ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
Fast planning through planning graph analysis
Artificial Intelligence
CPlan: a constraint programming approach to planning
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
Knowlege in action: logical foundations for specifying and implementing dynamical systems
Knowlege in action: logical foundations for specifying and implementing dynamical systems
Planning as constraint satisfaction: solving the planning graph by compiling it into CSP
Artificial Intelligence
An Open-Ended Finite Domain Constraint Solver
PLILP '97 Proceedings of the9th International Symposium on Programming Languages: Implementations, Logics, and Programs: Including a Special Trach on Declarative Programming Languages in Education
Constraint Processing
Automated Planning: Theory & Practice
Automated Planning: Theory & Practice
Filtering Algorithms for the NValue Constraint
Constraints
The fast downward planning system
Journal of Artificial Intelligence Research
Generalizing GraphPlan by formulating planning as a CSP
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Branching and pruning: An optimal temporal POCL planner based on constraint programming
Artificial Intelligence
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Planning problems deal with finding a sequence of actions that transfer the initial state of the world into a desired state. Frequently such problems are solved by dedicated algorithms but there exist planners based on translating the planning problem into a different formalism such as constraint satisfaction or Boolean satisfiability and using a general solver for this formalism. The paper describes how to enhance existing constraintmodels of sequential planning problems by using techniques such as symmetry breaking (dominance rules), singleton consistency, nogoods, lifting, or techniques motivated by the partial-order planning.