Archive for the ‘Books’ Category

Case-Based Reasoning Research And Development

This book constitutes the refereed proceedings of the 19th International Conference on Case-Based Reasoning, held in London, UK, in September 2011. The 32 contributions presented together with 3 invited talks were carefully reviewd and selected from 67 submissions. The presentations and posters covered a wide range of CBR topics of interest both to practitioners and researchers, including CBR methodology covering case representation, similarity, retrieval, and adaptation; provenance and maintenance; recommender systems; multi-agent collaborative systems; data mining; time series analysis; Web applications; knowledge management; legal reasoning; healthcare systems and planning systems.
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Case-Based Reasoning Research and Development | Lecture Notes in Artificial Intelligence, Vol. 6880

edited by Ashwin Ram and Nirmalie Wiratunga

Springer, October 20, 2011, ISBN 978-3-642-23290-9

Understanding Language Understanding: Computional Models of Reading

This book highlights cutting-edge research relevant to the building of a computational model of reading comprehension, as in the processing and understanding of a natural language text or story. A distinguishing feature of the book is its emphasis on “real” understanding of “real” narrative texts rather than on syntactic parsing of single sentences taken out of context or on limited understanding of small, researcher-constructed stories.

The book takes an interdisciplinary approach to the study of reading, with contributions from computer science, psychology, and philosophy. Contributors cover the theoretical and psychological foundations of the research in discussions of what it means to understand a text, how one builds a computational model, and related issues in knowledge representation and reasoning. The book also addresses some of the broader issues that a natural language system must deal with, such as reading in context, linguistic novelty, and information extraction.

Dorrit Billman, Michael T. Cox, Eric Domeshek, Kurt Eiselt, Charles R. Fletcher, Richard Gerrig, Jennifer Holbrook, Eric Jones, Trent Lange, Mark Langston, Joe Magliano, Kavi Mahesh, Bonnie J. F. Meyer, Justin Peterson, William J. Rapaport, Ellen Riloff, Stuart C. Shapiro, Tom Trabasso, Charles M. Wharton.

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Understanding Language Understanding: Computional Models of Reading

edited by Ashwin Ram, Kenneth Moorman

MIT Press, Cambridge, MA, 1999, ISBN 978-0-262-18192-1

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Table of Contents

About the Editors

About the Authors
Chapter 1 (Introduction) Toward a Theory of Reading and Understanding, Ram, Moorman
Chapter 2 (Foundations) Cognition and Fiction, Rapaport, Shapiro
Chapter 3 (Sentence Processing) Sentence Processing in Understanding: Interaction and Integration of Knowledge Sources, Mahesh, Eiselt, Holbrook
Chapter 4 (Knowledge Representation) Capturing the Contents of Complex Narratives, Domeshek, Jones, Ram
Chapter 5 (Memory and Inference) Retrieval from Episodic Memory by Inferencing and Disambiguation, Lange, Wharton
Chapter 6 (Inference and Comprehension) A Connectionist Model of Narrative Comprehension, Langston, Trabasso, Magliano
Chapter 7 (Contextualization: Text Structure) Importance of Text Structure in Everyday Reading, Meyer
Chapter 8 (Contextualization: Goals) A Theory of Questions and Question Asking, Ram
Chapter 9 (Linguistic Novelty) Semantic Correspondence Theory, Peterson, Billman
Chapter 10 (Conceptual Novelty) Creativity in Reading: Understanding Novel Concepts, Moorman, Ram
Chapter 11 (Meta-Reasoning and Learning) On the Intersection of Story Understanding and Learning, Cox, Ram
Chapter 12 (Alternative Approaches) Information Extraction as a Stepping Stone toward Story Understanding, Riloff
Chapter 13 (Foundations Revisited) Text Processing and Narrative Worlds, Gerrig
Chapter 14 (Commentary) Computational Models of Reading and Understanding: What Good Are They?, Fletcher

Goal-Driven Learning

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, 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.

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Goal-Driven Learning

edited by Ashwin Ram, David Leake

MIT Press/Bradford Books, Cambridge, MA, 1995, ISBN 978-0-262-18165-5

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Table of Contents

Preface by Professor Tom Mitchell
Editors’ Preface
Chapter 1: Learning, Goals, and Learning Goals, Ram, Leake

Part I: Current state of the field

Chapter 2: Planning to Learn, Hunter
Chapter 3: Quantitative Results Concerning the Utility of Explanation-Based Learning, Minton
Chapter 4: The Use of Explicit Goals for Knowledge to Guide Inference and Learning, Ram, Hunter
Chapter 5: Deriving Categories to Achieve Goals, Barsalou
Chapter 6: Harpoons and Long Sticks: The Interaction of Theory and Similarity in Rule Induction, Wisniewski, Medin
Chapter 7: Introspective Reasoning using Meta-Explanations for Multistrategy Learning, Ram, Cox
Chapter 8: Goal-Directed Learning: A Decision-Theoretic Model for Deciding What to Learn Next, desJardins
Chapter 9: Goal-Based Explanation Evaluation, Leake
Chapter 10: Planning to Perceive, Pryor, Collins
Chapter 11: Learning and Planning in PRODIGY: Overview of an Integrated Architecture, Carbonell, Etzioni, Gil, Joseph, Knoblock, Minton, Veloso
Chapter 12: A Learning Model for the Selection of Problem Solving Strategies in Continuous Physical Systems, Xia, Yeung
Chapter 13: Explicitly Biased Generalization, Gordon, Perlis
Chapter 14: Three Levels of Goal Orientation in Learning, Ng, Bereiter
Chapter 15: Characterising the Application of Computer Simulations in Education: Instructional Criteria, van Berkum, Hijne, de Jong, van Joolingen, Njoo

Part II: Current research and recent directions

Chapter 16: Goal-Driven Learning: Fundamental Issues and Symposium Report, Leake, Ram
Chapter 17: Storage Side Effects: Studying Processing to Understand Learning, Barsalou
Chapter 18: Goal-Driven Learning in Multistrategy Reasoning and Learning Systems, Ram, Cox, Narayanan
Chapter 19: Inference to the Best Plan: A Coherence Theory of Decision, Thagard, Millgram
Chapter 20: Towards Goal-Driven Integration of Explanation and Action, Leake
Chapter 21: Learning as Goal-Driven Inference, Michalski, Ram


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