Evidence Theory, also known as Dempster-Shafer Theory, is a mathematical framework for modeling uncertainty and making decisions based on incomplete or ambiguous information. It allows for the representation of beliefs and degrees of confidence in various hypotheses, rather than just binary true or false outcomes. This is particularly useful in situations where evidence is not definitive. In Evidence Theory, information is represented using basic probability assignments that quantify the support for different propositions. The theory combines evidence from multiple sources to reach conclusions, using rules like Dempster's rule of combination to aggregate conflicting information. This approach is widely applied in fields such as artificial intelligence, risk assessment, and information fusion.