• DocumentCode
    2212239
  • Title

    Competence progress intrinsic motivation

  • Author

    Stout, Andrew ; Barto, Andrew G.

  • Author_Institution
    Dept. of Comput. Sci., Univ. of Massachusetts, Amherst, MA, USA
  • fYear
    2010
  • fDate
    18-21 Aug. 2010
  • Firstpage
    257
  • Lastpage
    262
  • Abstract
    One important role of an agent´s motivational system is to choose, at any given moment, which of a number of skills the agent should attempt to improve. Many researchers have suggested “intrinsically motivated” systems that receive internal reward for model learning progress, but for the most part this notion has not been applied with respect to skill competence, or to choose between skills. In this paper we propose an agent motivated to gain competence in its environment by learning a number of skills, addressing head-on the mechanism of competence progress motivation for the purpose of governing the efficient learning of skills. We demonstrate this new approach in a simple illustrative domain and show that it outperforms a naïve agent, achieving higher competence faster by focusing attention and learning effort on skills for which progress can be made while ignoring those skills that are already learned or are at the moment too difficult.
  • Keywords
    human factors; learning (artificial intelligence); multi-agent systems; agents motivational system; competence progress intrinsic motivation; learning progress model; naive agent; Conferences; Equations; Knowledge based systems; Learning; Markov processes; Proposals; Robots;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Development and Learning (ICDL), 2010 IEEE 9th International Conference on
  • Conference_Location
    Ann Arbor, MI
  • Print_ISBN
    978-1-4244-6900-0
  • Type

    conf

  • DOI
    10.1109/DEVLRN.2010.5578835
  • Filename
    5578835