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Morgan Kaufmann

  • Probabilistic Reasoning in Intelligent Systems

    Networks of Plausible Inference
    • 1st Edition
    • Judea Pearl
    • English
    Probabilistic Reasoning in Intelligent Systems is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty. The author provides a coherent explication of probability as a language for reasoning with partial belief and offers a unifying perspective on other AI approaches to uncertainty, such as the Dempster-Shafer formalism, truth maintenance systems, and nonmonotonic logic.The author distinguishes syntactic and semantic approaches to uncertainty--and offers techniques, based on belief networks, that provide a mechanism for making semantics-based systems operational. Specifically, network-propagation techniques serve as a mechanism for combining the theoretical coherence of probability theory with modern demands of reasoning-systems technology: modular declarative inputs, conceptually meaningful inferences, and parallel distributed computation. Application areas include diagnosis, forecasting, image interpretation, multi-sensor fusion, decision support systems, plan recognition, planning, speech recognition--in short, almost every task requiring that conclusions be drawn from uncertain clues and incomplete information.Probabil... Reasoning in Intelligent Systems will be of special interest to scholars and researchers in AI, decision theory, statistics, logic, philosophy, cognitive psychology, and the management sciences. Professionals in the areas of knowledge-based systems, operations research, engineering, and statistics will find theoretical and computational tools of immediate practical use. The book can also be used as an excellent text for graduate-level courses in AI, operations research, or applied probability.

  • Organizing Information

    Principles of Data Base and Retrieval Systems
    • 1st Edition
    • Dagobert Soergel
    • English
    This book gives a theoretical base and a perspective for the analysis, design, and operation of information systems, particularly their information storage and retrieval (ISAR) component, whether mechanized or manual. Information systems deal with many types of entities: events, persons, documents, business transactions, museum objects, research projects, and technical parts, to name a few. Among the purposes the serve are to inform the public, to support managers, researchers, and engineers, and to provide a knowledge base for an artificial intelligence program. The principles discussed in this book apply to all these contexts. The book achieves this generality by drawing on ideas from two conceptually overlapping areas—data base management and the organization and use of knowledge in libraries—and by integrating these ideas into a coherent framework. The principles discussed apply to the design of new systems and, more importantly, to the analysis of existing systems in order to exploit their capabilities better, to circumvent their shortcomings, and to introduce modifications where feasible.

  • Machine Learning

    An Artificial Intelligence Approach (Volume I)
    • 1st Edition
    • Ryszard S. Michalski + 2 more
    • English
    Machine Learning: An Artificial Intelligence Approach contains tutorial overviews and research papers representative of trends in the area of machine learning as viewed from an artificial intelligence perspective. The book is organized into six parts. Part I provides an overview of machine learning and explains why machines should learn. Part II covers important issues affecting the design of learning programs—particularl... programs that learn from examples. It also describes inductive learning systems. Part III deals with learning by analogy, by experimentation, and from experience. Parts IV and V discuss learning from observation and discovery, and learning from instruction, respectively. Part VI presents two studies on applied learning systems—one on the recovery of valuable information via inductive inference; the other on inducing models of simple algebraic skills from observed student performance in the context of the Leeds Modeling System (LMS). This book is intended for researchers in artificial intelligence, computer science, and cognitive psychology; students in artificial intelligence and related disciplines; and a diverse range of readers, including computer scientists, robotics experts, knowledge engineers, educators, philosophers, data analysts, psychologists, and electronic engineers.

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