Building Blocks for Creative Search

Barto, Andrew
University of Massachusetts Amherst

This project will develop a formal framework based on optimization and reinforcement learning to model important features of creative processes. Large, ill-defined optimization problems that characterize situations where creativity comes into play require selectional, or generate-and-test, procedures that include both a “smart generator” and a “smart tester.” The generator—responsible for generating structures to be evaluated—should be able to generate structures that are novel while at the same time have high probability of being successful. This project investigates new methods for injecting structured, knowledge-based, novelty into the generation process. The tester—the process that evaluates alternatives—should be a good surrogate for the primary objective function, which is often not easily or inexpensively accessible. A smart tester uses a combination of a priori knowledge, knowledge accumulated from past creative activity, and information gained during the current creative activity to assess alternatives. The working hypothesis is that the synergy created by the interaction of a sufficiently smart generator and a sufficiently smart tester can account for important aspects of creative processes.

Although there have been past attempts to mathematically and computationally model aspects of creativity, few bring to bear modern developments in machine learning or take advantage of recent advances in computational reinforcement learning and its relation to animal reward and motivational systems. Furthermore, computational studies have not taken advantage of psychological theories of play, curiosity, surprise, and other factors involved in intrinsically motivated behavior and that perform significant roles in creative activities. This project addresses these shortcomings by taking a interdisciplinary approach. The project will meet the challenge of providing a coherent theoretical account of aspects of creativity without losing sight of the fluidity and flexibility of creative processes.

Representing key elements of creative processes in a mathematically coherent framework can stimulate new directions of research in computer science, engineering, design research, and psychology. Algorithms designed according to this framework can facilitate the design of creative artificial agents as well as form the basis of tools for enhancing human creativity and creative enterprises. Such a framework can also provide a principled means for comparing performances of algorithms purporting to show creativity, thus forming a component of future research methodology directed toward creativity. The project has the potential to contribute to our understanding of general principles underlying human creativity, with implications for design, education, and the arts.