The Proof is in the Pudding: Using Automated Theorem Proving to Generate Cooking Recipes
This addresses recipe generation for cooking applications, but appears incremental as it builds on existing NLG and theorem proving techniques.
The paper tackles automated cooking recipe generation by developing FASTFOOD, a rule-based natural language generation program that uses automated theorem proving to select ingredients and instructions, with a temporal optimization module to rearrange steps for time efficiency. The system is described using a four-phase NLG framework and compared to existing approaches.
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.