Learning symbolic models of stochastic domains

Learning symbolic models of stochastic domains (2005)

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by Hanna M. Pasula , Luke S. Zettlemoyer , Leslie Pack Kaelbling

Venue:	JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH
Citations:	26 - 1 self

BibTeX

@ARTICLE{Pasula05learningsymbolic,
    author = {Hanna M. Pasula and Luke S. Zettlemoyer and Leslie Pack Kaelbling},
    title = {Learning symbolic models of stochastic domains},
    journal = {JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH},
    year = {2005},
    volume = {29},
    pages = {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 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.

Citations

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