From the Publisher:
Probabilistic Reasoning in Intelligent Systems is a complete andaccessible 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.
Probabilistic 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.

Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference

Fuzzy sets as a basis for a theory of possibility

Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier with strong assumptions of independence among features, called naive Bayes, is competitive with state-of-the-art classifiers such as C4.5. This fact raises the question of whether a classifier with less restrictive assumptions can perform even better. In this paper we evaluate approaches for inducing classifiers from data, based on the theory of learning Bayesian networks. These networks are factored representations of probability distributions that generalize the naive Bayesian classifier and explicitly represent statements about independence. Among these approaches we single out a method we call Tree Augmented Naive Bayes (TAN), which outperforms naive Bayes, yet at the same time maintains the computational simplicity (no search involved) and robustness that characterize naive Bayes. We experimentally tested these approaches, using problems from the University of California at Irvine repository, and compared them to C4.5, naive Bayes, and wrapper methods for feature selection.

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Bayesian Network Classifiers

Erik Eriksen is a remarkable individual. He has no college degrees yet is Professor of Human Development at Harvard University. He came to psychology via art, which explains why the reader will find him painting contexts and backgrounds rather than stating dull facts and concepts. He has been a training psychoanalyst for many years as well as a perceptive observer of cultural and social settings and their effect on growing up. This is not just a book on childhood. It is a panorama of our society. Anxiety in young children, apathy in American Indians, confusion in veterans of war, and arrogance in young Nazis are scrutinized under the psychoanalytic magnifying glass. The material is well written and devoid of technical jargon. The theme of the volume is that it is human to have a long childhood which will leave a lifelong residue of emotional immaturity in man. Primitive groups and

Childhood and Society.

Brooks, R.A., Intelligence without representation, Artificial Intelligence 47 (1991) 139159. Artificial intelligence research has foundered on the issue of representation. When intelligence is approached in an incremental manner, with strict reliance on interfacing to the real world through perception and action, reliance on representation disappears. In this paper we outline our approach to incrementally building complete intelligent Creatures. The fundamental decomposition of the intelligent system is not into independent information processing units which must interface with each other via representations. Instead, the intelligent system is decomposed into independent and parallel activity producers which all interface directly to the world through perception and action, rather than interface to each other particularly much. The notions of central and peripheral systems evaporateeverything is both central and peripheral. Based on these principles we have built a very successful series of mobile robots which operate without supervision as Creatures in standard office environments.

Intelligence without Representation

Computer-based medical consultations, MYCIN

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Rule-based expert systems : the MYCIN experiments of the Stanford Heuristic Programming Project

Medical science often suffers from having so few data and so much imperfect knowledge that a rigorous probabilistic analysis, the ideal standard by which to judge the rationality of a physician's decision, is seldom possible Physicians nevertheless seem to have developed an ill-defined mechanism for reaching decisions despite a lack of formal knowledge regarding the interrelationships of all the variables that they are considering This report proposes a quantification scheme which attempts to model the inexact reasoning processes of medical experts The numerical conventions provide what is essentially an approximation to conditional probability, but offer advantages over Bayesian analysis when they are utilized in a rule-based computer diagnostic system One such system, a clinical consultation program named mycin , is described in the context of the proposed model of inexact reasoning

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A model of inexact reasoning in medicine

Rule Based Expert Systems: The Mycin Experiments of the Stanford Heuristic Programming Project (The Addison-Wesley series in artificial intelligence)

Recurrent Themes in Medical Informatics: The Computer Meets Medicine and Biology: Emergence of a Discipline Medical Data: Their Acquisition, Storage, and Use Medical Decision-Making: Probabilistic Medical Reasoning Essential Concepts for Medical Computing System Design and Engineering Standards in Medical Informatics Ethics and Health Informatics: Uses, Standards, and Outcomes Evaluation and Technology Assessment * Medical Computing Applications: Computer-Based Patient-Record Systems Management of Clinical Information in Integrated Delivery Networks Public Health and Consumer Uses of Health Information: Education, Research, Policy, Prevention, and Quality Assurance Patient Care Systems Patient-Monitoring Systems Imaging Systems Information-Retrieval Systems Clinical Decision-Support Systems Computers in Medical Education Bioinformatics * Medical Informatics in the Years Ahead: Health Care and Information Technology: Growing Up Together The Future of Computer Applications in Health Care.

Edward H. Shortliffe

Papers

Computer-based medical consultations, MYCIN

Rule-based expert systems : the MYCIN experiments of the Stanford Heuristic Programming Project

A model of inexact reasoning in medicine

Rule Based Expert Systems: The Mycin Experiments of the Stanford Heuristic Programming Project (The Addison-Wesley series in artificial intelligence)

Biomedical Informatics: Computer Applications in Health Care and Biomedicine