Learning Planning Rules in Noisy Stochastic Worlds

Learning Planning Rules in Noisy Stochastic Worlds

Luke Zettlemoyer
Hanna Pasula
Leslie Pack Kaelbling

Abstract: We present an algorithm for learning a model of the effects of actions in noisy stochastic worlds. We consider learning in a 3D simulated blocks world with realistic physics. To model this world, we develop a planning representation with explicit mechanisms for expressing object reference and noise. We then present a learning algorithm that can create rules while also learning derived predicates, and evaluate this algorithm in the blocks world simulator, demonstrating that we can learn rules that effectively model the world dynamics.

To appear in: Proceedings of the Twentieth National Conference on Artificial Intelligence (AAAI-05) .

Download: PDF version


Hanna Pasula
Last modified: Mon Aug 16 18:51:38 EDT 2004