Hierarchical Representations of Behavior for Efficient Creative Search.

Abstract

We present a computational framework in which to explore the generation of creative behavior in artificial systems. In particular, we adopt an evolutionary perspective of human creative processes and outline the essential components of a creative system this view entails. These components are implemented in a hierarchical reinforcement learning frame-work and the creative potential of the system is demonstrated in a simple artificial domain. The results presented here lend support to our conviction that creative thought and behavior are generated through the interaction of a sufficiently sophis-ticated variation mechanism and a comparably sophisticated selection mechanism.