TY - GEN
T1 - Comparison of several machine learning techniques in pursuit-evasion games
AU - Baltes, Jacky
AU - Park, Yongjoo
PY - 2002
Y1 - 2002
N2 - 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.
AB - 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.
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U2 - 10.1007/3-540-45603-1_29
DO - 10.1007/3-540-45603-1_29
M3 - Conference contribution
AN - SCOPUS:84867465162
SN - 3540439129
SN - 9783540439127
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 269
EP - 274
BT - RoboCup 2001
PB - Springer Verlag
T2 - 5th Robot World Cup Soccer Games and Conferences, RoboCup 2001
Y2 - 2 August 2001 through 10 August 2001
ER -