Artificial Intelligence - Special issue on knowledge representation
Temporal planning with continuous change
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
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
Artificial Intelligence - Special issue on heuristic search in artificial intelligence
Planning as constraint satisfaction: solving the planning graph by compiling it into CSP
Artificial Intelligence
Temporal Planning with Mutual Exclusion Reasoning
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Planning as Heuristic Search: New Results
ECP '99 Proceedings of the 5th European Conference on Planning: Recent Advances in AI Planning
Automated Planning: Theory & Practice
Automated Planning: Theory & Practice
Bridging the gap between planning and scheduling
The Knowledge Engineering Review
Branching and pruning: an optimal temporal POCL planner based on constraint programming
Artificial Intelligence
Planning and scheduling in an e-learning environment. A constraint-programming-based approach
Engineering Applications of Artificial Intelligence
Branching and pruning: an optimal temporal POCL planner based on constraint programming
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
The FF planning system: fast plan generation through heuristic search
Journal of Artificial Intelligence Research
PDDL2.1: an extension to PDDL for expressing temporal planning domains
Journal of Artificial Intelligence Research
VHPOP: versatile heuristic partial order planner
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
Temporal planning using subgoal partitioning and resolution in SGPlan
Journal of Artificial Intelligence Research
Modelling mixed discrete-continuous domains for planning
Journal of Artificial Intelligence Research
On the application of planning and scheduling techniques to e-learning
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part I
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Planning research is recently concerned with the resolution of more realistic problems as evidenced in the many works and new extensions to the Planning Domain Definition Language (PDDL) to better approximate real problems. Researchers’ works to push planning algorithms and capture more complex domains share an essential ingredient, namely the incorporation of new types of constraints. Adding constraints seems to be the way of approximating real problems: these constraints represent the duration of tasks, temporal and resource constraints, deadlines, soft constraints, etc., i.e. features that have been traditionally associated to the area of scheduling. This desired expressiveness can be achieved by augmenting the planning reasoning capabilities, at the cost of slightly deviating the planning process from its traditional implicit purpose, that is finding the causal structure of the plan. However, the resolution of complex domains with a great variety of different constraints may involve as much planning effort as scheduling effort (and perhaps the latter being more prominent in many problems). For this reason, in this paper we present a general approach to model those problems under a constraint programming formulation which allows us to represent and handle a wide range of constraints. Our work is based on the original model of $\mathsf{CPT}$ , an optimal temporal planner, and it extends the $\mathsf{CPT}$ ’s formulation to deal with more expressive constraints. We will show that our general formulation can be used for planning and/or scheduling, from scheduling a given complete plan to generating the whole plan from scratch. However, our contribution is not a new planner but a constraint programming formulation for representing highly-constrained planning + scheduling problems.