A Hybrid Genetic Programming Decision Making System for RoboCup Soccer Simulation

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

  author =       "Amir Tavafi and Wolfgang Banzhaf",
  title =        "A Hybrid Genetic Programming Decision Making System
                 for RoboCup Soccer Simulation",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  series =       "GECCO '17",
  year =         "2017",
  isbn13 =       "978-1-4503-4920-8",
  address =      "Berlin, Germany",
  pages =        "1025--1032",
  size =         "8 pages",
  URL =          "http://doi.acm.org/10.1145/3071178.3071194",
  DOI =          "doi:10.1145/3071178.3071194",
  acmid =        "3071194",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  keywords =     "genetic algorithms, genetic programming, RoboCup,
                 decision making, multi-agent systems, soccer
  month =        "15-19 " # jul,
  abstract =     "In this contribution we propose a hybrid genetic
                 programming approach for evolving a decision making
                 system in the domain of RoboCup Soccer (Simulation
                 League). Genetic programming has been rarely used in
                 this domain in the past, due to the difficulties and
                 restrictions of the soccer simulation. The real-time
                 requirements of robot soccer and the lengthy evaluation
                 time even for simulated games provide a formidable
                 obstacle to the application of evolutionary approaches.
                 Our new method uses two evolutionary phases, each of
                 which compensating for restrictions and limitations of
                 the other. The first phase produces some evolved GP
                 individuals applying an off-game evaluation system
                 which can be trained on snapshots of game situations as
                 they actually happened in earlier games, and
                 corresponding decisions tagged as correct or wrong. The
                 second phase uses the best individuals of the first
                 phase as input to run another GP system to evolve
                 players in a real game environment where the quality of
                 decisions is evaluated through winning or losing during
                 real-time runs of the simulator. We benchmark the new
                 system against a baseline system used by most
                 simulation league teams, as well as against winning
                 systems of the 2016 tournament.",
  notes =        "Also known as \cite{Tavafi:2017:HGP:3071178.3071194}
                 GECCO-2017 A Recombination of the 26th International
                 Conference on Genetic Algorithms (ICGA-2017) and the
                 22nd Annual Genetic Programming Conference (GP-2017)",

Genetic Programming entries for Amir Tavafi Wolfgang Banzhaf