@COMMENT This file was generated by bib2html.pl <http://www.cs.cmu.edu/~pfr/misc_software/index.html#bib2html> version 0.91
@COMMENT written by Patrick Riley <http://www.cs.cmu.edu/~pfr>
@COMMENT This file came from Jefferson Provosts's publication pages at
@COMMENT http://www.cnbc.cmu.edu/~jp/research/publications
@InProceedings{provost-epirob07,
  author = 	 {Jefferson Provost and Benjamin J. Kuipers and Risto Miikkulainen},
  title = 	 {Self-Organizing Distinctive State Abstraction Using Options},
  booktitle = {Proceedings of the 7th International Conference on Epigenetic Robotics},
  year = 	 2007,
  volume = 	 {7},
  abstract = {The behavior of a realistic robotic agent takes place in
                  high-dimensional continuous sensory, state, and
                  motor spaces. Autonomous learning of effective
                  behaviors requires autonomous learning of useful
                  abstractions for these spaces. The concept of
                  distinctive state from the topological mapping
                  literature can be used to learn such actions from
                  the agent’s own experience, without prior knowledge
                  provided by an external designer. In the approach
                  taken in this paper, Self-Organizing
                  Distinctive-state Abstraction (SODA), a variant of
                  self-organizing maps defines a finite set of
                  distinctive sensory prototypes; distinctive states
                  are then defined as local maxima of the activation
                  function for the leading prototype. Hierarchical
                  reinforcement learning is then used to learn options
                  that move the agent among distinctive states with
                  increasing reliability. This state-action
                  abstraction is learned autonomously, and reflects
                  only the environment and the agent’s sensorimotor
                  capabilities, without external direction. Using
                  SODA, a robot can learn to navigate in large
                  environments that are intractable to learn in using
                  primitive motor commands.}
}