A Computational Learning Approach for the Development of Karate Sequences

摘要

Karate, like most martial arts, relies on the development of complex sequences of interrelated techniques. The development of strong technique, physical endurance, mobility, and precision requires repetitive practice of sequences and combinations of moves. However, training typically has biases, leading to limited repertoire, and poor dynamical decision making. Here we use machine learning to develop a mathematical model of sequences of karate techniques. We present a series of algorithms for the generation of novel training combinations, which are internally consistent with a supplied training regime. Due to the general nature of the mathematical approach developed here, we anticipate our approach has wider applications, for example analysing individual competitors’ decision-making process and performance, identifying weaknesses and vulnerabilities in an athlete’s repertoire.