Propositional, Probabilistic and Evidential Reasoning

This facilitates purely symbolic reasoning using the possible worlds and numeric reasoning via the probabilities of those possible worlds. The consequence is a unified mechanism which includes both symbolic and numeric mechanisms as special cases. The book systematically provides the reader with a broad range of systems/research work to date that address 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 is addressed primarily to 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. 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.