Exploiting interactive genetic algorithms for creative humanoid dancing

The paper discusses an approach aimed at endowing a cognitive architecture with artificial creativity capabilities in order to make a humanoid able to dance in a pleasant manner. The robot associates movements to music perception cre- ating an aesthetically valuable dance by using a Hidden Markov Model with a nonclassical approach. Two matrices mainly influence the model: a Transition matrix TM, and an Emission Matrix EM. The TM matrix rules the transition between two subsequent movements. The EM matrix constitutes the link be- tween a set of movements and the perceived music features. In order to compute the EM matrix, we exploit a genetic algorithm approach. The approach makes use of two kinds of fitness functions. The first one is an internal evaluation fit- ness that allows the robot to autonomously learn the association between music and movements. The second one depends on the interaction with a human teacher, leading to the determination of different dance styles, which consti- tute the robot repertoire. The experimental part discusses the effects on the creativity of different distances to compute fitness.