Nils J. Nilsson – författare

What beliefs are, what they do for us, how we come to hold them, and how to evaluate them. Our beliefs constitute a large part of our knowledge of the world. We have beliefs about objects, about culture, about the past, and about the future. We have beliefs about other people, and we believe that they have beliefs as well. We use beliefs to predict, to explain, to create, to console, to entertain. Some of our beliefs we call theories, and we are extraordinarily creative at constructing them. Theories of quantum mechanics, evolution, and relativity are examples. But so are theories about astrology, alien abduction, guardian angels, and reincarnation. All are products (with varying degrees of credibility) of fertile minds trying to find explanations for observed phenomena. In this book, Nils Nilsson examines beliefs: what they do for us, how we come to hold them, and how to evaluate them. We should evaluate our beliefs carefully, Nilsson points out, because they influence so many of our actions and decisions.Some of our beliefs are more strongly held than others, but all should be considered tentative and changeable. Nilsson shows that beliefs can be quantified by probability, and he describes networks of beliefs in which the probabilities of some beliefs affect the probabilities of others. He argues that we can evaluate our beliefs by adapting some of the practices of the scientific method and by consulting expert opinion. And he warns us about "belief traps"—holding onto beliefs that wouldn't survive critical evaluation. The best way to escape belief traps, he writes, is to expose our beliefs to the reasoned criticism of others.

Artificial intelligence (AI) is a field within computer science that is attempting to build enhanced intelligence into computer systems. This book traces the history of the subject, from the early dreams of eighteenth-century (and earlier) pioneers to the more successful work of today's AI engineers. AI is becoming more and more a part of everyone's life. The technology is already embedded in face-recognizing cameras, speech-recognition software, Internet search engines, and health-care robots, among other applications. The book's many diagrams and easy-to-understand descriptions of AI programs will help the casual reader gain an understanding of how these and other AI systems actually work. Its thorough (but unobtrusive) end-of-chapter notes containing citations to important source materials will be of great use to AI scholars and researchers. This book promises to be the definitive history of a field that has captivated the imaginations of scientists, philosophers, and writers for centuries.

Artificial intelligence (AI) is a field within computer science that is attempting to build enhanced intelligence into computer systems. This book traces the history of the subject, from the early dreams of eighteenth-century (and earlier) pioneers to the more successful work of today's AI engineers. AI is becoming more and more a part of everyone's life. The technology is already embedded in face-recognizing cameras, speech-recognition software, Internet search engines, and health-care robots, among other applications. The book's many diagrams and easy-to-understand descriptions of AI programs will help the casual reader gain an understanding of how these and other AI systems actually work. Its thorough (but unobtrusive) end-of-chapter notes containing citations to important source materials will be of great use to AI scholars and researchers. This book promises to be the definitive history of a field that has captivated the imaginations of scientists, philosophers, and writers for centuries.

Intelligent agents are employed as the central characters in this introductory text. Beginning with elementary reactive agents, Nilsson gradually increases their cognitive horsepower to illustrate the most important and lasting ideas in AI. Neural networks, genetic programming, computer vision, heuristic search, knowledge representation and reasoning, Bayes networks, planning, and language understanding are each revealed through the growing capabilities of these agents. A distinguishing feature of this text is in its evolutionary approach to the study of AI. This book provides a refreshing and motivating synthesis of the field by one of AI's master expositors and leading researches. An evolutionary approach provides a unifying theme Thorough coverage of important AI ideas, old and new Frequent use of examples and illustrative diagrams Extensive coverage of machine learning methods throughout the text Citations to over 500 references Comprehensive index

Dieses Buch basiert auf zwei zentralen Annahmen: Für den wissen­ schaftlichen und technischen Fortschritt einer wissenschaftlichen Disziplin ist erstens ein geeignetes mathematisches Handwerkszeug zur Formulierung und Zusammenfassung neuer Ideen nötig. Zweitens ist die symbolische Logik ein sehr wesentlicher Bestandteil der in der Forschung über Künstliche Intelligenz (KI) verwendeten Mathe­ matik. Beide Behauptungen müssen begründet werden. Man sollte allerdings meinen, unser erster Grundsatz fände ei­ gentlich allgemeine Zustimmung. Dennoch gibt es in neuen Wissen­ schaftsgebieten, in denen das Wissen hauptsächlich an die Praxis und empirische Fallstudien gebunden ist, vehemente Einwände gegen die Versuche einer Mathematisierung. (Einer der Autoren erinnert sich beispielsweise daran, wie sich in den 50-er Jahren einige Elektroingenieure darüber beklagten, daß zum Verständnis von elek­ trischen Schaltkreisen und Kontrollsystemen Differentialglei­ chungen doch völlig unnötig seinen!) Wir behaupten nicht, daß das Wissen um die mathematischen Grundlagen und Techniken einer Diszi­ plin allein ausreicht, um in der Forschung oder in der Praxis er­ folgreich zu sein. Wir sind allerdings der Meinung, daß zu einem VIII Vorwort erfolgreichem Studium der modernen, insbesonders technisch orien­ tierten Wissenschaftsdisziplinen immer auch ein solides mathema­ tisches Handwerkszeug der jeweiligen Disziplin gehört. Das Studium dieser Grundlagen bietet die Voraussetzungen, um die jeweilige Disziplin interpretieren, verstehen und ausbauen zu können. Da die KI eine noch relativ junge Disziplin ist, ist es nicht verwunderlich, daß es hitzige und geistreiche Debatten zwischen "Formalisten" und "Experimentalisten" gibt. Die Formalisten mei­ nen, dieExperimentalisten kämen schneller voran, wenn sie ein tieferes Verständnis der theoretischen Grundlagen der KI besäßen.

Contents: Prologue. - Production Systems and AI. - SearchStrategies for AI Production Systems. - Search Strategiesfor Decomposable Production Systems.- The PredicateCalculus in AI. - Resolution Refutation Systems. -Rule-Based Deduction Systems. - Basic-Plan-GeneratingSystems. - Advanced Plan-Generating Systems. - StructuredObject Representations. - Prospectus. - Bibliography. -Author Index. - Subject Index