Accession Number : ADA610456


Title :   Learning Unknown Event Models


Descriptive Note : Conference paper


Corporate Author : KNEXUS RESEARCH CORP SPRINGFIELD VA


Personal Author(s) : Molineaux, Matthew ; Aha, David W


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/a610456.pdf


Report Date : Jul 2014


Pagination or Media Count : 8


Abstract : Agents with incomplete environment models are likely to be surprised, and this represents an opportunity to learn. We investigate approaches for situated agents to detect surprises, discriminate among different forms of surprise, and hypothesize new models for the unknown events that surprised them. We instantiate these approaches in a new goal reasoning agent (named FOOLMETWICE), investigate its performance in simulation studies, and report that it produces plans with significantly reduced execution cost in comparison to not learning models for surprising events.


Descriptors :   *REASONING , ROBOTICS


Subject Categories : Operations Research
      Cybernetics


Distribution Statement : APPROVED FOR PUBLIC RELEASE