The long-anticipated revision of this #1 selling book offers the most comprehensive, state of the art introduction to the theory and practice of artificial intelligence for modern applications. Intelligent Agents. Solving Problems by Searching. Informed Search Methods. Game Playing. Agents that Reason Logically. First-order Logic. Building a Knowledge Base. Inference in First-Order Logic. Logical Reasoning Systems. Practical Planning. Planning and Acting. Uncertainty. Probabilistic Reasoning Systems. Making Simple Decisions. Making Complex Decisions. Learning from Observations. Learning with Neural Networks. Reinforcement Learning. Knowledge in Learning. Agents that Communicate. Practical Communication in English. Perception. Robotics. For computer professionals, linguists, and cognitive scientists interested in artificial intelligence.

In a marketplace that depends so thoroughly on cutting-edge information technology, can classic economic principles still offer any real strategic value? Yes! say Carl Shapiro and Hal Varian. In Information Rules, they reveal that many conventional economic concepts can provide the insight and understanding necessary to succeed in the information age. Shapiro and Varian argue that if managers seriously want to develop effective strategies for competing in the new economy, they must understand the fundamental economics of information technology. Whether information takes the form of software code or recorded music, is published in a book or magazine, or even posted on a website, managers must know how to evaluate the consequences of pricing, protecting, and planning new versions of information products, services, and systems. The first book to distill the economics of information and networks into practical business strategies, Information Rules is a guide to the winning moves that can help business leaders-from writers, lawyers, and finance professionals to executives in the entertainment, publishing, and hardware and software industries--navigate successfully through the information economy.

Everyone makes decisions, but not everyone is a decision analyst. A decision analyst uses quantitative models and computational methods to formulate decision algorithms, assess decision performance, identify and evaluate options, determine trade-offs and risks, evaluate strategies for investigation, and so on. This book is written for decision analysts. The term 'decision analyst' covers an extremely broad range of practitioners. Virtually all engineers involved in design (of buildings, machines, processes, etc.) or analysis (of safety, reliability, feasibility, etc.) are decision analysts, usually without calling themselves by this name. In addition to engineers, decision analysts work in planning offices for public agencies, in project management consultancies, they are engaged in manufacturing process planning and control, in financial planning and economic analysis, in decision support for medical or technological diagnosis, and so on and on. Decision analysts provide quantitative support for the decision-making process in all areas where systematic decisions are made. This second edition entails changes of several sorts. First, info-gap theory has found application in several new areas - especially biological conservation, economic policy formulation, preparedness against terrorism, and medical decision-making. Pertinent new examples have been included. Second, the combination of info-gap analysis with probabilistic decision algorithms has found wide application. Consequently 'hybrid' models of uncertainty, which were treated exclusively in a separate chapter in the previous edition, now appear throughout the book as well as in a separate chapter. Finally, info-gap explanations of robust-satisficing behavior, and especially the Ellsberg and Allais 'paradoxes', are discussed in a new chapter together with a theorem indicating when robust-satisficing will have greater probability of success than direct optimizing with uncertain models. This title includes: new theory developed systematically; many examples from diverse disciplines; realistic representation of severe uncertainty; multi-faceted approach to risk; and, quantitative model-based decision theory.

After every crisis economists and policy analysts ask: can better models help prevent or ameliorate such situations? This book provides an answer. Yes, quantitative models can help if we remember that they are rough approximations to a vastly more complex reality. Models can help if we include realistic but simple representations of uncertainty among our models, and if we retain the pre-eminence of human judgment over the churning of our computers. Info-gap theory is a new method for modeling and managing severe uncertainty. The core of the book presents detailed examples of info-gap analysis of decisions in monetary policy, financial economics, environmental economics for pollution control and climate change, estimation and forecasting. This book is essential reading for economic policy analysts and researchers.