Comparison of several machine learning techniques in pursuit-evasion games

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper describes the results of an empirical evaluation comparing the performance of five different algorithms in a pursuit and evasion game. The pursuit and evasion game was played using two robots. The task of the pursuer was to catch the other robot (the evader). The algorithms tested were a random player, the optimal player, a genetic algorithm learner, a k-nearest neighbor learner, and a reinforcement learner. The k-nearest neighbor learner performed best overall, but a closer analysis of the results showed that the genetic algorithm suffered from an exploration-exploitation problem.

Original languageEnglish
Title of host publicationRoboCup 2001
Subtitle of host publicationRobot Soccer World Cup V
Pages269-274
Number of pages6
Publication statusPublished - 2002 Dec 1
Event5th Robot World Cup Soccer Games and Conferences, RoboCup 2001 - Seattle, WA, United States
Duration: 2001 Aug 22001 Aug 10

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2377 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other5th Robot World Cup Soccer Games and Conferences, RoboCup 2001
CountryUnited States
CitySeattle, WA
Period01/8/201/8/10

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Baltes, H. J., & Park, Y. (2002). Comparison of several machine learning techniques in pursuit-evasion games. In RoboCup 2001: Robot Soccer World Cup V (pp. 269-274). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2377 LNAI).