| M. Alicia Perez and Jaime G. Carbonell. Control knowledge to improve plan quality. In Proceedings of the Second International Conference on AI Planning Systems, pages 323--328, 1994. |
....like temporal deadlines and maintenance intervals that are difficult to capture using a time separable additive value function. Williamson [138, 139] implements this model by extending a classical planning algorithm to solve the resulting optimization problem. 19 Perez and Carbonell s work [105] also addresses the introduction of cost information into the classical planning framework, but maintains the split between a classical satisficing planner and additional cost information provided in the utility model. The cost information is used to learn search control rules that allow the ....
M. Alicia Perez and Jaime G. Carbonell. Control knowledge to improve plan quality. In Proceedings of the Second International Conference on AI Planning Systems, pages 323--328, 1994.
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