Proceedings

Extending Classical Planning to Real-World Execution with Machine Learning

Gerald DeJong, Scott Bennett

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Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence. Learning. Pages 1153-1160.

Abstract

In previous work (Bennett 1993; DeJong and Bennett 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.

Gerald DeJong, and Scott Bennett. 1995. Extending Classical Planning to Real-World Execution with Machine Learning. In Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, IJCAI-95. IJCAI, 1153–1160. https://ijcai.org/proceedings/1995/149
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