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Autonomous Learning of Action Models for Planning
Neville Mehta · Prasad Tadepalli · Alan Fern

Tue Dec 13 08:45 AM -- 02:59 PM (PST) @ None #None

This paper introduces two new frameworks for learning action models for planning. In the mistake-bounded planning framework, the learner has access to a planner for the given model representation, a simulator, and a planning problem generator, and aims to learn a model with at most a polynomial number of faulty plans. In the planned exploration framework, the learner does not have access to a problem generator and must instead design its own problems, plan for them, and converge with at most a polynomial number of planning attempts. The paper reduces learning in these frameworks to concept learning with one-sided error and provides algorithms for successful learning in both frameworks. A specific family of hypothesis spaces is shown to be efficiently learnable in both the frameworks.

Author Information

Neville Mehta (Oregon State University)
Prasad Tadepalli (Oregon State University)
Alan Fern (Oregon State University)

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