Tetsuya Murai - Böcker

This book discusses current topics in rough set theory. Since Pawlak’s rough set theory was first proposed to offer a basis for imprecise and uncertain data and reasoning from data, many workers have investigated its foundations and applications.

This book discusses current topics in rough set theory. Since Pawlak’s rough set theory was first proposed to offer a basis for imprecise and uncertain data and reasoning from data, many workers have investigated its foundations and applications.

This book approaches to the subject of common-sense reasoning in AI using epistemic situation calculus which integrates the ideas of situation calculus and epistemic logic. Artificial intelligence (AI) is the research area of science and engineering for intelligent machines, especially intelligent computer programs. It is very important to deal with common-sense reasoning in knowledge-based systems. If we employ a logic-based framework, classical logic is not suited for the purpose of describing common-sense reasoning. It is well known that there are several difficulties with logic-based approaches, e.g., the so-called Fame Problem. We try to formalize common-sense reasoning in the context of granular computing based on rough set theory. The book is intended for those, like experts and students, who wish to get involved in the field as a monograph or a textbook for the subject. We assume that the reader has mastered the material ordinarily covered in AI and mathematical logic

This book approaches to the subject of common-sense reasoning in AI using epistemic situation calculus which integrates the ideas of situation calculus and epistemic logic. If we employ a logic-based framework, classical logic is not suited for the purpose of describing common-sense reasoning.

This book explores reasoning with rough sets by developing a granularity-based framework. It begins with a brief description of the rough set theory, then examines selected relations between rough set theory and non-classical logics including modal logic.