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A General Explanation Based Learning Mechanism And Its Application To Narrative Understanding


A General Explanation Based Learning Mechanism And Its Application To Narrative Understanding
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A General Explanation Based Learning Mechanism And Its Application To Narrative Understanding


A General Explanation Based Learning Mechanism And Its Application To Narrative Understanding
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Author : Raymond J. Mooney
language : en
Publisher: Morgan Kaufmann
Release Date : 1990

A General Explanation Based Learning Mechanism And Its Application To Narrative Understanding written by Raymond J. Mooney and has been published by Morgan Kaufmann this book supported file pdf, txt, epub, kindle and other format this book has been release on 1990 with Computers categories.


By Raymond J. Mooney.



Extending Explanation Based Learning By Generalizing The Structure Of Explanations


Extending Explanation Based Learning By Generalizing The Structure Of Explanations
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Author : Jude W. Shavlik
language : en
Publisher: Morgan Kaufmann
Release Date : 2014-07-10

Extending Explanation Based Learning By Generalizing The Structure Of Explanations written by Jude W. Shavlik and has been published by Morgan Kaufmann this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-07-10 with Computers categories.


Extending Explanation-Based Learning by Generalizing the Structure of Explanations presents several fully-implemented computer systems that reflect theories of how to extend an interesting subfield of machine learning called explanation-based learning. This book discusses the need for generalizing explanation structures, relevance to research areas outside machine learning, and schema-based problem solving. The result of standard explanation-based learning, BAGGER generalization algorithm, and empirical analysis of explanation-based learning are also elaborated. This text likewise covers the effect of increased problem complexity, rule access strategies, empirical study of BAGGER2, and related work in similarity-based learning. This publication is suitable for readers interested in machine learning, especially explanation-based learning.



Investigating Explanation Based Learning


Investigating Explanation Based Learning
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Author : Gerald DeJong
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06

Investigating Explanation Based Learning written by Gerald DeJong 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.


Explanation-Based Learning (EBL) can generally be viewed as substituting background knowledge for the large training set of exemplars needed by conventional or empirical machine learning systems. The background knowledge is used automatically to construct an explanation of a few training exemplars. The learned concept is generalized directly from this explanation. The first EBL systems of the modern era were Mitchell's LEX2, Silver's LP, and De Jong's KIDNAP natural language system. Two of these systems, Mitchell's and De Jong's, have led to extensive follow-up research in EBL. This book outlines the significant steps in EBL research of the Illinois group under De Jong. This volume describes theoretical research and computer systems that use a broad range of formalisms: schemas, production systems, qualitative reasoning models, non-monotonic logic, situation calculus, and some home-grown ad hoc representations. This has been done consciously to avoid sacrificing the ultimate research significance in favor of the expediency of any particular formalism. The ultimate goal, of course, is to adopt (or devise) the right formalism.



Goal Driven Learning


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 1988


Machine Learning Proceedings 1988
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Author : John Laird
language : en
Publisher: Morgan Kaufmann
Release Date : 2014-05-23

Machine Learning Proceedings 1988 written by John Laird 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 1988



Intelligent Systems In Process Engineering Part Ii Paradigms From Process Operations


Intelligent Systems In Process Engineering Part Ii Paradigms From Process Operations
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Author :
language : en
Publisher: Academic Press
Release Date : 1995-11-14

Intelligent Systems In Process Engineering Part Ii Paradigms From Process Operations written by and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1995-11-14 with Science categories.


Volumes 21 and 22 of Advances in Chemical Engineering contain ten prototypical paradigms which integrate ideas and methodologies from artificial intelligence with those from operations research, estimation andcontrol theory, and statistics. Each paradigm has been constructed around an engineering problem, e.g. product design, process design, process operations monitoring, planning, scheduling, or control. Along with the engineering problem, each paradigm advances a specific methodological theme from AI, such as: modeling languages; automation in design; symbolic and quantitative reasoning; inductive and deductive reasoning; searching spaces of discrete solutions; non-monotonic reasoning; analogical learning;empirical learning through neural networks; reasoning in time; and logic in numerical computing. Together the ten paradigms of the two volumes indicate how computers can expand the scope, type, and amount of knowledge that can be articulated and used in solving a broad range of engineering problems. Sets the foundations for the development of computer-aided tools for solving a number of distinct engineering problems Exposes the reader to a variety of AI techniques in automatic modeling, searching, reasoning, and learning The product of ten-years experience in integrating AI into process engineering Offers expanded and realistic formulations of real-world problems



Readings In Machine Learning


Readings In Machine Learning
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Author : Jude W. Shavlik
language : en
Publisher: Morgan Kaufmann
Release Date : 1990

Readings In Machine Learning written by Jude W. Shavlik and has been published by Morgan Kaufmann this book supported file pdf, txt, epub, kindle and other format this book has been release on 1990 with Computers categories.


The ability to learn is a fundamental characteristic of intelligent behavior. Consequently, machine learning has been a focus of artificial intelligence since the beginnings of AI in the 1950s. The 1980s saw tremendous growth in the field, and this growth promises to continue with valuable contributions to science, engineering, and business. Readings in Machine Learning collects the best of the published machine learning literature, including papers that address a wide range of learning tasks, and that introduce a variety of techniques for giving machines the ability to learn. The editors, in cooperation with a group of expert referees, have chosen important papers that empirically study, theoretically analyze, or psychologically justify machine learning algorithms. The papers are grouped into a dozen categories, each of which is introduced by the editors.



Data Mining Using Grammar Based Genetic Programming And Applications


Data Mining Using Grammar Based Genetic Programming And Applications
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Author : Man Leung Wong
language : en
Publisher: Springer Science & Business Media
Release Date : 2005-12-02

Data Mining Using Grammar Based Genetic Programming And Applications written by Man Leung Wong 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 2005-12-02 with Computers categories.


Data mining involves the non-trivial extraction of implicit, previously unknown, and potentially useful information from databases. Genetic Programming (GP) and Inductive Logic Programming (ILP) are two of the approaches for data mining. This book first sets the necessary backgrounds for the reader, including an overview of data mining, evolutionary algorithms and inductive logic programming. It then describes a framework, called GGP (Generic Genetic Programming), that integrates GP and ILP based on a formalism of logic grammars. The formalism is powerful enough to represent context- sensitive information and domain-dependent knowledge. This knowledge can be used to accelerate the learning speed and/or improve the quality of the knowledge induced. A grammar-based genetic programming system called LOGENPRO (The LOGic grammar based GENetic PROgramming system) is detailed and tested on many problems in data mining. It is found that LOGENPRO outperforms some ILP systems. We have also illustrated how to apply LOGENPRO to emulate Automatically Defined Functions (ADFs) to discover problem representation primitives automatically. By employing various knowledge about the problem being solved, LOGENPRO can find a solution much faster than ADFs and the computation required by LOGENPRO is much smaller than that of ADFs. Moreover, LOGENPRO can emulate the effects of Strongly Type Genetic Programming and ADFs simultaneously and effortlessly. Data Mining Using Grammar Based Genetic Programming and Applications is appropriate for researchers, practitioners and clinicians interested in genetic programming, data mining, and the extraction of data from databases.



Machine Learning


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.



Scandinavian Conference On Artificial Intelligence 91


Scandinavian Conference On Artificial Intelligence 91
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Author : Brian Mayoh
language : en
Publisher: IOS Press
Release Date : 1991

Scandinavian Conference On Artificial Intelligence 91 written by Brian Mayoh and has been published by IOS Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1991 with Computers categories.