Description: The satisfaction function is a mathematical representation that evaluates how well a solution meets the established objectives in a model. In the context of optimization problems, this function is used to guide decision-making processes, allowing agents or algorithms to learn how to maximize rewards or minimize costs through interaction with their environment. The satisfaction function can be viewed as a criterion that measures a model’s performance in relation to its goals, providing a framework for optimization. In model optimization, this function becomes an objective that is sought to be maximized or minimized, depending on the context. Its design is crucial, as it must effectively capture the preferences and constraints of the problem at hand. The satisfaction function is not limited to evaluating outcomes; it also influences the behavior of the system, as the decisions made by the agent or model are directly related to the values assigned by this function. In summary, the satisfaction function is an essential component in the formulation and resolution of complex problems, as it allows quantifying the success of proposed solutions based on desired objectives.
