A Survey of Computational Creativity Theories — From Formal Rigor to Generative Power

Computational creativity has passed through two eras defined by an inverse relationship between generative reach and interpretability. In the formal era, creativity was built from explicit representations — bisociation operationalized as conceptual blending, Boden's conceptual spaces formalized as creative systems, category-theoretic invention in COINVENT — yielding systems interpretable by construction but narrow in scope. Large language models brought the bargain to its extreme: trained on web-scale text, they produce fluent creative output across every domain, yet generate without exposing the mechanism linking input to output. This survey reads the field through that trade-off, tracing three foundational questions — how creativity is generated, measured, and explained — across both eras, and surveys the field's open challenges: reliable evaluation, output homogenization, ethics of creative AI, human-AI co-creativity, the status of machine creativity, and multimodal reach. We argue that one promising direction is to recover the interpretability of the formal era within the generative power of learned models.