A Many-Valued Approach to Deduction and Reasoning for Artificial Intelligence


Book Description

This book introduces an approach that can be used to ground a variety of intelligent systems, ranging from simple fact based systems to highly sophisticated reasoning systems. As the popularity of AI related fields has grown over the last decade, the number of persons interested in building intelligent systems has increased exponentially. Some of these people are highly skilled and experienced in the use of Al techniques, but many lack that kind of expertise. Much of the literature that might otherwise interest those in the latter category is not appreci ated by them because the material is too technical, often needlessly so. The so called logicists see logic as a primary tool and favor a formal approach to Al, whereas others are more content to rely on informal methods. This polarity has resulted in different styles of writing and reporting, and people entering the field from other disciplines often find themselves hard pressed to keep abreast of current differences in style. This book attempts to strike a balance between these approaches by covering points from both technical and nontechnical perspectives and by doing so in a way that is designed to hold the interest of readers of each persuasion. During recent years, a somewhat overwhelming number of books that present general overviews of Al related subjects have been placed on the market . These books serve an important function by providing researchers and others entering the field with progress reports and new developments.







Many-Valued Logics 2


Book Description

Many-valued logics are becoming increasingly important in all areas of computer science. This is the second volume of an authoritative two-volume handbook on many valued logics by two leading figures in the field. While the first volume was mainly concerned with theoretical foundations, this volume emphasizes automated reasoning, practical applications, and the latest developments in fuzzy logic and rough set theory. Among the applications presented are those in software specification and electronic circuit verification.




Tort Theory


Book Description




Epistemic Situation Calculus Based on Granular Computing


Book Description

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




Automated Reasoning with Analytic Tableaux and Related Methods


Book Description

This book constitutes the refereed proceedings of the 16th International Conference on Automated Reasoning with Analytic Tableaux and Related Methods, TABLEAUX 2007, held in Aix en Provence, France. It covers the wide range of logics, from intuitionistic and substructural logics to modal logics (including temporal and dynamic logics), from many-valued logics to nonmonotonic logics, and from classical first-order logic to description logics.




Automated Deduction in Multiple-valued Logics


Book Description

A notation called sets-as-signs is developed, and then it is demonstrated how it can be used to modify any known inference method to handle many-valued logics. Applications are discussed, both in pure mathematics, and in hardware verification and interval arithmetic. Concludes with a historical overview of activities in many-valued theorem proving. Annotation copyright by Book News, Inc., Portland, OR




Artificial Intelligence and Symbolic Computation


Book Description

AISC 2004, the 7th International Conference on Artificial Intelligence and Symbolic Computation, was the latest in the series of specialized biennial conferences founded in 1992 by Jacques Calmet of the Universitat ̈ Karlsruhe and John Campbell of University College London with the initial title Artificial Intelligence and Symbolic Mathematical Computing (AISMC).The M disappeared from the title between the 1996 and 1998 conferences. As the editors of the AISC 1998 proceedings said, the organizers of the current meeting decided to drop the adjective 'mathematical' and to emphasize that the conference is concerned with all aspects of symbolic computation in AI: mathematical foundations, implementations, and applications, including applications in industry and academia. This remains the intended profile of the series, and will figure in the call for papers for AISC 2006,which is intended to take place in China. The distribution of papers in the present volume over all the areas of AISC happens to be rather noticeably mathematical, an effect that emerged because we were concerned to select the best relevant papers that were offered to us in 2004, irrespective of their particular topics; hence the title on the cover. Nevertheless, we encourage researchers over the entire spectrum of AISC, as expressed by the 1998 quotation above,to be in touch with us about their interests and the possibility of eventual submission of papers on their work for the next conference in the series. The papers in the present volume are evidence of the health of the field of AISC. Additionally, there are two reasons for optimism about the continuation of this situation.




Deductive Reasoning and Strategies


Book Description

This book brings together both theoretical and empirical research directed toward the role of strategies in deductive reasoning. It offers the first systematic attempt to discuss the role of strategies for deductive reasoning. The empirical chapters correspond well with the main issues in the study of deduction, namely propositional reasoning, spatial reasoning, and syllogistic reasoning. In addition, several chapters present a theoretical analysis of deduction, related to the concept strategy. The book also presents data about the role of strategies for statistical and social reasoning. This book will be of interest to researchers and students of cognitive psychology. It will also be of value to people working in Artificial Intelligence, because it highlights results on how humans use strategies while tackling deductive puzzles.