This second volume is arranged in four sections: Analysis contains papers which compare the attributes of various approaches to uncertainty. Tools provides sufficient information for the reader to implement uncertainty calculations. Papers in the Theory section explain various approaches to uncertainty. The Applications section describes the difficulties involved in, and the results produced by, incorporating uncertainty into actual systems.

This book develops a framework that shows how uncertainty in Artificial Intelligence (AI) expands and generalizes traditional AI. It explores the uncertainties of knowledge and intelligence. The authors focus on the importance of natural language – the carrier of knowledge and intelligence, and introduce efficient physical methods for data mining amd control. In this new edition, we have more in-depth description of the models and methods, of which the mathematical properties are proved strictly which make these theories and methods more complete. The authors also highlight their latest research results.

Hardbound. How to deal with uncertainty is a subject of much controversy in Artificial Intelligence. This volume brings together a wide range of perspectives on uncertainty, many of the contributors being the principal proponents in the controversy.Some of the notable issues which emerge from these papers revolve around an interval-based calculus of uncertainty, the Dempster-Shafer Theory, and probability as the best numeric model for uncertainty. There remain strong dissenting opinions not only about probability but even about the utility of any numeric method in this context.

Artificial Intelligence presents a practical guide to AI, including agents, machine learning and problem-solving simple and complex domains.

Formal ways of representing uncertainty and various logics for reasoning about it; updated with new material on weighted probability measures, complexity-theoretic considerations, and other topics. In order to deal with uncertainty intelligently, we need to be able to represent it and reason about it. In this book, Joseph Halpern examines formal ways of representing uncertainty and considers various logics for reasoning about it. While the ideas presented are formalized in terms of definitions and theorems, the emphasis is on the philosophy of representing and reasoning about uncertainty. Halpern surveys possible formal systems for representing uncertainty, including probability measures, possibility measures, and plausibility measures; considers the updating of beliefs based on changing information and the relation to Bayes' theorem; and discusses qualitative, quantitative, and plausibilistic Bayesian networks. This second edition has been updated to reflect Halpern's recent research. New material includes a consideration of weighted probability measures and how they can be used in decision making; analyses of the Doomsday argument and the Sleeping Beauty problem; modeling games with imperfect recall using the runs-and-systems approach; a discussion of complexity-theoretic considerations; the application of first-order conditional logic to security. Reasoning about Uncertainty is accessible and relevant to researchers and students in many fields, including computer science, artificial intelligence, economics (particularly game theory), mathematics, philosophy, and statistics.

Innovation is how businesses stay ahead of the competition and adapt to market conditions that change in unpredictable and uncertain ways. In the first decade of the twenty-first century, high-end cuisine underwent a profound transformation. Once an industry that prioritized consistency and reliability, it turned into one where constant change was a competitive necessity. A top restaurant’s reputation and success have become so closely bound up with its ability to innovate that a new organizational form, the culinary research and development team, has emerged. The best of these R&D teams continually expand the frontiers of food—they invent a constant stream of new dishes, new cooking processes and methods, and even new ways of experiencing food. How do they achieve this nonstop novelty? And what can culinary research and development teach us about how organizations innovate? Vaughn Tan opens up the black box of elite culinary R&D to provide essential insights. Drawing on years of unprecedented access to the best and most influential culinary R&D teams in the world, he reveals how they exemplify what he calls the uncertainty mindset. Such a mindset intentionally incorporates uncertainty into organization design rather than simply trying to reduce risk. It changes how organizations hire, set goals, and motivate team members and leads organizations to work in highly unconventional ways. A revelatory look at the R&D kitchen, The Uncertainty Mindset upends conventional wisdom about how to organize for innovation and offers practical insights for businesses trying to become innovative and adaptable.