Combining fitness-based search and user modeling in evolutionary robotics

Methodologies are emerging in many branches of computer science that demonstrate how human users and automated algorithms can collaborate on a problem such that their combined solutions outperform those produced by either humans or algorithms alone. The problem of behavior optimization in robotics seems particularly well-suited for this approach because humans have intuitions about how animals---and thus robots---should and should not behave, and can visually detect non-optimal behaviors that are trapped in local optima. Here we introduce a multiobjective approach in which a surrogate user (which stands in for a human user) deflects search away from local optima and a traditional fitness function eventually leads search toward the global optimum. We show that this approach produces superior solutions for a deceptive robotics problem compared to a similar search method that is guided by just a surrogate user or just a fitness function.