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. © 2002 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Baltes, J., & Park, Y. (2002). Comparison of several machine learning techniques in pursuit-evasion games. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2377 LNAI, pp. 269–274). Springer Verlag. https://doi.org/10.1007/3-540-45603-1_29
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