Computational Creativity via Generative Process Modeling

Abstract

This paper explores the development of a novel approach to computational creativity centered around generative process modeling. Current AI systems striving for creative output frequently fall short of mimicking the nuanced and often unpredictable nature of human creativity. A key limitation is the absence of a mechanism for representing and controlling the creative process itself. This research proposes a framework that explicitly models the creative process as a series of discrete computational steps, encompassing stages such as ideation, experimentation, and refinement. The core mechanism involves constructing a generative process model (GPM) that can actively guide and shape the creative journey. This approach moves beyond simply generating outputs and instead focuses on the strategic manipulation of the underlying creative process, offering a more robust and controllable pathway to creative outcomes. The paper details the design principles of the GPM and outlines a potential architecture for its implementation, highlighting its potential to overcome current limitations in AI-driven creative systems. The presented model offers a framework for understanding and replicating the iterative and often messy nature of human creativity, ultimately leading to more sophisticated and genuinely creative AI systems.