@COMMENT This file was generated by bib2html.pl <http://www.cs.cmu.edu/~pfr/misc_software/index.html#bib2html> version 0.90
@COMMENT written by Patrick Riley <http://www.cs.cmu.edu/~pfr>
@COMMENT This file came from Peter Stone's publication pages at
@COMMENT http://www.cs.utexas.edu/~pstone/papers
@TechReport{faseltr08,
  author       = "Ian Fasel and Michael Quinlan and Peter Stone",
  title	       = "A General Purpose Task Specification Language for Bootstrap Learning",
  institution  = "The University of Texas at Austin, Department of
Computer Sciences, AI Laboratory",
  number       = "AI-08-1",
  year	       = 2008,
  abstract     = {
                  Reinforcement learning (RL) is an effective
                  framework for online learning by autonomous
                  agents. Most RL research focuses on
                  domain-independent learning \emph{algorithms},
                  requiring an expert human to define the
                  \emph{environment} (state and action representation)
                  and \emph{task} to be performed (e.g.\ start state
                  and reward function) on a case-by-case basis. In
                  this paper, we describe a general language for a
                  teacher to specify sequential decision making tasks
                  to RL agents.  The teacher may communicate
                  properties such as start states, reward functions,
                  termination conditions, successful execution traces,
                  task decompositions, and other advice. The learner
                  may then practice and learn the task on its own
                  using any RL algorithm. We demonstrate our language
                  in a simple GridWorld example and on the RoboCup
                  soccer keepaway benchmark problem. The language
                  forms the basis of a larger ``Bootstrap Learning''
                  model for machine learning, a paradigm for
                  incremental development of complete systems through
                  integration of multiple machine learning techniques.
  },
}