The book systematically provides the reader with a broad range of systems/research work to date that addresses the importance of combining numerical and symbolic approaches to reasoning under uncertainty in complex applications. It covers techniques on how to extend propositional logic to a probabilistic one and compares such derived probabilistic logic with closely related mechanisms, namely evidence theory, assumption-based truth maintenance systems and rough sets, in terms of representing and reasoning with knowledge and evidence. The book primarily addresses researchers, practitioners, students and lecturers in the field of Artificial Intelligence, particularly in the areas of reasoning under uncertainty, logic, knowledge representation and reasoning, and non-monotonic reasoning.