Communications of the ACM
SOAR: an architecture for general intelligence
Artificial Intelligence
Machine learning: a theoretical approach
Machine learning: a theoretical approach
A machine learning approach to planning in complex real-world domains
A machine learning approach to planning in complex real-world domains
Learning Search Control Knowledge: An Explanation-Based Approach
Learning Search Control Knowledge: An Explanation-Based Approach
Machine Learning
GRASPER: a permissive planning robot
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Concurrent reactive plans: anticipating and forestalling execution failures
Concurrent reactive plans: anticipating and forestalling execution failures
Compilation of non-contemporaneous constraints
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
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In previous work (Bennett 1993 DeJong and Bennetl 1993) we proposed a machine learning approach called permissive planning to extend classical planning into the realm of real world plan execution. Our prior results have been favorable but empirical (Bennett and DeJong 1991). Here we examine the analytic foundations of our empirical success. We advance a formal account of realworld planning adequacy. We prove that permissive planning does what it claims to do it probabilistically achieves adequate real-world performance or guarantees that no adequate real-world planning behavior is possible within the flexibility allowed. We prove that the approach scales tractably We prove that restrictions are necessary without them permissive planning is impossible. We also show how these restrictions can be quite naturally met through schema based planning and explanation-based learning.