If you are responsible for the management of an intelligence enterprise operation and its timely and accurate delivery of reliable intelligence to key decision-makers, this book is must reading. It is the first easy-to-understand, system-level book that specifically applies knowledge management principles, practices and technologies to the intelligence domain. The book describes the essential principles of intelligence, from collection, processing and analysis, to dissemination for both national intelligence and business applications.

The book includes all the background material required to understand the principles underlying intelligence, as well as enough detailed information on intelligent robotics and simulated agents so readers can begin experiments and projects on their own. By the mid-1980s researchers from artificial intelligence, computer science, brain and cognitive science, and psychology realized that the idea of computers as intelligent machines was inappropriate. The brain does not run "programs"; it does something entirely different. But what? Evolutionary theory says that the brain has evolved not to do mathematical proofs but to control our behavior, to ensure our survival. Researchers now agree that intelligence always manifests itself in behavior—thus it is behavior that we must understand. An exciting new field has grown around the study of behavior-based intelligence, also known as embodied cognitive science, "new AI," and "behavior-based AI." This book provides a systematic introduction to this new way of thinking. After discussing concepts and approaches such as subsumption architecture, Braitenberg vehicles, evolutionary robotics, artificial life, self-organization, and learning, the authors derive a set of principles and a coherent framework for the study of naturally and artificially intelligent systems, or autonomous agents. This framework is based on a synthetic methodology whose goal is understanding by designing and building. The book includes all the background material required to understand the principles underlying intelligence, as well as enough detailed information on intelligent robotics and simulated agents so readers can begin experiments and projects on their own. The reader is guided through a series of case studies that illustrate the design principles of embodied cognitive science.

Issues raised by the Theory of Knowledge, a central theme in the development of Artificial Intelligence, are the main topic of this book. The major questions are: How is the expert's knowledge to be elicited, what are the limits and possibilities? How can skill be developed and maintained in a more and more computerized and abstract working life? This last question is also closely related to the discussion on programs for education and training in society and working life. Long term effects on skill formation in working life in relation to new technology are a very important area of research. Case studies form the basis for philosophical reflections with the main concept of tacit knowledge as the central issue of skill and new technology. To a great extent the discussion is based on current case studies of professional groups with experience in advanced computer technology. The contributions of this book demonstrate the complicated nature of human knowledge. They introduce different theoretical perspectives on the issue of knowledge acquisition and elicitation.

Artificial Intelligence (AI) fascinates, challenges and disturbs us. There are many voices in society that predict drastic changes that may come as a consequence of AI – a possible apocalypse or Eden on earth. However, only a few people truly understand what AI is, what it can do and what its limitations are. Understanding Artificial Intelligence explains, through a straightforward narrative and amusing illustrations, how AI works. It is written for a non-specialist reader, adult or adolescent, who is interested in AI but is missing the key to understanding how it works. The author demystifies the creation of the so-called "intelligent" machine and explains the different methods that are used in AI. It presents new possibilities offered by algorithms and the difficulties that researchers, engineers and users face when building and using such algorithms. Each chapter allows the reader to discover a new aspect of AI and to become fully aware of the possibilities offered by this rich field.

Strategic intelligence (SI) has mostly been used in military settings, but its worth goes well beyond that limited role. It has become invaluable for improving any organization's strategic decision making process. The author of Strategic Intelligence: Business Intelligence, Competitive Intelligence, and Knowledge Management recognizes synergies amo

A collection of fourteen essays which provide an overview of the argument for intelligent design, with diagrams, explanations, and relevant quotations.

"This work examines the many roadblocks the Intelligence Community confronts as it attempts to provide accurate and timely intelligence in support of American foreign policy and national security interests. Kennedy's goal is to meet the needs of policymakers and, at the same time, to provide the average American, as well as students of foreign and security policy, with a more comprehensive understanding of the overall intelligence effort."--Résumé de l'éditeur

Knowledge representation is at the very core of a radical idea for understanding intelligence. This book talks about the central concepts of knowledge representation developed over the years. It is suitable for researchers and practitioners in database management, information retrieval, object-oriented systems and artificial intelligence.

In the Handbook of Understanding and Measuring Intelligence distinguished scholars Oliver Wilhelm and Randall W. Engle have assembled a group of respected experts from two fields of intelligence research--cognition and methods--to summarize, review, and evaluate research in their areas of expertise. Each chapter presents the state-of-the-art in a particular domain of intelligence research, illustrating and highlighting important methodological considerations, theoretical claims, and pervasive problems in the field.

Handbook of Knowledge Representation describes the essential foundations of Knowledge Representation, which lies at the core of Artificial Intelligence (AI). The book provides an up-to-date review of twenty-five key topics in knowledge representation, written by the leaders of each field. It includes a tutorial background and cutting-edge developments, as well as applications of Knowledge Representation in a variety of AI systems. This handbook is organized into three parts. Part I deals with general methods in Knowledge Representation and reasoning and covers such topics as classical logic in Knowledge Representation; satisfiability solvers; description logics; constraint programming; conceptual graphs; nonmonotonic reasoning; model-based problem solving; and Bayesian networks. Part II focuses on classes of knowledge and specialized representations, with chapters on temporal representation and reasoning; spatial and physical reasoning; reasoning about knowledge and belief; temporal action logics; and nonmonotonic causal logic. Part III discusses Knowledge Representation in applications such as question answering; the semantic web; automated planning; cognitive robotics; multi-agent systems; and knowledge engineering. This book is an essential resource for graduate students, researchers, and practitioners in knowledge representation and AI. * Make your computer smarter* Handle qualitative and uncertain information* Improve computational tractability to solve your problems easily