Creating A Memory of Causal Relationships

Creating A Memory of Causal Relationships PDF

Author: Michael J. Pazzani

Publisher: Psychology Press

Published: 2014-02-25

Total Pages: 294

ISBN-13: 1317783913

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This book presents a theory of learning new causal relationships by making use of perceived regularities in the environment, general knowledge of causality, and existing causal knowledge. Integrating ideas from the psychology of causation and machine learning, the author introduces a new learning procedure called theory-driven learning that uses abstract knowledge of causality to guide the induction process. Known as OCCAM, the system uses theory-driven learning when new experiences conform to common patterns of causal relationships, empirical learning to learn from novel experiences, and explanation-based learning when there is sufficient existing knowledge to explain why a new outcome occurred. Together these learning methods construct a hierarchical organized memory of causal relationships. As such, OCCAM is the first learning system with the ability to acquire, via empirical learning, the background knowledge required for explanation-based learning. Please note: This program runs on common lisp.

Creating A Memory of Causal Relationships

Creating A Memory of Causal Relationships PDF

Author: Michael J. Pazzani

Publisher: Psychology Press

Published: 2014-02-25

Total Pages: 361

ISBN-13: 1317783921

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This book presents a theory of learning new causal relationships by making use of perceived regularities in the environment, general knowledge of causality, and existing causal knowledge. Integrating ideas from the psychology of causation and machine learning, the author introduces a new learning procedure called theory-driven learning that uses abstract knowledge of causality to guide the induction process. Known as OCCAM, the system uses theory-driven learning when new experiences conform to common patterns of causal relationships, empirical learning to learn from novel experiences, and explanation-based learning when there is sufficient existing knowledge to explain why a new outcome occurred. Together these learning methods construct a hierarchical organized memory of causal relationships. As such, OCCAM is the first learning system with the ability to acquire, via empirical learning, the background knowledge required for explanation-based learning. Please note: This program runs on common lisp.

Creating a Memory of Casual Relationships

Creating a Memory of Casual Relationships PDF

Author: Michael J. Pazzani

Publisher: Lawrence Erlbaum Associates

Published: 1990

Total Pages: 360

ISBN-13: 9781563210402

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This book presents a theory of learning new causal relationships by making use of perceived regularities in the environment, general knowledge of causality, and existing causal knowledge. Integrating ideas from the psychology of causation and machine learning, the author introduces a new learning procedure called theory-driven learning that uses abstract knowledge of causality to guide the induction process. Known as OCCAM, the system uses theory-driven learning when new experiences conform to common patterns of causal relationships, empirical learning to learn from novel experiences, and explanation-based learning when there is sufficient existing knowledge to explain why a new outcome occurred. Together these learning methods construct a hierarchical organized memory of causal relationships. As such, OCCAM is the first learning system with the ability to acquire, via empirical learning, the background knowledge required for explanation-based learning. Please note: This program runs on common lisp.

Causal Learning

Causal Learning PDF

Author: Alison Gopnik

Publisher: Oxford University Press

Published: 2007-03-22

Total Pages: 384

ISBN-13: 0190208260

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Understanding causal structure is a central task of human cognition. Causal learning underpins the development of our concepts and categories, our intuitive theories, and our capacities for planning, imagination and inference. During the last few years, there has been an interdisciplinary revolution in our understanding of learning and reasoning: Researchers in philosophy, psychology, and computation have discovered new mechanisms for learning the causal structure of the world. This new work provides a rigorous, formal basis for theory theories of concepts and cognitive development, and moreover, the causal learning mechanisms it has uncovered go dramatically beyond the traditional mechanisms of both nativist theories, such as modularity theories, and empiricist ones, such as association or connectionism.

Memory in the Real World

Memory in the Real World PDF

Author: Gillian Cohen

Publisher: Psychology Press

Published: 2007-12-03

Total Pages: 552

ISBN-13: 1135419876

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This fully revised and updated third edition of the highly acclaimed Memory in the Real World includes recent research in all areas of everyday memory. Distinguished researchers have contributed new and updated material in their own areas of expertise. The controversy about the value of naturalistic research, as opposed to traditional laboratory methods, is outlined, and the two approaches are seen to have converged and become complementary rather than antagonistic. The editors bring together studies on many different topics, such as memory for plans and actions, for names and faces, for routes and maps, life experiences and flashbulb memory, and eyewitness memory. Emphasis is also given to the role of memory in consciousness and metacognition. New topics covered in this edition include life span development of memory, collaborative remembering, deja-vu and memory dysfunction in the real world. Memory in the Real World will be of continuing appeal to students and researchers in the area.

Machine Learning

Machine Learning PDF

Author: Ryszard S. Michalski

Publisher: Morgan Kaufmann

Published: 1994-02-09

Total Pages: 798

ISBN-13: 9781558602519

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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.

Discourse Comprehension

Discourse Comprehension PDF

Author: Charles A. Weaver, III

Publisher: Routledge

Published: 2012-12-06

Total Pages: 426

ISBN-13: 1136482741

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This volume is derived from presentations given at a conference hosted in Boulder, Colorado in honor of the 60th birthday of Walter Kintsch. Though the contents of the talks, and thus the chapters, varied widely, all had one thing in common -- they were inspired to some degree by the work of Walter Kintsch. When making plans for an edited book centered around this conference, the editors had a primary goal: to acknowledge the wide variety of researchers and research areas Kintsch had influenced. As a consequence, one of the more unusual elements of this volume is the diversity of the contributors. Researchers from six different countries contributed chapters to this book which is loosely organized around three main thrusts of Kintsch's work: * text-based representations that explain how meaning in a text is constructed, * situation models which represent what the text is about rather than what a text literally says, and * the construction-integration model, Kintsch's most recent work in discourse comprehension.

Artificial Intelligence in Education

Artificial Intelligence in Education PDF

Author: Gautam Biswas

Publisher: Springer

Published: 2011-06-13

Total Pages: 664

ISBN-13: 3642218695

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This book constitutes the refereed proceedings of the 15th International Conference on Artificial Intelligence in Education, AIED 2011, held in Auckland, New Zealand in June/July 2011. The 49 revised full papers presented together with three invited talks and extended abstracts of poster presentations, young researchers contributions and interactive systems reports and workshop reports were carefully reviewed and selected from a total of 193 submissions. The papers report on technical advances in and cross-fertilization of approaches and ideas from the many topical areas that make up this highly interdisciplinary field of research and development including artificial intelligence, agent technology, computer science, cognitive and learning sciences, education, educational technology, game design, psychology, philosophy, sociology, anthropology and linguistics.