Machine Learning Meta Reasoning And Logics

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Machine Learning, Meta-Reasoning and Logics

Pavel B. Brazdil,Kurt Konolige
Machine Learning Meta Reasoning and Logics Book PDF

Author : Pavel B. Brazdil,Kurt Konolige
Publisher : Springer Science & Business Media
Page : 339 pages
File Size : 42,6 Mb
Release : 2012-12-06
Category : Computers
ISBN : 9781461316411

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Machine Learning, Meta-Reasoning and Logics by Pavel B. Brazdil,Kurt Konolige Pdf

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.

Machine Learning Proceedings 1989

Machine Learning
Machine Learning Proceedings 1989 Book PDF

Author : Machine Learning
Publisher : Morgan Kaufmann
Page : 510 pages
File Size : 52,7 Mb
Release : 2016-04-20
Category : Computers
ISBN : 9781483297408

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Machine Learning Proceedings 1989 by Machine Learning Pdf

Machine Learning Proceedings 1989

Inductive Logic Programming

Stephen Muggleton
Inductive Logic Programming Book PDF

Author : Stephen Muggleton
Publisher : Morgan Kaufmann
Page : 602 pages
File Size : 55,5 Mb
Release : 1992
Category : Computers
ISBN : 0125097158

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Inductive Logic Programming by Stephen Muggleton Pdf

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.

Machine Learning

Yves Kodratoff,Ryszard S. Michalski
Machine Learning Book PDF

Author : Yves Kodratoff,Ryszard S. Michalski
Publisher : Elsevier
Page : 825 pages
File Size : 41,9 Mb
Release : 2014-06-28
Category : Computers
ISBN : 9780080510552

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Machine Learning by Yves Kodratoff,Ryszard S. Michalski Pdf

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.

Neural-Symbolic Cognitive Reasoning

Artur S. D'Avila Garcez,Luís C. Lamb,Dov M. Gabbay
Neural Symbolic Cognitive Reasoning Book PDF

Author : Artur S. D'Avila Garcez,Luís C. Lamb,Dov M. Gabbay
Publisher : Springer Science & Business Media
Page : 200 pages
File Size : 53,9 Mb
Release : 2009
Category : Computers
ISBN : 9783540732457

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Neural-Symbolic Cognitive Reasoning by Artur S. D'Avila Garcez,Luís C. Lamb,Dov M. Gabbay Pdf

This book explores why, regarding practical reasoning, humans are sometimes still faster than artificial intelligence systems. It is the first to offer a self-contained presentation of neural network models for many computer science logics.

Metareasoning

Michael T. Cox,Anita Raja
Metareasoning Book PDF

Author : Michael T. Cox,Anita Raja
Publisher : MIT Press
Page : 349 pages
File Size : 40,6 Mb
Release : 2011
Category : Computers
ISBN : 9780262014809

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Metareasoning by Michael T. Cox,Anita Raja Pdf

Experts report on the latest artificial intelligence research concerning reasoning about reasoning itself.

Goal-driven Learning

Ashwin Ram,David B. Leake
Goal driven Learning Book PDF

Author : Ashwin Ram,David B. Leake
Publisher : MIT Press
Page : 548 pages
File Size : 40,7 Mb
Release : 1995
Category : Computers
ISBN : 0262181657

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Goal-driven Learning by Ashwin Ram,David B. Leake Pdf

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

Machine Learning Proceedings 1990

Machine Learning
Machine Learning Proceedings 1990 Book PDF

Author : Machine Learning
Publisher : Morgan Kaufmann
Page : 427 pages
File Size : 48,5 Mb
Release : 2014-05-23
Category : Computers
ISBN : 9781483298580

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Machine Learning Proceedings 1990 by Machine Learning Pdf

Machine Learning Proceedings 1990

Machine Learning Proceedings 1991

Machine Learning
Machine Learning Proceedings 1991 Book PDF

Author : Machine Learning
Publisher : Morgan Kaufmann
Page : 661 pages
File Size : 44,6 Mb
Release : 2014-06-28
Category : Computers
ISBN : 9781483298177

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Machine Learning Proceedings 1991 by Machine Learning Pdf

Machine Learning

Machine Learning

Ryszard S. Michalski,George Tecuci
Machine Learning Book PDF

Author : Ryszard S. Michalski,George Tecuci
Publisher : Morgan Kaufmann
Page : 798 pages
File Size : 43,8 Mb
Release : 1994-02-09
Category : Computers
ISBN : 1558602518

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Machine Learning by Ryszard S. Michalski,George Tecuci Pdf

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.

Machine Learning - EWSL-91

Yves Kodratoff
Machine Learning EWSL 91 Book PDF

Author : Yves Kodratoff
Publisher : Springer Science & Business Media
Page : 554 pages
File Size : 42,8 Mb
Release : 1991-02-20
Category : Computers
ISBN : 354053816X

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Machine Learning - EWSL-91 by Yves Kodratoff Pdf

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.

Reinforcement Learning

Richard S. Sutton
Reinforcement Learning Book PDF

Author : Richard S. Sutton
Publisher : Springer Science & Business Media
Page : 186 pages
File Size : 43,8 Mb
Release : 1992-05-31
Category : Computers
ISBN : 0792392345

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Reinforcement Learning by Richard S. Sutton Pdf

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.

Machine Learning Proceedings 1988

John Laird
Machine Learning Proceedings 1988 Book PDF

Author : John Laird
Publisher : Morgan Kaufmann
Page : 467 pages
File Size : 41,9 Mb
Release : 2014-05-23
Category : Computers
ISBN : 9781483297699

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Machine Learning Proceedings 1988 by John Laird Pdf

Machine Learning Proceedings 1988

An Introduction to Fuzzy Logic Applications in Intelligent Systems

Ronald R. Yager,Lotfi A. Zadeh
An Introduction to Fuzzy Logic Applications in Intelligent Systems Book PDF

Author : Ronald R. Yager,Lotfi A. Zadeh
Publisher : Springer Science & Business Media
Page : 358 pages
File Size : 46,7 Mb
Release : 2012-12-06
Category : Computers
ISBN : 9781461536406

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An Introduction to Fuzzy Logic Applications in Intelligent Systems by Ronald R. Yager,Lotfi A. Zadeh Pdf

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.

Abductive Reasoning and Learning

Dov M. Gabbay,Philippe Smets
Abductive Reasoning and Learning Book PDF

Author : Dov M. Gabbay,Philippe Smets
Publisher : Springer Science & Business Media
Page : 446 pages
File Size : 42,7 Mb
Release : 2013-04-17
Category : Mathematics
ISBN : 9789401717335

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Abductive Reasoning and Learning by Dov M. Gabbay,Philippe Smets Pdf

This book contains leading survey papers on the various aspects of Abduction, both logical and numerical approaches. Abduction is central to all areas of applied reasoning, including artificial intelligence, philosophy of science, machine learning, data mining and decision theory, as well as logic itself.