  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.
