Integer and combinatorial optimization
Integer and combinatorial optimization
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
Theoretical Computer Science
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
Using temporal logics to express search control knowledge for planning
Artificial Intelligence
Model checking
Constraint-Based Scheduling
Planning for temporally extended goals
Annals of Mathematics and Artificial Intelligence
TALplanner: A temporal logic based forward chaining planner
Annals of Mathematics and Artificial Intelligence
Integer Programs and Valid Inequalities for Planning Problems
ECP '99 Proceedings of the 5th European Conference on Planning: Recent Advances in AI Planning
Constraint-Based Attribute and Interval Planning
Constraints
Integer optimization models of AI planning problems
The Knowledge Engineering Review
IEEE Intelligent Systems
Introduction to Discrete Event Systems
Introduction to Discrete Event Systems
Handbook of Constraint Programming (Foundations of Artificial Intelligence)
Handbook of Constraint Programming (Foundations of Artificial Intelligence)
PDDL2.1: an extension to PDDL for expressing temporal planning domains
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
Modelling mixed discrete-continuous domains for planning
Journal of Artificial Intelligence Research
On the use of integer programming models in AI planning
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
Planning with sharable resource constraints
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Branching and pruning: An optimal temporal POCL planner based on constraint programming
Artificial Intelligence
The Knowledge Engineering Review
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This paper presents an example of cooperation between AI planning techniques and Constraint Programming or Operations Research. More precisely, it presents a way of boosting forward planning using combinatorial optimization techniques. The idea consists in combining on one hand a dynamic model that represents the Markovian dynamics of the system considered (i.e. state transitions), and on the other hand a static model that describes the global properties that are required over state trajectories. The dynamic part is represented by so-called constraint-based timed automata , whereas the static part is represented by so-called constraint-based observers . The latter are modeled using standard combinatorial optimization frameworks, such as linear programming, constraint programming, scheduling, or boolean satisfiability. They can be called at any step of the forward search to cut it via inconsistency detection. Experiments show significant improvements on some benchmarks of the International Planning Competition.