David B. Leake

Thomas R. Roth-Berghofer

Stefan Schulz

This book constitutes the thoroughly refereed post-proceedings of the Second International Workshop on Modeling and Retrieval of Context, MRC Edinburgh 2005. The 9 revised full papers presented were carefully selected and include extended versions of some presented at the MRC 2005 workshop. A major goal of the workshop was to study, understand, and explore the handling of context in IT applications. The papers illustrate the state of the art of context modeling and elicitation as well as identification and application of context in different application scenarios.

Second International Workshop, MRC 2005, Edinburgh, UK, July 31-August 1, 2005, Revised Selected Papers

Modeling and Retrieval of Context

R. i International Conference on Case-Based Reasoning 1997 Providence

Enric Plaza

International Conference On Case-based Reasoning 1

This book constitutes the refereed proceedings of the Second International Conference on Case-Based Reasoning, ICCBR-97, held in Providence, RI, USA, in July 1997. The volume presents 39 revised full scientific papers selected from a total of 102 submissions; also included are 20 revised application papers. Among the topics covered are representation and formalization, indexing and retrieval, adaptation, learning, integrated approaches, creative reasoning, CBR and uncertainty. This collection of papers is a comprehensive documentation of the state of the art in CBR research and development.

Second International Conference on Case-Based Reasoning, ICCBR-97 Providence, RI, USA, July 25-27, 1997 Proceedings

Case-Based Reasoning Research and Development

David Leake

Ashwin Ram

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

Goal-driven Learning

David B. Leake

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