A nutritional meal planning system was implemented to test the effectiveness of a previously developed routine decision making algorithm. The combinatorics involved in ordering recipes in all possible combinations to produce variability in a meal plan and provide sufficient nutrition is conceptually intensive. Meal planning involves selection of food to eat to fulfill a person\u27s nutritional and personal preferences. This thesis demonstrates meal planning as a decision making problem and demonstrates the utility of the routine decision making algorithm by solving this problem. Generic Tasks, identified through artificial intelligence research, provides the basis for this algorithm. It uses user preferences and to select recipes from a database of possible recipes and generate meal plans for the user
