Meta-Cases: Explaining Case-Based Reasoning

@inproceedings{Goel1996MetaCasesEC,
  title={Meta-Cases: Explaining Case-Based Reasoning},
  author={Ashok K. Goel and J. William Murdock},
  booktitle={EWCBR},
  year={1996}
}
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… 
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References

SHOWING 1-10 OF 41 REFERENCES
Explaining control strategies in problem solving
TLDR
The authors propose generic-task methodology as one way to build knowledge-based systems that contain basic explanation constructs at appropriate abstraction levels and note that most current approaches to knowledge- based system construction require expressing knowledge and control at such low levels that it's hard to give high-level explanations.
Derivational analogy: a theory of reconstructive problem solving and expertise acquisition
Abstract : Derivational analogy, a method of solving problems based on the transfer of past experience to new problem situations, is discussed in the context of other general approaches to problem
A Reflective Architecture for Integrated Memory-Based Learning and Reasoning
TLDR
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
TLDR
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
TLDR
This work considers the two related problems of explaining the system’s reasoning and the design generated by the system, and analyzes the content of explanations of design reasoning and design solutions in the domain of physical devices.
Knowledge-based tutoring: the GUIDON program
TLDR
This is the first attempt to adapt a rule base for tutoring and opens the door to what will most likely be a dramatic growth in interest in the use of expert systems for teaching.
Heuristic Classification
Reflective, Self-Adaptive Problem Solvers
TLDR
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
TLDR
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.
...
...