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@InProceedings{AAAI04-abstract-MDP,
  author =	 {Patrick Riley and Manuela Veloso},
  title =	 {Advice Generation from Observed Execution: Abstract
                  {M}arkov Decision Process Learning},
  year =	 2004,
  booktitle =	 aaai2004,
  abstract =	 { An advising agent, a coach, provides advice to
                  other agents about how to act. In this paper we
                  contribute an advice generation method using
                  observations of agents acting in an
                  environment. Given an abstract state definition and
                  partially specified abstract actions, the algorithm
                  extracts a Markov Chain, infers a Markov Decision
                  Process, and then solves the MDP (given an arbitrary
                  reward signal) to generate advice. We evaluate our
                  work in a simulated robot soccer environment and
                  experimental results show improved agent performance
                  when using the advice generated from the MDP for
                  both a sub-task and the full soccer game. },
  bib2html_pubtype ={Refereed Conference},
  bib2html_rescat ={Coaching,Opponent and Teammate Modeling},
  bib2html_funding ={ActiveTemplates,RADAR},
  bib2html_extra_info={<a href="http://www.cs.cmu.edu/~pfr/appedices/2004aaai.html">Online Appendix</a>}
}