Machine Learning Meta Reasoning And Logics
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Machine Learning Meta Reasoning And Logics
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Author : Pavel B. Brazdil
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Machine Learning Meta Reasoning And Logics written by Pavel B. Brazdil and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Computers categories.
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.
Neural Symbolic Cognitive Reasoning
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Author : Artur S. D'Avila Garcez
language : en
Publisher: Springer Science & Business Media
Release Date : 2009
Neural Symbolic Cognitive Reasoning written by Artur S. D'Avila Garcez and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with Computers categories.
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
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Author : Michael T. Cox
language : en
Publisher: MIT Press
Release Date : 2011
Metareasoning written by Michael T. Cox and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with Computers categories.
Experts report on the latest artificial intelligence research concerning reasoning about reasoning itself.
Machine Learning Proceedings 1989
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Author : Machine Learning
language : en
Publisher: Morgan Kaufmann
Release Date : 2016-04-20
Machine Learning Proceedings 1989 written by Machine Learning and has been published by Morgan Kaufmann this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-04-20 with Computers categories.
Machine Learning Proceedings 1989
Inductive Logic Programming
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Author : Stephen Muggleton
language : en
Publisher: Morgan Kaufmann
Release Date : 1992
Inductive Logic Programming written by Stephen Muggleton and has been published by Morgan Kaufmann this book supported file pdf, txt, epub, kindle and other format this book has been release on 1992 with Computers categories.
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
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Author : Yves Kodratoff
language : en
Publisher: Elsevier
Release Date : 2014-06-28
Machine Learning written by Yves Kodratoff and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-06-28 with Computers categories.
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.
Goal Driven Learning
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Author : Ashwin Ram
language : en
Publisher: MIT Press
Release Date : 1995
Goal Driven Learning written by Ashwin Ram and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1995 with Computers categories.
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
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Author : Machine Learning
language : en
Publisher: Morgan Kaufmann
Release Date : 2014-05-23
Machine Learning Proceedings 1990 written by Machine Learning and has been published by Morgan Kaufmann this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-05-23 with Computers categories.
Machine Learning Proceedings 1990
Machine Learning Proceedings 1991
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Author : Machine Learning
language : en
Publisher: Morgan Kaufmann
Release Date : 2014-06-28
Machine Learning Proceedings 1991 written by Machine Learning and has been published by Morgan Kaufmann this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-06-28 with Computers categories.
Machine Learning
Machine Learning
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Author : Ryszard S. Michalski
language : en
Publisher: Morgan Kaufmann
Release Date : 1994-02-09
Machine Learning written by Ryszard S. Michalski and has been published by Morgan Kaufmann this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994-02-09 with Computers categories.
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.