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 3) a reward
model for improving creativity over time by incorporating feedback from the AI,
the creator/manager, and/or the customers. This framework enables the
application of GenAI for various levels of creativity strategically.

基于强化学习方法和计算创造力的不同研究流派，本文提出了一个三重引导 - 响应 - 奖励工程框架，以提高生成人工智能（GenAI）的创造能力。该框架包括了引导模型、响应模型和奖励模型，通过开发创造性的引导、生成出令人惊喜和创新性的输出，并结合 AI、创作者 / 管理者和客户的反馈，逐步提高生成人工智能的创造力。