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JAIR
2007

Learning Symbolic Models of Stochastic Domains

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Learning Symbolic Models of Stochastic Domains
In this article, we work towards the goal of developing agents that can learn to act in complex worlds. We develop a a new probabilistic planning rule representation to compactly model model noisy, nondeterministic action effects and show how these rules can be effectively learned. Through experiments in simple planning domains and a 3D simulated blocks world with realistic physics, we demonstrate that this learning algorithm allows agents to effectively model world dynamics.
Hanna M. Pasula, Luke S. Zettlemoyer, Leslie Pack
Added 15 Dec 2010
Updated 15 Dec 2010
Type Journal
Year 2007
Where JAIR
Authors Hanna M. Pasula, Luke S. Zettlemoyer, Leslie Pack Kaelbling
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