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Intellectics and Computational Logic
"Audience: Researchers working in the areas of Intellectics and Computational Logic, who are convinced that an intelligent behavior of machines should be based on a rigid formal treatment of knowledge representation and reasoning."--Jacket.
Theory Reasoning in Connection Calculi
The ability to draw inferences is a central operation in any artificial intelligence system. Automated reasoning is therefore among the traditional disciplines in AI. Theory reasoning is about techniques for combining automated reasoning systems with specialized and efficient modules for handling domain knowledge called background reasoners. Connection methods have proved to be a good choice for implementing high-speed automated reasoning systems. They are the starting point in this monograph,in which several theory reasoning versions are defined and related to each other. A major contribution of the book is a new technique of linear completion allowing for the automatic construction of background reasoners from a wide range of axiomatically given theories. The emphasis is on theoretical investigations, but implementation techniques based on Prolog are also covered.
Foundations of Knowledge Base Management
In the past, applied artificial intelligence systems were built with particular emphasis on general reasoning methods intended to function efficiently, even when only relatively little domain-specific knowledge was available. In other words, AI technology aimed at the processing of knowledge stored under comparatively general representation schemes. Nowadays, the focus has been redirected to the role played by specific and detailed knowledge, rather than to the reasoning methods themselves. Many new application systems are centered around knowledge bases, i. e. , they are based on large collections offacts, rules, and heuristics that cap ture knowledge about a specific domain of applications...
The Death of Argument
The present work is a fair record of work I've done on the fallacies and related matters in the fifteen years since 1986. The book may be seen as a sequel to Fallacies: Selected papers 1972-1982, which I wrote with Douglas Walton, and which appeared in 1989 with Foris. This time I am on my own. Douglas Walton has, long since, found his own voice, as the saying has it; and so have I. Both of us greatly value the time we spent performing duets, but we also recognize the attractions of solo work. If I had to characterize the difference that has manifested itself in our later work, I would venture that Walton has strayed more, and I less, from what has come to be called the Woods-Walton Approach...
Logical and Computational Aspects of Model-Based Reasoning
Information technology has been, in recent years, under increasing commercial pressure to provide devices and systems which help/ replace the human in his daily activity. This pressure requires the use of logic as the underlying foundational workhorse of the area. New logics were developed as the need arose and new foci and balance has evolved within logic itself. One aspect of these new trends in logic is the rising impor tance of model based reasoning. Logics have become more and more tailored to applications and their reasoning has become more and more application dependent. In fact, some years ago, I myself coined the phrase "direct deductive reasoning in application areas", advocating the methodology of model-based reasoning in the strongest possible terms. Certainly my discipline of Labelled Deductive Systems allows to bring "pieces" of the application areas as "labels" into the logic. I therefore heartily welcome this important book to Volume 25 of the Applied Logic Series and see it as an important contribution in our overall coverage of applied logic.
Labelled Deduction
Labelled deduction is an approach to providing frameworks for presenting and using different logics in a uniform and natural way by enriching the language of a logic with additional information of a semantic proof-theoretical nature. Labelled deduction systems often possess attractive properties, such as modularity in the way that families of related logics are presented, parameterised proofs of metatheoretic properties, and ease of mechanisability. It is thus not surprising that labelled deduction has been applied to problems in computer science, AI, mathematical logic, cognitive science, philosophy and computational linguistics - for example, formalizing and reasoning about dynamic `state oriented' properties such as knowledge, belief, time, space, and resources.
The Semantics and Proof Theory of the Logic of Bunched Implications
This is a monograph about logic. Specifically, it presents the mathe matical theory of the logic of bunched implications, BI: I consider Bl's proof theory, model theory and computation theory. However, the mono graph is also about informatics in a sense which I explain. Specifically, it is about mathematical models of resources and logics for reasoning about resources. I begin with an introduction which presents my (background) view of logic from the point of view of informatics, paying particular attention to three logical topics which have arisen from the development of logic within informatics: • Resources as a basis for semantics; • Proof-search as a basis for reasoning; and • The ...
A Modern Perspective on Type Theory
This book provides an overview of type theory. The first part of the book is historical, yet at the same time, places historical systems in the modern setting. The second part deals with modern type theory as it developed since the 1940s, and with the role of propositions as types (or proofs as terms. The third part proposes new systems that bring more advantages together.
Abduction and Induction
From the very beginning of their investigation of human reasoning, philosophers have identified two other forms of reasoning, besides deduction, which we now call abduction and induction. Deduction is now fairly well understood, but abduction and induction have eluded a similar level of understanding. The papers collected here address the relationship between abduction and induction and their possible integration. The approach is sometimes philosophical, sometimes that of pure logic, and some papers adopt the more task-oriented approach of AI. The book will command the attention of philosophers, logicians, AI researchers and computer scientists in general.
Artificial Intelligence Today
Artificial Intelligence is one of the most fascinating and unusual areas of academic study to have emerged this century. For some, AI is a true scientific discipline, that has made important and fundamental contributions to the use of computation for our understanding of nature and phenomena of the human mind; for others, AI is the black art of computer science. Artificial Intelligence Today provides a showcase for the field of AI as it stands today. The editors invited contributions both from traditional subfields of AI, such as theorem proving, as well as from subfields that have emerged more recently, such as agents, AI and the Internet, or synthetic actors. The papers themselves are a mixture of more specialized research papers and authorative survey papers. The secondary purpose of this book is to celebrate Springer-Verlag's Lecture Notes in Artificial Intelligence series.
