HTN planning: complexity and expressivity
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
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
Goal ordering in partially ordered plans
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
Understanding planning tasks: domain complexity and heuristic decomposition
Understanding planning tasks: domain complexity and heuristic decomposition
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Automated Planning is a very active area of research within Artificial Intelligence. Broadly this discipline deals with the methods by which an agent can independently determine the action sequence required to successfully achieve a set of objectives. In this paper, we will present initial work outlining a new approach to planning based on Clustering techniques, in order to group states of the world together and use the fundamental structure of the world to lift out more abstract representations. We will show that this approach can limit the combinatorial explosion of a typical planning problem in a way that is much more intuitive and reusable than has previously been possible, and outline ways that this approach can be developed further.