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| Author | : Jürg Kohlas |
| Publisher | : |
| Total Pages | : 36 |
| Release | : 1987 |
| Genre | : |
| ISBN | : |
| Author | : Jürg Kohlas |
| Publisher | : |
| Total Pages | : 76 |
| Release | : 1987 |
| Genre | : |
| ISBN | : |
| Author | : Kurt Weichselberger |
| Publisher | : Lecture Notes in Artificial Intelligence |
| Total Pages | : 154 |
| Release | : 1990-03-07 |
| Genre | : Computers |
| ISBN | : |
In this book the consequent use of probability theory is proposed for handling uncertainty in expert systems. It is shown that methods violating this suggestion may have dangerous consequences (e.g., the Dempster-Shafer rule and the method used in MYCIN). The necessity of some requirements for a correct combining of uncertain information in expert systems is demonstrated and suitable rules are provided. The possibility is taken into account that interval estimates are given instead of exact information about probabilities. For combining information containing interval estimates rules are provided which are useful in many cases.
| Author | : David C. Knue |
| Publisher | : |
| Total Pages | : 109 |
| Release | : 1986 |
| Genre | : Expert systems (Computer science) |
| ISBN | : |
A study of using probability to manage uncertainty in expert systems is presented. The study begins with a comprehensive summary of the literature on applying numeric techniques to manage uncertainty in expert systems. In addition to probability, fuzzy sets, certainty factors, and belief functions are addressed. basic principles and rules of information combination for each technique are discussed. The Lindley scoring rule argument for why probability is mathematically techniques is reviewed. The issues why using probability is considered to be a hindrance to managing uncertainty in expert systems are also reviewed. A simple expert system is developed using a state of the art expert system building tool called ALTERID. ALTERID is unique in that it unifies logical and probabilistic inference. This simple expert system is used to explore how probability theory can be used to manage the uncertainty in expert systems. The simple ALTERID based expert system is also used to evaluate the aforementioned issues for using probability to manage uncertainty in expert systems. Keywords: artificial intelligence Bayes theorem; decision analysis; theses.
| Author | : Roger M. Cooke |
| Publisher | : Oxford University Press |
| Total Pages | : 334 |
| Release | : 1991-10-24 |
| Genre | : Science |
| ISBN | : 0195362373 |
This book is an extensive survey and critical examination of the literature on the use of expert opinion in scientific inquiry and policy making. The elicitation, representation, and use of expert opinion is increasingly important for two reasons: advancing technology leads to more and more complex decision problems, and technologists are turning in greater numbers to "expert systems" and other similar artifacts of artificial intelligence. Cooke here considers how expert opinion is being used today, how an expert's uncertainty is or should be represented, how people do or should reason with uncertainty, how the quality and usefulness of expert opinion can be assessed, and how the views of several experts might be combined. He argues for the importance of developing practical models with a transparent mathematic foundation for the use of expert opinion in science, and presents three tested models, termed "classical," "Bayesian," and "psychological scaling." Detailed case studies illustrate how they can be applied to a diversity of real problems in engineering and planning.
| Author | : Didier Dubois |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 274 |
| Release | : 2012-12-06 |
| Genre | : Mathematics |
| ISBN | : 1468452878 |
In the evolution of scientific theories, concern with uncertainty is almost invariably a concomitant of maturation. This is certainly true of the evolution· of physics, economics, operations research, communication sciences, and a host of other fields. And it is true of what has been happening more recently in the area of artificial intelligence, most notably in the development of theories relating to the management of uncertainty in knowledge-based systems. In science, it is traditional to deal with uncertainty through the use of probability theory. In recent years, however, it has become increasingly clear that there are some important facets of uncertainty which do not lend themselves to analysis by classical probability-based methods. One such facet is that of lexical elasticity, which relates to the fuzziness of words in natural languages. As a case in point, even a simple relation X, Y, and Z, expressed as if X is small and Y is very large then between Z is not very small, does not lend itself to a simple interpretation within the framework of probability theory by reason of the lexical elasticity of the predicates small and large.
| Author | : Joseph Y. Halpern |
| Publisher | : MIT Press |
| Total Pages | : 505 |
| Release | : 2017-04-07 |
| Genre | : Computers |
| ISBN | : 0262533804 |
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.
| Author | : Irwin R. Goodman |
| Publisher | : North Holland |
| Total Pages | : 706 |
| Release | : 1985 |
| Genre | : Computers |
| ISBN | : |
| Author | : Perry Lee McCarty |
| Publisher | : |
| Total Pages | : 500 |
| Release | : 1987 |
| Genre | : Dissertations, Academic |
| ISBN | : |
| Author | : Cornelius T. Leondes |
| Publisher | : Elsevier |
| Total Pages | : 2125 |
| Release | : 2001-09-26 |
| Genre | : Computers |
| ISBN | : 0080531458 |
This six-volume set presents cutting-edge advances and applications of expert systems. Because expert systems combine the expertise of engineers, computer scientists, and computer programmers, each group will benefit from buying this important reference work. An "expert system" is a knowledge-based computer system that emulates the decision-making ability of a human expert. The primary role of the expert system is to perform appropriate functions under the close supervision of the human, whose work is supported by that expert system. In the reverse, this same expert system can monitor and double check the human in the performance of a task. Human-computer interaction in our highly complex world requires the development of a wide array of expert systems. Expert systems techniques and applications are presented for a diverse array of topics including Experimental design and decision support The integration of machine learning with knowledge acquisition for the design of expert systems Process planning in design and manufacturing systems and process control applications Knowledge discovery in large-scale knowledge bases Robotic systems Geograhphic information systems Image analysis, recognition and interpretation Cellular automata methods for pattern recognition Real-time fault tolerant control systems CAD-based vision systems in pattern matching processes Financial systems Agricultural applications Medical diagnosis
