The idea behind weight manipulation is simple: as long as you'll be in a caloric deficit you'll lose weight and vice-versa: if you'll be in a caloric surplus you'll gain weight. The algorithm will use as input the foods that I want to consume in a given day and some data about me (e.g current weight, desired weight, activity level, goal (loose/gain weight), macronutrient ratio). We consider that every weight represents the quantity of food (in grams) that you should eat, from the corresponding food vector input. The idea is that the resulted macronutrients for the diet based on our weights to be as close as possible to your ideal diet's macronutrients. Then, I have used a free macronutrient calculator, which based on some personal information (e.g age, sex, current weight, desired weight, level of activity) told me that it will be ideal to eat 175 grams of protein, 359 of carbs, and 101 of fats.

This paper describes NAIVE, a low-level knowledge representation language and inferencing process. NAIVE has been designed for reasoning about nondeterministic dynamic systems like those found in medicine. Knowledge is represented in a graph structure consisting of nodes, which correspond to the variables describing the system of interest, and arcs, which correspond to the procedures used to infer the value of a variable from the values of other variables. The value of a variable can be determined at an instant in time, over a time interval or for a series of times. Information about the value of a variable is expressed as a probability density function which quantifies the likelihood of each possible value. The inferencing process uses these probability density functions to propagate uncertainty. NAIVE has been used to develop medical knowledge bases including over 100 variables.