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Poster
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
in Poster Session »

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.

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Author Information

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

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