Genetic Programming, Logic Design and Case-Based Reasoning for Obstacle Avoidance

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

@InProceedings{Keane:2015:LION,
  author =       "Andy Keane",
  title =        "Genetic Programming, Logic Design and Case-Based
                 Reasoning for Obstacle Avoidance",
  booktitle =    "9th International Conference Learning and Intelligent
                 Optimization, LION 2015",
  year =         "2015",
  editor =       "Clarisse Dhaenens and Laetitia Jourdan and 
                 Marie-Eleonore Marmion",
  volume =       "8994",
  series =       "Lecture Notes in Computer Science",
  pages =        "104--118",
  address =      "Lille, France",
  month =        jan # " 12-15",
  publisher =    "Springer",
  note =         "Revised Selected Papers",
  keywords =     "genetic algorithms, genetic programming, decision
                 tree, data miningfication, algorithm construction,
                 robot",
  isbn13 =       "978-3-319-19083-9",
  bibsource =    "OAI-PMH server at eprints.soton.ac.uk",
  oai =          "oai:eprints.soton.ac.uk:378237",
  type =         "PeerReviewed",
  URL =          "http://eprints.soton.ac.uk/378237/1/Path_Planning.pdf",
  URL =          "http://eprints.soton.ac.uk/378237/",
  isbn13 =       "978-3-319-19084-6",
  DOI =          "doi:10.1007/978-3-319-19084-6_9",
  size =         "15 pages",
  abstract =     "This paper draws on three different sets of ideas from
                 computer science to develop a self-learning system
                 capable of delivering an obstacle avoidance decision
                 tree for simple mobile robots. All three topic areas
                 have received considerable attention in the literature
                 but their combination in the fashion reported here is
                 new. This work is part of a wider initiative on
                 problems where human reasoning is currently the most
                 commonly used form of control. Typical examples are in
                 sense and avoid studies for vehicles -- for example the
                 current lack of regulator approved sense and avoid
                 systems is a key road-block to the wider deployment of
                 uninhabited aerial vehicles (UAVs) in civil
                 airspaces.

                 The paper shows that by using well established ideas
                 from logic circuit design (the espresso algorithm) to
                 influence genetic programming (GP), it is possible to
                 evolve well-structured case-based reasoning (CBR)
                 decision trees that can be used to control a mobile
                 robot. The enhanced search works faster than a standard
                 GP search while also providing improvements in best and
                 average results. The resulting programs are
                 non-intuitive yet solve difficult obstacle avoidance
                 and exploration tasks using a parsimonious and
                 unambiguous set of rules. They are based on studying
                 sensor inputs to decide on simple robot movement
                 control over a set of random maze navigation
                 problems.",
  notes =        "VLSI, RPN",
}

Genetic Programming entries for Andy J Keane

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