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Chapter 16
A Culinary Computational Creativity System
Florian Pinel, Lav R. Varshney and Debarun Bhattacharjya
Abstract
Compared to artifacts in expressive or performance domains, work
products resulting from scientific creativity (including culinary recipes) seem much
more conducive to data-driven assessment. If such products are viewed as an assem-
bly of constituents that follow certain association principles, one could apply com-
putationally intensive techniques to generate many possible combinations and use
automated assessors to evaluate each of them. Assembly work plans for the selected
novel products could subsequently be inferred from existing records. In this chapter,
we report on our efforts to build a computational creativity system for culinary
recipes. After gathering data and creating a knowledge base of recipes and ingre-
dients, the system generates ingredient combinations that satisfy user inputs such
as the choice of key ingredient, desired dish, and cuisine. Once a combination has
been selected with the help of novelty and quality evaluators, the system further
recommends ingredient proportions using a distributional conformance method and
generates recipe steps using a subgraph composition algorithm. The time durations
or efforts of atomic steps are estimated by solving an inverse problem from data on
complete recipes. The example of culinary recipes could be generalized and applied
to other scientific domains; manufacturing products and business processes could
potentially follow a similar recipe for success.
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