Minimizing conflicts: a heuristic repair method for constraint satisfaction and scheduling problems
Artificial Intelligence - Special volume on constraint-based reasoning
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)
Extending Planning Graphs to an ADL Subset
ECP '97 Proceedings of the 4th European Conference on Planning: Recent Advances in AI Planning
Fast Planning through Greedy Action Graphs
Fast Planning through Greedy Action Graphs
Fast planning through planning graph analysis
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Pushing the envelope: planning, propositional logic, and stochastic search
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Planning by rewriting: efficiently generating high-quality plans
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Incremental Local Search for Planning Problems
ECAI '00 Proceedings of the Workshop on Local Search for Planning and Scheduling-Revised Papers
Beyond the Plan-Length Criterion
ECAI '00 Proceedings of the Workshop on Local Search for Planning and Scheduling-Revised Papers
Automatic Case Base Management in a Multi-modal Reasoning System
EWCBR '00 Proceedings of the 5th European Workshop on Advances in Case-Based Reasoning
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
Lagrange Multipliers for Local Search on Planning Graphs
ECAI '00 Proceedings of the Workshop on Local Search for Planning and Scheduling-Revised Papers
An approach to efficient planning with numerical fluents and multi-criteria plan quality
Artificial Intelligence
Planning graph as a (dynamic) CSP: exploiting EBL, DDB and other CSP search techniques in Graphplan
Journal of Artificial Intelligence Research
Taming numbers and durations in the model checking integrated planning system
Journal of Artificial Intelligence Research
Planning through stochastic local search and temporal action graphs in LPG
Journal of Artificial Intelligence Research
An approach to temporal planning and scheduling in domains with predictable exogenous events
Journal of Artificial Intelligence Research
Constraint-based agents: an architecture for constraint-based modeling and local-search-based reasoning for planning and scheduling in open and dynamic worlds
Efficient Plan Adaptation through Replanning Windows and Heuristic Goals
Fundamenta Informaticae - RCRA 2008 Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion
Planning in domains with derived predicates through rule-action graphs and local search
Annals of Mathematics and Artificial Intelligence
An Empirical Analysis of Some Heuristic Features for Planning through Local Search and Action Graphs
Fundamenta Informaticae - RCRA 2009 Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion
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Domain-independent planning is a notoriously hard search problem. Several systematic search techniques have been proposed in the context of various formalisms. However, despite their theoretical completeness, in practice these algorithms are incomplete because for many problems the search space is too large to be (even partially) explored.In this paper we propose a new search method in the context of Blum and Furst's planning graph approach, which is based on local search. Local search techniques are incomplete, but in practice they can efficiently solve problems that are unsolvable for current systematic search methods. We introduce three heuristics to guide the local search (Walkplan, Tabuplan and T-Walkplan), and we propose two methods for combining local and systematic search.Our techniques are implemented in a system called GPG, which can be used for both plan-generation and plan-adaptation tasks. Experimental results show that GPG can efficiently solve problems that are very hard for current planners based on planning graphs.