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 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. 
L. P. Kaelbling, H. M. Pasula, L. S. Zettlemoyer
cs.LG cs.AI