The Proof is in the Pudding: Using Automated Theorem Proving to Generate Cooking Recipes

TL;DR

This paper introduces FASTFOOD, a rule-based system that uses automated theorem proving to generate cooking recipes, optimizing for time efficiency through a temporal rearrangement module.

Contribution

It presents a novel approach combining automated theorem proving with natural language generation for recipe creation, including a temporal optimization feature.

Findings

FASTFOOD successfully generates coherent recipes.

The temporal module improves cooking time efficiency.

Comparison shows advantages over existing systems.

Abstract

This paper presents FASTFOOD, a rule-based Natural Language Generation Program for cooking recipes. Recipes are generated by using an Automated Theorem Proving procedure to select the ingredients and instructions, with ingredients corresponding to axioms and instructions to implications. FASTFOOD also contains a temporal optimization module which can rearrange the recipe to make it more time-efficient for the user, e.g. the recipe specifies to chop the vegetables while the rice is boiling. The system is described in detail, using a framework which divides Natural Language Generation into 4 phases: content production, content selection, content organisation and content realisation. A comparison is then made with similar existing systems and techniques.