H. M. Pasula, L. S. Zettlemoyer and L. P. Kaelbling (2007) "Learning Symbolic Models of Stochastic Domains", Volume 29, pages 309-352

PDF | PostScript | doi:10.1613/jair.2113

In this article, we work towards the goal of developing agents that can learn to act in complex worlds. We develop a probabilistic, relational planning rule representation that compactly models noisy, nondeterministic action effects, and show how such 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.

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