Static and Dynamic Multi-Robot Coverage with Grammatical Evolution Guided by Reinforcement and Semantic Rules

Created by W.Langdon from gp-bibliography.bib Revision:1.3949

@InCollection{Mingo:2012:idarla,
  author =       "Jack Mario Mingo and Ricardo Aler and 
                 Dario Maravall and Javier {de Lope}",
  title =        "Static and Dynamic Multi-Robot Coverage with
                 Grammatical Evolution Guided by Reinforcement and
                 Semantic Rules",
  booktitle =    "Intelligent Data Analysis for Real-Life Applications:
                 Theory and Practice",
  publisher =    "IGI Global",
  year =         "2012",
  editor =       "Rafael Magdalena-Benedito and 
                 Marcelino Martinez-Sober and Jose Maria Martinez-Martinez and 
                 Joan Vila-Frances and Pablo Escandell-Montero",
  chapter =      "17",
  pages =        "336--365",
  address =      "Hershey",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution",
  issn13 =       "9781466618060",
  URL =          "http://www.igi-global.com/book/intelligent-data-analysis-real-life/62622",
  DOI =          "doi:10.4018/978-1-4666-1806-0.ch017",
  abstract =     "In recent years there has been an increasing interest
                 in the application of robot teams to solve some kind of
                 problems. Although there are several environments and
                 tasks where a team of robots can deliver better results
                 than a single robot, one of the most active attention
                 focus is concerned with solving coverage problems,
                 either static or dynamic, mainly in unknown
                 environments. The authors propose a method in this work
                 to solve these problems in simulation by means of
                 grammatical evolution of high-level controllers.
                 Evolutionary algorithms have been successfully applied
                 in many applications, but better results can be
                 achieved when evolution and learning are combined in
                 some way. This work uses one of this hybrid algorithms
                 called Grammatical Evolution guided by Reinforcement
                 but the authors enhance it by adding semantic rules in
                 the grammatical production rules. This way, they can
                 build automatic high-level controllers in fewer
                 generations and the solutions found are more readable
                 as well. Additionally, a study about the influence of
                 the number of members implied in the evolutionary
                 process is addressed.",
}

Genetic Programming entries for Jack Mario Mingo Ricardo Aler Mur Dario Maravall Gomez-Allende Javier de Lope

Citations