B. Chandrasekaran

Bio: B. Chandrasekaran is an academic researcher from Ohio State University. The author has contributed to research in topics: Diagrammatic reasoning & Knowledge-based systems. The author has an hindex of 48, co-authored 188 publications receiving 10803 citations. Previous affiliations of B. Chandrasekaran include Ford Motor Company & University of Pennsylvania.

What are ontologies, and why do we need them?

Abstract: This survey provides a conceptual introduction to ontologies and their role in information systems and AI. The authors also discuss how ontologies clarify the domain's structure of knowledge and enable knowledge sharing.

Generic Tasks in Knowledge-Based Reasoning: High-Level Building Blocks for Expert System Design

Abstract: ion level relative to the information processing task, some control issues are artifacts of the representation. In our opinion these are often misinterpreted as issues at the knowledge level. For example, rule-based approaches often concern themselves with syntactic conflict resolution strategies. When the knowledge is viewed at the appropriate level, we can often see the existence of organizations of knowledge that bring up only a small, highly relevant body of knowledge without any need for conflict resolution at all. Of course, these organizational constructs could be \"programmed\" in the rule language (metarules are meant to do this in rule-based systems), but because of the status assigned to the rules and their control as knowledge-level phenomena (as opposed to the implementation-level phenomena, which they often are), knowledge acquisition is often directed toward strategies for conflict resolution, whereas they ought to be directed to issues of knowledge organization. This is not to argue that rule representations and backwardor forward-chaining controls are not natural for some situations. If all a problem solver has in the form of knowledge in a domain is a large collection of unorganized associative patterns, then data-directed or goal-directed associations may be the best the agent can do. But that is precisely the occasion for weak methods such as hypothesize -and-match (of which the above associations are variants), and, typically, successful solutions cannot be expected in complex problems without combinatorial searches. Typically, however, expertise consists of much better organized collections of knowledge, with control behavior indexed by the kinds of organization and forms of knowledge they contain. We have found six generic tasks that are very useful as building blocks for the construction (and understanding) of knowledge-based systems. These tasks cover a wide range of existing expert systems. Because of their role as building blocks, we call them elementary generic tasks. While we have been adding to our repertoire of elementary generic tasks for quite some time, the basic elements of the framework have been in place for a number of years. In particular, our work on MDX4,5 identified hierarchical classification, hypothesis matching, and knowledge-directed information passing as three generic tasks and showed how certain classes of diagnostic problems can be implemented as an integration of these generic tasks. (In the past we have also referred to them as problem-solving types.) Over the years we have identified several others: object synthesis by plan selection and refinement,6 state abstraction,7 and abductive assembly of hypotheses.8 This list is not exhaustive; in fact, our ongoing research objective is to identify other useful generic tasks and understand their knowledge representation and control

39 Dimensionality and sample size considerations in pattern recognition practice

Abstract: Publisher Summary This chapter discusses the role that the relationship between the number of measurements and the number of training patterns plays at various stages in the design of a pattern recognition system. The designer of a pattern recognition system should make every possible effort to obtain as many samples as possible. As the number of samples increases, not only does the designer have more confidence in the performance of the classifier, but also more measurements can be incorporated in the design of the classifier without the fear of peaking in its performance. However, there are many pattern classification problems where either the number of samples is limited or obtaining a large number of samples is extremely expensive. If the designer chooses to take the optimal Bayesian approach, the average performance of the classifier improves monotonically as the number of measurements is increased. Most practical pattern recognition systems employ a non-Bayesian decision rule because the use of optimal Bayesian approach requires knowledge of prior densities, and besides, their complexity precludes the development of real-time recognition systems. The peaking behavior of practical classifiers is caused principally by their nonoptimal use of measurements.

Diagrammatic Reasoning: Cognitive and Computational Perspectives

Abstract: From the Publisher: foreword by Herbert Simon "Understanding diagrammatic thinking will be of special importance to those who design human-computer interfaces, where the diagrams presented on computer screens must find their way to the Mind's Eye. . . . In a society that is preoccupied with `Information Superhighways,' a deep understanding of diagrammatic reasoning will be essential to keep the traffic moving." -- Herbert Simon Diagrammatic reasoning -- the understanding of concepts and ideas by the use of diagrams and imagery, as opposed to linguistic or algebraic representations -- not only allows us to gain insight into the way we think, but is a potential base for constructing representations of diagrammatic information that can be stored and processed by computers. Diagrammatic Reasoning brings together recent investigations into the cognitive, the logical, and particularly the computational characteristics of diagrammatic representations and the reasoning that can be done with them. Following a foreword by Herbert Simon and an introduction by the editors, twenty-seven chapters provide an overview of the recent history of the subject, survey and extend the underlying theory of diagrammatic representation, and provide numerous examples of diagrammatic reasoning (human and mechanical) that illustrate both its powers and its limitations. Each of the book's four sections (Historical and Philosophical Background, Theoretical Foundations, Cognitive and Computational Models, and Problem Solving with Diagrams) begins with an introduction by an eminent researcher. These introductions provide interesting personal perspectives as well as place the work in the proper context. Additional information on Diagrammatic Reasoning Distributed for AAAI Press

Design Problem Solving: Knowledge Structures and Control Strategies

Abstract: This work provides a perspective on design problem solving in the context of expert systems and outlines a general theory of knowledge-based reasoning. An expert system for design built according to these ideas is described.

Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference

Abstract: 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.

Statistical pattern recognition: a review

Abstract: The primary goal of pattern recognition is supervised or unsupervised classification. Among the various frameworks in which pattern recognition has been traditionally formulated, the statistical approach has been most intensively studied and used in practice. More recently, neural network techniques and methods imported from statistical learning theory have been receiving increasing attention. The design of a recognition system requires careful attention to the following issues: definition of pattern classes, sensing environment, pattern representation, feature extraction and selection, cluster analysis, classifier design and learning, selection of training and test samples, and performance evaluation. In spite of almost 50 years of research and development in this field, the general problem of recognizing complex patterns with arbitrary orientation, location, and scale remains unsolved. New and emerging applications, such as data mining, web searching, retrieval of multimedia data, face recognition, and cursive handwriting recognition, require robust and efficient pattern recognition techniques. The objective of this review paper is to summarize and compare some of the well-known methods used in various stages of a pattern recognition system and identify research topics and applications which are at the forefront of this exciting and challenging field.

Knowledge engineering: principles and methods

Abstract: This paper gives an overview of the development of the field of Knowledge Engineering over the last 15 years. We discuss the paradigm shift from a transfer view to a modeling view and describe two approaches which considerably shaped research in Knowledge Engineering: Role-limiting Methods and Generic Tasks. To illustrate various concepts and methods which evolved in recent years we describe three modeling frameworks: CommonKADS, MIKE and PROTEGE-II. This description is supplemented by discussing some important methodological developments in more detail: specification languages for knowledge-based systems, problem-solving methods and ontologies. We conclude by outlining the relationship of Knowledge Engineering to Software Engineering, Information Integration and Knowledge Management.

Neuronlike adaptive elements that can solve difficult learning control problems

Abstract: It is shown how a system consisting of two neuronlike adaptive elements can solve a difficult learning control problem. The task is to balance a pole that is hinged to a movable cart by applying forces to the cart's base. It is argued that the learning problems faced by adaptive elements that are components of adaptive networks are at least as difficult as this version of the pole-balancing problem. The learning system consists of a single associative search element (ASE) and a single adaptive critic element (ACE). In the course of learning to balance the pole, the ASE constructs associations between input and output by searching under the influence of reinforcement feedback, and the ACE constructs a more informative evaluation function than reinforcement feedback alone can provide. The differences between this approach and other attempts to solve problems using neurolike elements are discussed, as is the relation of this work to classical and instrumental conditioning in animal learning studies and its possible implications for research in the neurosciences.

The Self-Organizing Map

Abstract: An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.