Permissive Planning: A Machine Learning Approach to Linking Internal and External Worlds

Gerald DeJong, Scott Bennett

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 pemissive 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.

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