress of the Italian Association for Artificial Intelligence on Advances in Artificial Intelligence
Pushing the envelope: planning, propositional logic, and stochastic search
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
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GPG is a planner based on planning graphs that combines local search and backtracking techniques for solving both plan-generation and plan-adaptation tasks. The space of the local search is formed by particular subgraphs of a planning graph representing partial plans. The operators for moving from one search state to the next one are graph modification operations corresponding to adding (deleting) actions to (from) a partial plan. GPG can use different types of heuristics based on a parametrized cost function, where the parameters weight different types of constraint violation that are present in the current subgraph. A drawback of this method is that the performance is sensitive to the static values assigned to these parameters.In this paper we propose a refined version of the local search heuristics of GPG using a cost function with dynamic parameters. In particular, the cost of the constraint violations are dynamically evaluated using Lagrange multipliers. As the experimental results show, the use of these multipliers gives two important improvements to our local search. First, the revised cost function is more informative and can discriminate more accurately the elements in the neighborhood. As a consequence, the new cost function can give better performances. Secondly, the performance of the search does not depend anymore on the values of the parameters that in the previous version need to be tuned by hand before the search.