Associative Algorithms for Computational Creativity

Abstract

Computational creativity, the generation of new, unimagined ideas or artifacts by a machine that are deemed creative by people, can be applied in the culinary domain to create novel and flavorful dishes. In fact, we have done so successfully using a combinatorial algorithm for recipe generation combined with statistical models for recipe ranking and selection. However, the algorithm of creation in our prior work may be difficult for people to interpret, understand, and ultimately adopt because the process differs from the process of human creativity theorized in the psychology and neuroscience literatures. In this paper, to address this issue, we discuss how human creativity, including in the food arena, may be built on associations and how an algorithm also built on associations can be more relatable to people so they can interact with the tool more easily. We propose a computational creativity approach that extends the data mining technique of association rule mining to generate new food recipes. We illustrate this associative algorithm on real-world culinary data.