  Agents learning to act autonomously in real-world domains must acquire a
model of the dynamics of the domain in which they operate. Learning domain
dynamics can be challenging, especially where an agent only has partial access
to the world state, and/or noisy external sensors. Even in standard STRIPS
domains, existing approaches cannot learn from noisy, incomplete observations
typical of real-world domains. We propose a method which learns STRIPS action
models in such domains, by decomposing the problem into first learning a
transition function between states in the form of a set of classifiers, and
then deriving explicit STRIPS rules from the classifiers' parameters. We
evaluate our approach on simulated standard planning domains from the
International Planning Competition, and show that it learns useful domain
descriptions from noisy, incomplete observations.
