Problems Of Reducing The Exhaustive Search

This collection contains translations of papers on propositional satisfiability and related logical problems which appeared in Problemy Sokrashcheniya Perebora, published in Russian in 1987 by the Scientific Council ``Cybernetics'' of the USSR Academy of Sciences. The problems form the nucleus of this intensively developing area. This translation is dedicated to the memory of two remarkable Russian mathematicians, Sergei Maslov and his wife, Nina Maslova. Maslov is known as the originator of the inverse method in automated deduction, which was discovered at the same time as the resolution method of J. A. Robinson and has approximately the same range of applications. In 1981, Maslov proposed an iterative algorithm for propositional satisfiability based on some general ideas of search described in detail in his posthumously published book, Theory of Deductive Systems and Its Applications (1986; English 1987). This collection contains translations of papers on proposititional satisfiability and related logical problems. The papers related to Maslov's iterative method of search reduction play a significant role.

First Published in 1987. Information processing has come to be regarded as one of the central issues in modern psychology. In recent years it has acquired an especially keen interest due to growing amounts of information. This book continues the authors’ research and constructs a conceptual model of peculiarities (separate aspects) of visual information processing based on views similar to those by Arbib and Ivanov.

The analysis and control of complex systems have been the main motivation for the emergence of fuzzy set theory since its inception. It is also a major research field where many applications, especially industrial ones, have made fuzzy logic famous. This unique handbook is devoted to an extensive, organized, and up-to-date presentation of fuzzy systems engineering methods. The book includes detailed material and extensive bibliographies, written by leading experts in the field, on topics such as: Use of fuzzy logic in various control systems. Fuzzy rule-based modeling and its universal approximation properties. Learning and tuning techniques for fuzzy models, using neural networks and genetic algorithms. Fuzzy control methods, including issues such as stability analysis and design techniques, as well as the relationship with traditional linear control. Fuzzy sets relation to the study of chaotic systems, and the fuzzy extension of set-valued approaches to systems modeling through the use of differential inclusions. Fuzzy Systems: Modeling and Control is part of The Handbooks of Fuzzy Sets Series. The series provides a complete picture of contemporary fuzzy set theory and its applications. This volume is a key reference for systems engineers and scientists seeking a guide to the vast amount of literature in fuzzy logic modeling and control.

This book addresses an intriguing question: are our decisions rational? It explains seemingly irrational human decision-making behavior by taking into account our limited ability to process information. It also shows with several examples that optimization under granularity restriction leads to observed human decision-making. Drawing on the Nobel-prize-winning studies by Kahneman and Tversky, researchers have found many examples of seemingly irrational decisions: e.g., we overestimate the probability of rare events. Our explanation is that since human abilities to process information are limited, we operate not with the exact values of relevant quantities, but with “granules” that contain these values. We show that optimization under such granularity indeed leads to observed human behavior. In particular, for the first time, we explain the mysterious empirical dependence of betting odds on actual probabilities. This book can be recommended to all students interested in human decision-making, to researchers whose work involves human decisions, and to practitioners who design and employ systems involving human decision-making —so that they can better utilize our ability to make decisions under uncertainty.

This book constitutes the refereed proceedings of the 17th Brazilian Symposium on Artificial Intelligence, SBIA 2004, held in Sao Luis, Maranhao, Brazil in September/October 2004. The 54 revised full papers presented were carefully reviewed and selected from 208 submissions from 21 countries. The papers are organized in topical sections on logics, planning, and theoretical methods; search, reasoning, and uncertainty; knowledge representation and ontologies; natural language processing; machine learning, knowledge discovery and data mining; evolutionary computing, artificial life, and hybrid systems; robotics and compiler vision; and autonomous agents and multi-agent systems.

This collection contains translations of papers on propositional satisfiability and related logical problems which appeared in roblemy Sokrashcheniya Perebora, published in Russian in 1987 by the Scientific Council "Cybernetics" of the USSR Academy of Sciences. The problems form the nucleus of this intensively developing area. This translation is dedicated to the memory of two remarkable Russian mathematicians, Sergei Maslov and his wife Nina Maslova. Maslov is known as the originator of the universe method in automated deduction, which was discovered at the same time as the resolution method of J. A. Robison and has approximately the same range of applications. In 1981, Maslov proposed an iterative algorithm for propositional satisfiability based on some general ideas of search described in detail in his posthumously published book, Theory of Deductive Systems and Its Applications (1986; English 1987). This collection contains translations of papers on propositional satisfiability and related logical problems. The papers related to Maslov's iterative method of search reduction play a significant role.

Planning is among the characteristic features of intelligence and therefore it is a central research topic of Intellectics since its beginning. Although planning is a very hard task, recent planning systems have achieved an astonishing performance and are applied in various fields. One reason for the success of these systems lies, among others, in the exploitation of structural properties that are present in many but not all problems. The use of such structural properties therefore leads to a specialization on a class of problems. Their exploitation is often conducted by a preprocessing step, i.e., by the application of a special algorithm prior to the search for a plan. This work identifies and examines the class of c-invariants as such a structural property of planning problems. c-Invariants are state invariants and are present in many problems of practical interest. Building on the features of c-invariants, the dissertation presents path reduction, a preprocessing technique that can significantly simplify planning problems. Finally, the work describes an implementation of path reduction and examines its application.