Automating Creativity
Ming-Hui Huang, Roland T. Rust

TL;DR
This paper proposes a reinforcement learning framework with prompt, response, and reward models to enhance the creative capabilities of generative AI, moving beyond mere content generation to genuine creativity.
Contribution
It introduces a novel triple prompt-response-reward engineering framework to systematically develop and improve AI's creative abilities.
Findings
Framework enables strategic application of GenAI for different creativity levels
Incorporates feedback from multiple sources to enhance creativity
Facilitates development of more surprising and innovative outputs
Abstract
Generative AI (GenAI) has spurred the expectation of being creative, due to its ability to generate content, yet so far, its creativity has somewhat disappointed, because it is trained using existing data following human intentions to generate outputs. The purpose of this paper is to explore what is required to evolve AI from generative to creative. Based on a reinforcement learning approach and building upon various research streams of computational creativity, we develop a triple prompt-response-reward engineering framework to develop the creative capability of GenAI. This framework consists of three components: 1) a prompt model for expected creativity by developing discriminative prompts that are objectively, individually, or socially novel, 2) a response model for observed creativity by generating surprising outputs that are incrementally, disruptively, or radically innovative, and…
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Taxonomy
TopicsCreativity in Education and Neuroscience
