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

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