ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
DDL.1: a formal description of a constraint representation language for physical domains
New directions in AI planning
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
Planning as constraint satisfaction: solving the planning graph by compiling it into CSP
Artificial Intelligence
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Constraint-Based Scheduling
Visopt ShopFloor: On the Edge of Planning and Scheduling
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Constraint-Based Attribute and Interval Planning
Constraints
Automated Planning: Theory & Practice
Automated Planning: Theory & Practice
IEEE Intelligent 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
An algebraic graphical model for decision with uncertainties, feasibilities, and utilities
Journal of Artificial Intelligence Research
Valued constraint satisfaction problems: hard and easy problems
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Branching and pruning: An optimal temporal POCL planner based on constraint programming
Artificial Intelligence
Combining static and dynamic models for boosting forward planning
CPAIOR'12 Proceedings of the 9th international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
A resource enhanced HTN planning approach for emergency decision-making
Applied Intelligence
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The CNT framework (Constraint Network on Timelines) has been designed to model discrete event dynamic systems and the properties one knows, one wants to verify, or one wants to enforce on them. In this article, after a reminder about the CNT framework, we show its modeling power and its ability to support various modeling styles, coming from the planning, scheduling, and constraint programming communities. We do that by producing and comparing various models of two mission management problems in the aerospace domain: management of a team of unmanned air vehicles and of an Earth observing satellite.