Accelerating the Evolution of Cognitive Behaviors Through Human-Computer Collaboration

Abstract

An open problem in neuroevolution (i.e. evolving artificial neural networks) is to evolve complex cognitive behaviors that allow robots to adapt and learn from past experience. While previous studies on the evolution of cognitive behaviors have shown that more explorative search methods such as novelty search, outperform traditional objective-based approaches, evolving more sophisticated cognitive capabilities remains difficult. In this context, a major challenge is the deceptive nature of learning to learn. Because it is easier at first to improve fitness without evolving the ability to learn, evolution often converges on non-adaptive solutions. The novel hypothesis in this paper is that we can leverage human insights during the search for cognitive behaviors because of our ability to more easily distinguish between adaptive and non-adaptive solutions than novelty or fitness-based approaches. This paper shows that the recently introduced method novelty-assisted interactive evolution (NA-IEC), which combines human intuition with novelty search, allows the evolution of cognitive behaviors in a T-Maze domain faster than fully-automated searches by themselves.