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Machine Learning
Multistrategy learning is one of the newest and most promising research directions in the development of machine learning systems. The objectives of research in this area are to study trade-offs between different learning strategies and to develop learning systems that employ multiple types of inference or computational paradigms in a learning process. Multistrategy systems offer significant advantages over monostrategy systems. They are more flexible in the type of input they can learn from and the type of knowledge they can acquire. As a consequence, multistrategy systems have the potential to be applicable to a wide range of practical problems. This volume is the first book in this fast growing field. It contains a selection of contributions by leading researchers specializing in this area. See below for earlier volumes in the series.
The KBMT Project
Machine translation of natural languages is one of the most complex and comprehensive applications of computational linguistics and artificial intelligence. This is especially true of knowledge-based machine translation (KBMT) systems, which require many knowledge resources and processing modules to carry out the necessary levels of analysis, representation and generation of meaning and form. The number of real-world problems, tasks, and solutions involved in developing any realistic-size knowledge-based machine translation system is enormous. It is thus difficult for researchers in the field to learn what a system "really does". This book fills that need with a detailed case study of a KBMT...
Case-based Reasoning
None
Computer Systems and Software Engineering
Computer Systems and Software Engineering is a compilation of sixteen state-of-the-art lectures and keynote speeches given at the COMPEURO '92 conference. The contributions are from leading researchers, each of whom gives a new insight into subjects ranging from hardware design through parallelism to computer applications. The pragmatic flavour of the contributions makes the book a valuable asset for both researchers and designers alike. The book covers the following subjects: Hardware Design: memory technology, logic design, algorithms and architecture; Parallel Processing: programming, cellular neural networks and load balancing; Software Engineering: machine learning, logic programming and program correctness; Visualization: the graphical computer interface.
Architectures for Intelligence
This unique volume focuses on computing systems that exhibit intelligent behavior. As such, it discusses research aimed at building a computer that has the same cognitive architecture as the mind -- permitting evaluations of it as a model of the mind -- and allowing for comparisons between computer performance and experimental data on human performance. It also examines architectures that permit large, complex computations to be performed -- and questions whether the computer so structured can handle these difficult tasks intelligently.
Modeling Creativity and Knowledge-Based Creative Design
Over the last decade research into design processes utilizing ideas and models drawn from artificial intelligence has resulted in a better understanding of design -- particularly routine design -- as a process. Indeed, most of the current research activity directly or indirectly deals only with routine design. Not surprisingly, many practicing designers state that the level of understanding represented by these models is only of mild interest because they fail to embody any ideas about creativity. This volume provides a set of chapters in the areas of modeling creativity and knowledge-based creative design that examines the potential role and form of computer-aided design which supports creativity. It aims to define the state-of-the-art of computational creativity in design as well as to identify research directions. Published at a time when the field of computational creativity in design is still immature, it should influence the directions of growth and assist the field in reaching maturity.
Knowledge, Data and Computer-Assisted Decisions
Proceedings of the NATO Advanced Research Workshop on Data, Expert Knowledge and Decisions, held in Hamburg, FRG, September 3-5, 1989
Foundations for A Psychology of Education
The chapters in this collection illustrate how current concepts and principles from various disciplines can be viewed from the perspective of their value to educational process thinking. While not providing specific prescriptions for educational problems, the articles provide relevant experimental and theoretical knowledge has accumulated in many fields including learning theory, cognitive development, motivation, and intellectual abilities and attitudes.
Sketches of Thought
Much of the cognitive lies beyond articulate, discursive thought, beyond the reach of current computational notions. In Sketches of Thought, Vinod Goel argues that the cognitive computational conception of the world requires our thought processes to be precise, rigid, discrete, and unambiguous; yet there are dense, ambiguous, and amorphous symbol systems, like sketching, painting, and poetry, found in the arts and much of everyday discourse that have an important, non-trivial place in cognition. Goel maintains that while on occasion our thoughts do conform to the current computational theory of mind, they often are - indeed must be - vague, fluid, ambiguous, and amorphous. He argues that if ...
Artificial Intelligence and the Future of Testing
This volume consists of a series of essays written by experts, most of whom participated in a conference conducted by the Educational Testing Service to explore how current fields of artificial intelligence might contribute to ETS's plans to automate one or more of its testing activities. The papers presented in Artificial Intelligence and the Future of Testing touch on a variety of topics including mathematics tutors, graph comprehension and computer vision, student reasoning and human accessing, modeling software design within a general problem-space architecture, memory organization and retrieval, and natural language systems. Also included: speculation on possible uses each AI specialty might have for a wide number of testing activities, and selective critical commentaries by two eminent AI researchers. As Roy Freedle notes in his introduction, "We are at an exciting juncture in applying AI to testing activities." The essays presented in this collection convey some of that excitement, and represent an important step toward the merging of AI and testing -- a powerful combination that has the potential to instruct and inspire.
