Learning probabilistic planning operators from noisy observations

Kira Mourao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

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.
Original languageEnglish
Title of host publication31st Workshop of the UK Planning & Scheduling Special Interest Group (PlanSIG 2013)
Number of pages2
Publication statusPublished - 2014


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