Building agents which can learn to act autonomously in the world is an important challenge for artificial intelligence. While autonomous agents often have to operate in noisy, uncertain worlds, current methods to learn action models from agents’ experiences typically assume fully deterministic worlds. This paper presents a noise-tolerant approach to learning probabilistic planning operators from experience. Preliminary experiments demonstrate that the approach learns accurate models even if agents’ observations are noisy.
|Title of host publication||31st Workshop of the UK Planning & Scheduling Special Interest Group (PlanSIG 2013)|
|Number of pages||2|
|Publication status||Published - 2014|