A
Agnar Aamodt
Researcher at Norwegian University of Science and Technology
Publications - 96
Citations - 9008
Agnar Aamodt is an academic researcher from Norwegian University of Science and Technology. The author has contributed to research in topics: Case-based reasoning & Domain knowledge. The author has an hindex of 27, co-authored 96 publications receiving 8612 citations. Previous affiliations of Agnar Aamodt include Spanish National Research Council & SINTEF.
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Case-based reasoning: foundational issues, methodological variations, and system approaches
Agnar Aamodt,Enric Plaza +1 more
TL;DR: An overview of the foundational issues related to case-based reasoning is given, some of the leading methodological approaches within the field are described, and the current state of the field is exemplified through pointers to some systems.
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Retrieval, reuse, revision and retention in case-based reasoning
Ramon López de Mántaras,David McSherry,Derek Bridge,David B. Leake,Barry Smyth,Susan Craw,Boi Faltings,Mary Lou Maher,Michael T. Cox,Kenneth D. Forbus,Mark T. Keane,Agnar Aamodt,Ian Watson +12 more
TL;DR: The cognitive science foundations of CBR and its relationship to analogical reasoning are examined, and a representative selection ofCBR research in the past few decades on aspects of retrieval, reuse, revision and retention are reviewed.
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Explanation in Case-Based Reasoning---Perspectives and Goals
TL;DR: A framework for explanation in case-based reasoning (CBR) based on explanation goals is presented, and ways that the goals of the user and system designer should be taken into account when deciding what is a good explanation for a given CBR system are proposed.
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Different roles and mutual dependencies of data, information, and knowledge—an AI perspective on their integration
Agnar Aamodt,Mads Nygård +1 more
TL;DR: It is shown that a specific problem solving episode, or case, may be viewed as data, information, or knowledge, depending on its role in decision making and learning from experience, and a conceptual framework for integration is suggested by focusing on their different roles and frames of reference within a decision-making process.
A knowledge-intensive, integrated approach to problem solving and sustained learning
TL;DR: A unified framework is developed through an analysis of various types, aspects and roles of knowledge relevant for the kind of systems described above, which aims to provide an environment for discussion of different approaches to knowledge intensive problem solving and learning.