Bücher von Dov M. Gabbay

Time is a fascinating subject and has long since captured mankind's imagination, from the ancients to modern man, both adult and child alike. It has been studied across a wide range of disciplines, from the natural sciences to philosophy and logic. Today, thirty plus years since Prior's work in laying out foundations for temporal logic, and two decades on from Pnueli's seminal work applying of temporal logic in specification and verification of computer programs, temporal logic has a strong and thriving international research community within the broad disciplines of computer science and artificial intelligence. Areas of activity include, but are certainly not restricted to: Pure Temporal Logic, e. g. temporal systems, proof theory, model theory, expressiveness and complexity issues, algebraic properties, application of game theory; Specification and Verification, e. g. of reactive systems, ofreal-time components, of user interaction, of hardware systems, techniques and tools for verification, execution and prototyping methods; Temporal Databases, e. g. temporal representation, temporal query- ing, granularity of time, update mechanisms, active temporal data- bases, hypothetical reasoning; Temporal Aspects in AI, e. g. modelling temporal phenomena, in- terval temporal calculi, temporal nonmonotonicity, interaction of temporal reasoning with action/knowledge/belief logics, temporal planning; Tense and Aspect in Natural Language, e. g. models, ontologies, temporal quantifiers, connectives, prepositions, processing tempo- ral statements; Temporal Theorem Proving, e. g. translation methods, clausal and non-clausal resolution, tableaux, automata-theoretic approaches, tools and practical systems.

This book opens with a lucid explanation of the theory of reactive Kripke semantics, followed by chapters demonstrating applications in a variety of areas. The book benefits both researchers and graduate students working in the field of mathematical logic.

This book covers modularity and independence in classical and nonclassical logic, including many-valued logics and structures, plus neighbourhood semantics and their connection to independence, as well as their common points and differences for various logics.

This book explores why, regarding practical reasoning, humans are sometimes still faster than artificial intelligence systems. It is the first to offer a self-contained presentation of neural network models for many computer science logics.

This book is suitable for researchers and postgraduates in the areas of artificial intelligence, database theory, and logic. The authors illustrate the idea through the presentation of various algorithms and offer the most-up-to-date book on this topic.

This book covers general logical tools for handling change. The tools are preferential reasoning, theory revision and reasoning in inheritance systems. Logics examined are nonmonotonic, deontic, modal, intuitionistic and temporal as well as counterfactuals.

From the point of view of non-classical logics, Heyting's implication is the smallest implication for which the deduction theorem holds. This book studies properties of logical systems having some of the classical connectives and implication in the neighbourhood of Heyt ing's implication.

This book regards Dung's abstract argumentation frames as a general network, comparable to other networks such as general logics neural-nets, ecology nets, electrical networks, flow networks and so on. As such the book imports general meta-logical methods and applies them to argumentation and exports ideas from argumentation to general logics and networks. Among the topics discussed are ¿ Connection with modal logic ¿ Connections with the Peirce-Quine dagger connective ¿ Combining and merging networks ¿ Bipolar networks ¿ Numerical and weighted argumentation networks ¿ The equational approach to networks ¿ Temporal aspects of networks ¿ Handling of loops ¿ Connection with logic programming ¿ Higher level attack and support The book presents many research problems suitable for students' PhD theses. This is an indispensable book for any serious researcher in abstract argumentation networks and an outstanding contribution to the future evolution of the subject.

We analyse the Klal uPrat family of textual rules in the Talmud. We view them as common-sense practical rules for defining sets. Such methods do not exist in general common-sense logical systems, and they complement the existing common-sense (non-monotonic) deductive logics.

This book returns to the discussion in volume 1 on analogy and induction,and analyses their substance. The first part distinguishes between twokinds of logic: One kind based on union of the common features, and theother kind based on synthesis of different features. In the second part ofthe book we propose a formal scheme for synthesis of concepts. The thirdpart analyses various mechanisms for kidushin and kinyan, which form amathematical group.

This monograph is an investigation into certain new logical structures implicit in the formalism of quantum mechanics.Its message rests on two pillars. The first pillar is the dynamic view of propositions. Propositions are viewed as acting on states of the world and changing them rather than just being true or false in them. The second pillar is a logical enquiry into the nature of the states of a dynamic framework in general and thus the nature of physical states in particular. It turns out that a physical state viewed as a logical entity must encode other states and also itself.The main logical structures under investigation are that of an M-algebra and that of a holistic logic. In a sense to be made precise the latter structures reflect the 'holistic' nature of quantum mechanics.

Logic for Artificial Intelligence and Information Technology is based on student notes used to teach logic to second year undergraduates and Artificial Intelligence to graduate students at the University of London since1984, first at Imperial College and later at King's College.Logic has been applied to a wide variety of subjects such as theoretical computer science, software engineering, hardware design, logic programming, computational linguistics and artificial intelligence. In this way it has served to stimulate the research for clear conceptual foundations.Over the past 20 years many extensions of classical logic such as temporal, modal, relevance, fuzzy, probabilistic and non-monotoinic logics have been widely used in computer science and artificial intelligence, therefore requiring new formulations of classical logic, which can be modified to yield the effect of the new applied logics.The text introduces classical logic in a goal directed way which can easily deviate into discussing other applied logics. It defines the many types of logics and differences between them.Dov Gabbay, FRSC, FAvH, FRSA, FBCS, is Augustus De Morgan Professor of Logic at the University of London. He has written over 300 papers in logic and over 20 books. He is Editor-in-Chief of several leading journals and has published over 50 handbooks of logic volumes. He is a world authority on applied logics and is one of the directors and founder of the UK charity the International Federation of Computational Logic

Goal Directed Proof Theory presents a uniform and coherent methodology for automated deduction in non-classical logics, the relevance of which to computer science is now widely acknowledged.

They provide the basis for the wider field of Fuzzy Logic, encompassing diverse areas such as fuzzy control, fuzzy databases, and fuzzy mathematics.

GABBA Y PART 0 AN INTRODUCTION TO GENERAL INTENSIONAL LOGICS CHAPTER 0 CONSEQUENCE RELATIONS Motivation We introduce the notions of a consequence relation (which is a generalization of the notion of a logical system) and of a semantics.

Goal Directed Proof Theory presents a uniform and coherent methodology for automated deduction in non-classical logics, the relevance of which to computer science is now widely acknowledged.

It is divided into three sections, covering the main topics of neural-symbolic integration - theoretical advances in knowledge representation and learning, knowledge extraction from trained neural networks, and inconsistency handling in neural-symbolic systems.