Interactively Learned Probabilistic Context-sensitive Grammar in Genetic Programming for the Evolution of Snake-like Robot

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Tanev:2009:ICCAS-SICE,
  author =       "Ivan Tanev and Katsunori Shimohara",
  title =        "Interactively Learned Probabilistic Context-sensitive
                 Grammar in Genetic Programming for the Evolution of
                 Snake-like Robot",
  booktitle =    "ICRAS \& SICE International Joint Conference,
                 ICCAS-SICE, 2009",
  year =         "2009",
  month =        "18-21 " # aug,
  address =      "Fukuoka",
  pages =        "2732--2737",
  publisher =    "IEEE",
  isbn13 =       "978-4-9077-6433-3",
  keywords =     "genetic algorithms, genetic programming,
                 context-sensitive grammar, interactively learned
                 consensus sequences, probabilistic context, snake-like
                 robot evolution, snakebot, user feedback,
                 context-sensitive grammars, learning (artificial
                 intelligence), probability, robots",
  URL =          "http://ieeexplore.ieee.org/search/srchabstract.jsp?tp=&arnumber=5333378",
  size =         "6 pages",
  abstract =     "We discuss an approach of incorporating interactively
                 learned consensus sequences (ILCS) in genetic
                 programming (GP) for efficient evolution of simulated
                 Snakebot situated in a challenging environment. ILCS
                 introduce a biased mutation in GP via probabilistic
                 context sensitive grammar, in which the probabilities
                 of applying the production rules with multiple
                 right-hand side alternatives depend on the grammatical
                 context. The distribution of these probabilities is
                 learned interactively from the syntax of the Snakebots,
                 exhibiting behavioral traits that according to the
                 human observer are relevant for the emergence of
                 ability to overcome obstacles. Because at the earlier
                 stages of evolution these behavioral traits are not
                 necessarily pertinent to the best performing (i.e.
                 fastest) Snakebots, the user feedback provides the
                 evolution with an additional insight about the
                 promising areas in the fitness landscape. Empirical
                 results verify that employing ILCS improves the
                 efficiency of GP in that the evolved Snakebots are
                 faster than those obtained via canonical GP.",
  notes =        "http://www.sice.or.jp/ICCAS-SICE2009/ Also known as
                 \cite{5333378}",
}

Genetic Programming entries for Ivan T Tanev Katsunori Shimohara

Citations