A Constraint-Based Method for Project Scheduling with Time Windows
Journal of Heuristics
Gaining efficiency and flexibility in the simple temporal problem
TIME '96 Proceedings of the 3rd Workshop on Temporal Representation and Reasoning (TIME'96)
Sapa: a multi-objective metric temporal planner
Journal of Artificial Intelligence Research
Planning through stochastic local search and temporal action graphs in LPG
Journal of Artificial Intelligence Research
Flaw selection strategies for partial-order planning
Journal of Artificial Intelligence Research
Learning first-order definitions of functions
Journal of Artificial Intelligence Research
Total-order planning with partially ordered subtasks
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Towards an automatic monitoring for higher education Learning Design
International Journal of Metadata, Semantics and Ontologies
AN AI ELECTRICAL GROUND SUPPORT EQUIPMENT FOR CONTROLLING AND TESTING A SPACE INSTRUMENT
Applied Artificial Intelligence
Assisting Data Mining through Automated Planning
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
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Recently, the areas of planning and scheduling in Artificial Intelligence (AI) have witnessed a big push toward their integration in order to solve complex problems. These problems require both reasoning on which actions are to be performed as well as their precedence constraints (planning) and the reasoning with respect to temporal constraints (e.g., duration, precedence, and deadline); those actions should satisfy the resources they use (scheduling). This paper describes ipss (Integrated Planning and Scheduling System), a domain independent solver that integrates an AI planner that synthesizes courses of actions with constraint-based techniques that reason based upon time and resources. ipss is able to manage not only simple precedence constraints, but also more complex temporal requirements (as the Allen primitives) and multicapacity resource usage/consumption. The solver is evaluated against a set of problems characterized by the use of multiple agents (or multiple resources) that have to perform tasks with some temporal restrictions in the order of the tasks or some constraints in the availability of the resources. Experiments show how the integrated reasoning approach improves plan parallelism and gains better makespans than some state-of-the-art planners where multiple agents are represented as additional fluents in the problem operators. It also shows that ipss is suitable for solving real domains (i.e., workflow problems) because it is able to impose temporal windows on the goals or set a maximum makespan, features that most of the planners do not yet incorporate.