Ebook: Propositional, Probabilistic and Evidential Reasoning: Integrating Numerical and Symbolic Approaches

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.

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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.

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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.
Content:
Front Matter....Pages i-xiv
Introduction....Pages 1-27
Incidence Calculus....Pages 29-54
Generalizing Incidence Calculus....Pages 55-77
From Numerical to Symbolic Assignments....Pages 79-99
Combining Multiple Pieces of Evidence....Pages 101-118
The Dempster-Shafer Theory of Evidence....Pages 119-158
A Comprehensive Comparison of Generalized Incidence Calculus and Dempster-Shafer Theory....Pages 159-182
Assumption-Based Truth Maintenance Systems....Pages 183-195
Relations Between Extended Incidence Calculus and Assumption-Based Truth Maintenance System....Pages 197-221
Conclusion....Pages 223-244
Back Matter....Pages 245-274

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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.
Content:
Front Matter....Pages i-xiv
Introduction....Pages 1-27
Incidence Calculus....Pages 29-54
Generalizing Incidence Calculus....Pages 55-77
From Numerical to Symbolic Assignments....Pages 79-99
Combining Multiple Pieces of Evidence....Pages 101-118
The Dempster-Shafer Theory of Evidence....Pages 119-158
A Comprehensive Comparison of Generalized Incidence Calculus and Dempster-Shafer Theory....Pages 159-182
Assumption-Based Truth Maintenance Systems....Pages 183-195
Relations Between Extended Incidence Calculus and Assumption-Based Truth Maintenance System....Pages 197-221
Conclusion....Pages 223-244
Back Matter....Pages 245-274
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