ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
Fast planning through planning graph analysis
Artificial Intelligence
Planning as constraint satisfaction: solving the planning graph by compiling it into CSP
Artificial Intelligence
Branching and pruning: an optimal temporal POCL planner based on constraint programming
Artificial Intelligence
Concise finite-domain representations for PDDL planning tasks
Artificial Intelligence
Revisiting Constraint Models for Planning Problems
ISMIS '09 Proceedings of the 18th International Symposium on Foundations of Intelligent Systems
Data structures for generalised arc consistency for extensional constraints
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
PDDL2.1: an extension to PDDL for expressing temporal planning domains
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
Macro-FF: improving AI planning with automatically learned macro-operators
Journal of Artificial Intelligence Research
The fast downward planning system
Journal of Artificial Intelligence Research
The role of macros in tractable planning over causal graphs
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Generalizing GraphPlan by formulating planning as a CSP
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Optimal symbolic planning with action costs and preferences
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
A meta-CSP model for optimal planning
SARA'07 Proceedings of the 7th International conference on Abstraction, reformulation, and approximation
A search-infer-and-relax framework for integrating solution methods
CPAIOR'05 Proceedings of the Second international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
Strategic cognitive sequencing: a computational cognitive neuroscience approach
Computational Intelligence and Neuroscience - Special issue on Neurocognitive Models of Sense Making
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Constraint satisfaction techniques provide powerful inference algorithms that can prune choices during search. Constraint-based approaches provide a useful complement to heuristic search optimal planners. We develop a constraint-based model for cost-optimal planning that uses global constraints to improve the inference in planning. The key novelty in our approach is in a transformation of the SAS+ input that adds a form of macro-action to fully connect chains of composable operators. This translation leads to the development of a natural dominance constraint on the new problem which we add to our constraint model. We provide empirical results to show that our planner, Constance, solves more instances than the current best constraint-based planners. We also demonstrate the power of our new dominance constraints in this representation.