Reliable and plausible inference in intelligent systems
The methods of reliable (deductive) and plausible (abductive, inductive) conclusions in intelligent systems for various purposes are considered. Methods of deductive inference on graph structures are given. Both classical and nonmonotonic modal logics are described: the logic of persuasion and knowledge, the nonmonotonic logic of McDermott and Doyle, the autoepistemic logic of Moore, the logic of Reiter's silence. The fundamentals of the theory of argumentation and methods of deductive inference are given. The basic principles of building learning and decision-making systems are considered and the tasks of teaching "without a teacher" and "with a teacher" are given. Inductive methods for the case with incomplete information and methods of the theory of approximate sets are presented. The 2nd edition includes chapters on the calculus of statements and on the calculus of predicates of the first order, as well as on working with real "noisy" databases in the problem of inductive concept formation. Approved by the Ministry of Education of the Russian Federation as a textbook for university students studying in the fields of "Applied Mathematics and Computer Science", "Computer Science and Computer Engineering" and specialties "Applied Computer Science" (in the directions) and "Applied Mathematics and Computer Science". 2nd edition, revised and expanded.
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