Belief functions are mathematical tools used in the field of artificial intelligence and decision-making to represent uncertainty. They allow for the modeling of degrees of belief about various outcomes, rather than just binary true or false values. This approach helps in situations where information is incomplete or ambiguous, providing a more flexible way to handle uncertainty. In a belief function framework, each piece of evidence contributes to a belief mass assigned to different hypotheses. This enables the aggregation of information from various sources, allowing for a more comprehensive understanding of the situation. Belief functions are often used in Dempster-Shafer theory, which formalizes how to combine these beliefs systematically.