Learning symbolic models of stochastic domains

Authors:
Hanna M. Pasula;Luke S. Zettlemoyer;Leslie Pack Kaelbling
Affiliations:
MIT CSAIL, Cambridge, MA;MIT CSAIL, Cambridge, MA;MIT CSAIL, Cambridge, MA
Venue:
Journal of Artificial Intelligence Research
Year:
2007

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Abstract

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.