Planning for conjunctive goals
Artificial Intelligence
Practical planning: extending the classical AI planning paradigm
Practical planning: extending the classical AI planning paradigm
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
Explanation-Based Learning: An Alternative View
Machine Learning
Dynamic Programming
IJCAI'75 Proceedings of the 4th international joint conference on Artificial intelligence - Volume 1
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Because complex real-world domains defy perfect formalization, real-world planners must be able to cope with incorrect domain knowledge. This paper offers a theoretical framework for permissive planning, a machine learning method for improving the real-world behavior of planners. Permissive planning aims to acquire techniques that tolerate the inevitable mismatch between the planner's internal beliefs and the external world. Unlike the reactive approach to this mismatch, permissive planning embraces projection. The method is both problem-independent and domain-independent. Unlike classical planning, permissive planning does not exclude real-world performance from the formal definition of planning.