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

The architecture and learning procedure underlying ANFIS (adaptive-network-based fuzzy inference system) is presented, which is a fuzzy inference system implemented in the framework of adaptive networks. By using a hybrid learning procedure, the proposed ANFIS can construct an input-output mapping based on both human knowledge (in the form of fuzzy if-then rules) and stipulated input-output data pairs. In the simulation, the ANFIS architecture is employed to model nonlinear functions, identify nonlinear components on-line in a control system, and predict a chaotic time series, all yielding remarkable results. Comparisons with artificial neural networks and earlier work on fuzzy modeling are listed and discussed. Other extensions of the proposed ANFIS and promising applications to automatic control and signal processing are also suggested. >

ANFIS: adaptive-network-based fuzzy inference system

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Data Mining Practical Machine Learning Tools and Techniques

Fuzzy Set Theory - And Its Applications, Third Edition is a textbook for courses in fuzzy set theory. It can also be used as an introduction to the subject. The character of a textbook is balanced with the dynamic nature of the research in the field by including many useful references to develop a deeper understanding among interested readers. The book updates the research agenda (which has witnessed profound and startling advances since its inception some 30 years ago) with chapters on possibility theory, fuzzy logic and approximate reasoning, expert systems, fuzzy control, fuzzy data analysis, decision making and fuzzy set models in operations research. All chapters have been updated. Exercises are included.

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Fuzzy Set Theory - and Its Applications

In the expert systems of artificial intelligence, the facts and/or the rules to be represented may often be uncertain or imprecise.

Models for Approximate Reasoning in Expert Systems

By means of a careful analysis of early papers by Zadeh on fuzzy rules, we suggest an explanation of why Mamdani came up with his way of modelling fuzzy control rules. And then we recall the semantics of fuzzy rules so as to position Mamdani,s rules in possibility theory. We also explain the links between (probabilistic) conditionals, as well as association rules, and Mamdani’s rules. Finally, we comment on Mamdani’s constant taste for applied Artificial Intelligence (AI), while the whole field of fuzzy rule-based systems he created, and viewed as part of AI, eventually moved away from it.

Abe Mamdani: A Pioneer of Soft Artificial Intelligence.

The material in this book is based on a nontraditional approach to the imprecise and the uncertain. The basic concept is the measure of possibility. The object of this introduction is to provide motivation and context, to define measures of possibility, and to present basic notions necessary for understanding the later chapters. It appeals considerably to results contained in the authors’ theses [3, 24], among other references.

Measures of Possibility and Fuzzy Sets

Probability theory in artificial intelligence: Judea Pearl. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. San Mateo, CA: Morgan Kaufman, 1988. pp. xix + 552. $44.95

Argumentation can be modelled at an abstract level using an argument graph (i.e. a directed graph where each node denotes an argument and each arc denotes an attack by one argument on another). Since argumentation involves uncertainty, it is potentially valuable to consider how this can quantified in argument graphs. In this talk, we will consider two probabilistic approaches for modeling uncertainty in argumentation. The first is the structural approach which involves a probability distribution over the subgraphs of the argument graph, and this can be used to represent the uncertainty over the structure of the graph. The second is the epistemic approach which involves a probability distribution over the subsets of the arguments, and this can be used to represent the uncertainty over which arguments are believed. The epistemic approach can be constrained to be consistent with Dungs dialectical semantics, but it can also be used as a potential valuable alternative to Dungs dialectical semantics. We will consider applications of probabilistic argumentation in handling enthymemes (arguments with incomplete premises) and in selecting moves in an argumentation dialogue. IJCAI-15 Workshop on Weighted Logics for Artiticial Intelligence (WL4AI-2015)

/pdf/working-papers-of-the-ijcai-2015-workshop-on-weighted-logics-3z68f155si.pdf

Henri Prade

Papers

Models for Approximate Reasoning in Expert Systems

Abe Mamdani: A Pioneer of Soft Artificial Intelligence.

Measures of Possibility and Fuzzy Sets

Probability theory in artificial intelligence: Judea Pearl. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. San Mateo, CA: Morgan Kaufman, 1988. pp. xix + 552. $44.95

Working Papers of the IJCAI-2015 Workshop on Weighted Logics for Artificial Intelligence