Download or read book Machine Learning Meta Reasoning and Logics written by Pavel B. Brazdil and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 339 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book contains a selection of papers presented at the International Workshop Machine Learning, Meta-Reasoning and Logics held in Hotel de Mar in Sesimbra, Portugal, 15-17 February 1988. All the papers were edited afterwards. The Workshop encompassed several fields of Artificial Intelligence: Machine Learning, Belief Revision, Meta-Reasoning and Logics. The objective of this Workshop was not only to address the common issues in these areas, but also to examine how to elaborate cognitive architectures for systems capable of learning from experience, revising their beliefs and reasoning about what they know. Acknowledgements The editing of this book has been supported by COST-13 Project Machine Learning and Knowledge Acquisition funded by the Commission o/the European Communities which has covered a substantial part of the costs. Other sponsors who have supported this work were Junta Nacional de lnvestiga~ao Cientlfica (JNICT), lnstituto Nacional de lnvestiga~ao Cientlfica (INIC), Funda~ao Calouste Gulbenkian. I wish to express my gratitude to all these institutions. Finally my special thanks to Paula Pereira and AnaN ogueira for their help in preparing this volume. This work included retyping all the texts and preparing the camera-ready copy. Introduction 1 1. Meta-Reasoning and Machine Learning The first chapter is concerned with the role meta-reasoning plays in intelligent systems capable of learning. As we can see from the papers that appear in this chapter, there are basically two different schools of thought.
Download or read book Inductive Logic Programming written by Stephen Muggleton and published by Morgan Kaufmann. This book was released on 1992 with total page 602 pages. Available in PDF, EPUB and Kindle. Book excerpt: Inductive logic programming is a new research area emerging at present. Whilst inheriting various positive characteristics of the parent subjects of logic programming an machine learning, it is hoped that the new area will overcome many of the limitations of its forbears. This book describes the theory, implementations and applications of Inductive Logic Programming.
Download or read book Machine Learning Proceedings 1989 written by Alberto Maria Segre and published by Morgan Kaufmann. This book was released on 2014-06-28 with total page 521 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning Proceedings 1989
Download or read book An Introduction to Fuzzy Logic Applications in Intelligent Systems written by Ronald R. Yager and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 358 pages. Available in PDF, EPUB and Kindle. Book excerpt: An Introduction to Fuzzy Logic Applications in Intelligent Systems consists of a collection of chapters written by leading experts in the field of fuzzy sets. Each chapter addresses an area where fuzzy sets have been applied to situations broadly related to intelligent systems. The volume provides an introduction to and an overview of recent applications of fuzzy sets to various areas of intelligent systems. Its purpose is to provide information and easy access for people new to the field. The book also serves as an excellent reference for researchers in the field and those working in the specifics of systems development. People in computer science, especially those in artificial intelligence, knowledge-based systems, and intelligent systems will find this to be a valuable sourcebook. Engineers, particularly control engineers, will also have a strong interest in this book. Finally, the book will be of interest to researchers working in decision support systems, operations research, decision theory, management science and applied mathematics. An Introduction to Fuzzy Logic Applications in Intelligent Systems may also be used as an introductory text and, as such, it is tutorial in nature.
Download or read book Machine Learning written by Yves Kodratoff and published by Elsevier. This book was released on 2014-06-28 with total page 836 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning: An Artificial Intelligence Approach, Volume III presents a sample of machine learning research representative of the period between 1986 and 1989. The book is organized into six parts. Part One introduces some general issues in the field of machine learning. Part Two presents some new developments in the area of empirical learning methods, such as flexible learning concepts, the Protos learning apprentice system, and the WITT system, which implements a form of conceptual clustering. Part Three gives an account of various analytical learning methods and how analytic learning can be applied to various specific problems. Part Four describes efforts to integrate different learning strategies. These include the UNIMEM system, which empirically discovers similarities among examples; and the DISCIPLE multistrategy system, which is capable of learning with imperfect background knowledge. Part Five provides an overview of research in the area of subsymbolic learning methods. Part Six presents two types of formal approaches to machine learning. The first is an improvement over Mitchell's version space method; the second technique deals with the learning problem faced by a robot in an unfamiliar, deterministic, finite-state environment.
Download or read book Machine Intelligence 15 written by Koichi Furukawa and published by Oxford University Press. This book was released on 1999 with total page 518 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Machine Intelligence series was founded in 1965 by Donald Michie and has included many of the most important developments in the field over the past decades. This volume focuses on the theme of intelligent agents and features work by a number of eminent figures in artificial intelligence, including John McCarthy, Alan Robinson, Robert Kowalski, and Mike Genesereth. Topics include representations of consciousness, SoftBots, parallel implementations of logic, machine learning, machine vision, and machine-based scientific discovery in molecular biology.
Download or read book Machine Learning EWSL 91 written by Yves Kodratoff and published by Springer Science & Business Media. This book was released on 1991-02-20 with total page 554 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book contemporary knowledge of superconductivity is set against its historical background. First, the highlights of superconductivity research in the twentieth century are reviewed. Further contributions then describe the basic phenomena resulting from the macroscopic quantum state of superconductivity (such as zero resistivity, the Meissner-Ochsenfeld effect, and flux quantization) and review possible mechaniscs, including the classical BCS theory and the more recent alternative theories. The main categories of superconductors - elements, intermetallic phases, chalcogenides, oxides and organic compounds - are described. Common features and differences in their structure and electronic properties are pointed out. This broad overview of superconductivity is completed by a discussion of properties related to the coherence length. Newcomers to the field who seek an overall picture of research in superconductivity, and of the cross-links between its branches, will find this volume especially useful.
Download or read book Machine Learning Proceedings 1991 written by Lawrence A. Birnbaum and published by Morgan Kaufmann. This book was released on 2014-06-28 with total page 682 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning
Download or read book Goal driven Learning written by Ashwin Ram and published by MIT Press. This book was released on 1995 with total page 548 pages. Available in PDF, EPUB and Kindle. Book excerpt: Brings together a diversity of research on goal-driven learning to establish a broad, interdisciplinary framework that describes the goal-driven learning process. In cognitive science, artificial intelligence, psychology, and education, a growing body of research supports the view that the learning process is strongly influenced by the learner's goals. The fundamental tenet of goal-driven learning is that learning is largely an active and strategic process in which the learner, human or machine, attempts to identify and satisfy its information needs in the context of its tasks and goals, its prior knowledge, its capabilities, and environmental opportunities for learning. This book brings together a diversity of research on goal-driven learning to establish a broad, interdisciplinary framework that describes the goal-driven learning process. It collects and solidifies existing results on this important issue in machine and human learning and presents a theoretical framework for future investigations. The book opens with an an overview of goal-driven learning research and computational and cognitive models of the goal-driven learning process. This introduction is followed by a collection of fourteen recent research articles addressing fundamental issues of the field, including psychological and functional arguments for modeling learning as a deliberative, planful process; experimental evaluation of the benefits of utility-based analysis to guide decisions about what to learn; case studies of computational models in which learning is driven by reasoning about learning goals; psychological evidence for human goal-driven learning; and the ramifications of goal-driven learning in educational contexts. The second part of the book presents six position papers reflecting ongoing research and current issues in goal-driven learning. Issues discussed include methods for pursuing psychological studies of goal-driven learning, frameworks for the design of active and multistrategy learning systems, and methods for selecting and balancing the goals that drive learning. A Bradford Book
Download or read book A Future for Knowledge Acquisition written by Luc Steels and published by Springer Science & Business Media. This book was released on 1994-09-14 with total page 438 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the last few years rapid advances have been made in reproductive medicine, making it necessary for those involved to regularly update their knowledge. The purpose of this book is to describe the state of the art in this field, making it possible for the reader to gain an orientation among all the diagnostic and therapeutic potentials of modern reproductive medicine in order to advise patients fully. Chapters from the fields of gynecology, and reproductive medicine in a specific sense provide knowledge about these subjects. Authors of international standing have contributed chapters on their specialties. These chapters together form a book describing the state of the art in the diagnosis and therapy of sterility in gynecology and andrology.
Download or read book Analogical and Inductive Inference written by Klaus P. Jantke and published by Springer Science & Business Media. This book was released on 1992-09-23 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume contains the text of the five invited papers and 16 selected contributions presented at the third International Workshop on Analogical and Inductive Inference, AII `92, held in Dagstuhl Castle, Germany, October 5-9, 1992. Like the two previous events, AII '92 was intended to bring together representatives from several research communities, in particular, from theoretical computer science, artificial intelligence, and from cognitive sciences. The papers contained in this volume constitute a state-of-the-art report on formal approaches to algorithmic learning, particularly emphasizing aspects of analogical reasoning and inductive inference. Both these areas are currently attracting strong interest: analogical reasoning plays a crucial role in the booming field of case-based reasoning, and, in the fieldof inductive logic programming, there have recently been developed a number of new techniques for inductive inference.
Download or read book Machine Learning Proceedings 1990 written by Bruce Porter and published by Morgan Kaufmann. This book was released on 2014-05-23 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning Proceedings 1990
Download or read book Reinforcement Learning written by Richard S. Sutton and published by Springer Science & Business Media. This book was released on 1992-05-31 with total page 186 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reinforcement learning is the learning of a mapping from situations to actions so as to maximize a scalar reward or reinforcement signal. The learner is not told which action to take, as in most forms of machine learning, but instead must discover which actions yield the highest reward by trying them. In the most interesting and challenging cases, actions may affect not only the immediate reward, but also the next situation, and through that all subsequent rewards. These two characteristics -- trial-and-error search and delayed reward -- are the most important distinguishing features of reinforcement learning. Reinforcement learning is both a new and a very old topic in AI. The term appears to have been coined by Minsk (1961), and independently in control theory by Walz and Fu (1965). The earliest machine learning research now viewed as directly relevant was Samuel's (1959) checker player, which used temporal-difference learning to manage delayed reward much as it is used today. Of course learning and reinforcement have been studied in psychology for almost a century, and that work has had a very strong impact on the AI/engineering work. One could in fact consider all of reinforcement learning to be simply the reverse engineering of certain psychological learning processes (e.g. operant conditioning and secondary reinforcement). Reinforcement Learning is an edited volume of original research, comprising seven invited contributions by leading researchers.
Download or read book Machine Learning Proceedings 1988 written by John Laird and published by Morgan Kaufmann. This book was released on 2014-05-23 with total page 476 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning Proceedings 1988
Download or read book Machine Learning written by Ryszard S. Michalski and published by Morgan Kaufmann. This book was released on 1994-02-09 with total page 798 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Advances in Intelligent Computing IPMU 94 written by Bernadette Bouchon-Meunier and published by Springer Science & Business Media. This book was released on 1995-06-26 with total page 648 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a topical selection of full refereed research papers presented during the 5th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU '94, held in Paris, France in July 1994. The topical focus is on the role of uncertainty in the contruction of intelligent computing systems and it is shown how the concepts of AI, neural networks, and fuzzy logic can be utilized for that purpose. In total, there are presented 63 thoroughly revised papers organized in sections on fundamental issues; theory of evidence; networks, probabilistic, statistical, and informational methods; possibility theory, logics, chaos, reusability, and applications.
Download or read book Toward Learning Robots written by Walter Van de Velde and published by MIT Press. This book was released on 1993 with total page 182 pages. Available in PDF, EPUB and Kindle. Book excerpt: The contributions in Toward Learning Robots address the question of how a robot can be designed to acquire autonomously whatever it needs to realize adequate behavior in a complex environment. In-depth discussions of issues, techniques, and experiments in machine learning focus on improving ease of programming and enhancing robustness in unpredictable and changing environments, given limitations of time and resources available to researchers. The authors show practical progress toward a useful set of abstractions and techniques to describe and automate various aspects of learning in autonomous systems. The close interaction of such a system with the world reveals opportunities for new architectures and learning scenarios and for grounding symbolic representations, though such thorny problems as noise, choice of language, abstraction level of representation, and operationality have to be faced head-on. Contents Introduction: Toward Learning Robots * Learning Reliable Manipulation Strategies without Initial Physical Models * Learning by an Autonomous Agent in the Pushing Domain * A Cost-Sensitive Machine Learning Method for the Approach and Recognize Task * A Robot Exploration and Mapping Strategy Based on a Semantic Hierarchy of Spatial Representations * Understanding Object Motion: Recognition, Learning and Spatiotemporal Reasoning * Learning How to Plan * Robo-Soar: An Integration of External Interaction, Planning, and Learning Using Soar * Foundations of Learning in Autonomous Agents * Prior Knowledge and Autonomous Learning
