1 Introduction --; 2 Incidence Calculus --; 3 Generalizing Incidence Calculus --; 4 From Numerical to Symbolic Assignments --; 5 Combining Multiple Pieces of Evidence --; 6 The Dempster-Shafer Theory of Evidence --; 7 A Comprehensive Comparison of Generalized Incidence Calculus and Dempster-Shafer Theory --; 8 Assumption-Based Truth Maintenance Systems --; 9 Relations Between Extended Incidence Calculus and Assumption-Based Truth Maintenance System --; 10 Conclusion --; Mathematical Notation --; List of Figures --; List of Tables.
SUMMARY OR ABSTRACT
Text of Note
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.