Belief Revision

Belief revision is a topic of much interest in theoretical computer science and logic, and it forms a central problem in research into artificial intelligence. This book contains a collection of research articles on belief revision that are right up to date and an introductory chapter that presents a survey of current research in the area and the fundamentals of the theory.

What Is Belief Revision The process of altering one's views in order to take into account a new piece of knowledge is referred to as belief revision. Philosophy, database design, and artificial intelligence are all areas of study that are contributing to research on the logical formalization of belief revision for the construction of rational beings. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Belief revision Chapter 2: Axiom Chapter 3: Deductive reasoning Chapter 4: Abductive reasoning Chapter 5: Inductive logic programming Chapter 6: Non-monotonic logic Chapter 7: Description logic Chapter 8: Dempster-Shafer theory Chapter 9: Default logic Chapter 10: Epistemic modal logic (II) Answering the public top questions about belief revision. (III) Real world examples for the usage of belief revision in many fields. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of belief revision. What is Artificial Intelligence Series The artificial intelligence book series provides comprehensive coverage in over 200 topics. Each ebook covers a specific Artificial Intelligence topic in depth, written by experts in the field. The series aims to give readers a thorough understanding of the concepts, techniques, history and applications of artificial intelligence. Topics covered include machine learning, deep learning, neural networks, computer vision, natural language processing, robotics, ethics and more. The ebooks are written for professionals, students, and anyone interested in learning about the latest developments in this rapidly advancing field. The artificial intelligence book series provides an in-depth yet accessible exploration, from the fundamental concepts to the state-of-the-art research. With over 200 volumes, readers gain a thorough grounding in all aspects of Artificial Intelligence. The ebooks are designed to build knowledge systematically, with later volumes building on the foundations laid by earlier ones. This comprehensive series is an indispensable resource for anyone seeking to develop expertise in artificial intelligence.

Frontiers in Belief Revision is a unique collection of leading edge research in Belief Revision. It contains the latest innovative ideas of highly respected and pioneering experts in the area, including Isaac Levi, Krister Segerberg, Sven Ove Hansson, Didier Dubois, and Henri Prade. The book addresses foundational issues of inductive reasoning and minimal change, generalizations of the standard belief revision theories, strategies for iterated revisions, probabilistic beliefs, multiagent environments and a variety of data structures and mechanisms for implementations. This book is suitable for students and researchers interested in knowledge representation and in the state of the art of the theory and practice of belief revision.

Conditionals are omnipresent, in everyday life as well as in scientific environments; they represent generic knowledge acquired inductively or learned from books. They tie a flexible and highly interrelated network of connections along which reasoning is possible and which can be applied to different situations. Therefore, conditionals are important, but also quite problematic objects in knowledge representation. This book presents a new approach to conditionals which captures their dynamic, non-proportional nature particularly well by considering conditionals as agents shifting possible worlds in order to establish relationships and beliefs. This understanding of conditionals yields a rich theory which makes complex interactions between conditionals transparent and operational. Moreover,it provides a unifying and enhanced framework for knowledge representation, nonmonotonic reasoning, belief revision,and even for knowledge discovery.

This work contrasts conservative or minimally mutilating revisions of empirical theories as they are identified in the presently dominant AGM model of formal belief revision and the structuralist program for the reconstruction of empirical theories. The aim is to make understandable why both approaches only partly succeed in substantially informing and formally restraining the issue. With respect to the rationality of minimal change, the overall result is negative. Readers with an interest in formal epistemology are provided with application cases (mercury anomaly, revision of early thermo-dynamics, introduction of the neutrino), the historically inclined reader is offered a systematic perspective. The discussion can largely be followed without a background in formal logic.

Belief revision theory and philosophy of science both aspire to shed light on the dynamics of knowledge – on how our view of the world changes (typically) in the light of new evidence. Yet these two areas of research have long seemed strangely detached from each other, as witnessed by the small number of cross-references and researchers working in both domains. One may speculate as to what has brought about this surprising, and perhaps unfortunate, state of affairs. One factor may be that while belief revision theory has traditionally been pursued in a bottom- up manner, focusing on the endeavors of single inquirers, philosophers of science, inspired by logical empiricism, have tended to be more interested in science as a multi-agent or agent-independent phenomenon.

An account of how a rational agent should revise beliefs in the light of new evidence. Computationally implementable, it provides rigorous mathematical theory of dependency networks and investigates the complexity of algorithms for rational agents revising beliefs.

This work develops logical theories necessary to understand adaptable human reasoning & the design ofintelligent systems. It unifies lively & significant strands of research in logic, philosophy, economics & artificial intelligence.