Meta-Cases: Explaining Case-Based Reasoning

  title={Meta-Cases: Explaining Case-Based Reasoning},
  author={Ashok K. Goel and J. William Murdock},
AI research on case-based reasoning has led to the development of many laboratory case-based systems. As we move towards introducing these systems into work environments, explaining the processes of case-based reasoning is becoming an increasingly important issue. In this paper we describe the notion of a meta-case for illustrating, explaining and justifying case-based reasoning. A meta-case contains a trace of the processing in a problem-solving episode, and provides an explanation of the… 
Case-Based Reasoning: A Concise Introduction
The aim of this book is to present a concise introduction to case-based reasoning providing the essential building blocks for the design of case- based reasoning systems, as well as to bring together the main research lines in this field to encourage students to solve current CBR challenges.
Meta-case-Based Reasoning: Using Functional Models to Adapt Case-Based Agents
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Meta-case-based reasoning: self-improvement through self-understanding
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Interactive Case-Based Reasoning in Sequential Diagnosis
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Case-Based Argumentation via Process Models
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What's Hot in Case-Based Reasoning
Recent developments in research on case-based reasoning are summarized based partly on the recent Twenty Fourth International Conference on Case-Based Reasoning.
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The Massive Memory Architecture, an experimental framework for experience-based learning and reasoning, is described and the hypothesis that learning methods are inference methods able to inspect the problem solving process and modify the system itself so as to improve its behavior is put forth.
Learning by Analogy: Formulating and Generalizing Plans from Past Experience
This chapter outlines a theory of analogical problem solving based on an extension to means-ends analysis and an analogical transformation process is developed to extract knowledge from past successful problem-solving situations that bear a strong similarity to the current problem.
Explanatory Interface in Interactive Design Environments
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Knowledge-based tutoring: the GUIDON program
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Heuristic Classification
Reflective, Self-Adaptive Problem Solvers
To enable a system with the capability of self-adaptation, a framework for endowing it with the competence of reflection is developed, which captures a deep comprehension of the system's task structure, world knowledge and their inter-dependencies.
Automating Knowledge Acquisition for Expert Systems
This book describes the principles that guided the expert systems research group's work, looks in detail at the design and operation of each tool or methodology, and reports some lessons learned from the enterprise.