Abstract

A surprisingly large number of research disciplines have contributed towards the development of knowledge on lazy problem solving, which is characterized by its storage of ground cases and its demand-driven response to queries. Case-based reasoning (CBR) is an alternative, increasingly popular approach for designing expert systems that implements this approach. This paper lists pointers to some contributions in some related disciplines that offer insights for CBR research. We then outline a small number of Navy applications based on this approach that demonstrate its breadth of applicability. Finally, we list a few successful and failed attempts to apply CBR, and list some predictions on the future roles of CBR in applications.

Author Keywords: Case-based reasoning; Machine learning; Lazy learning

Article Outline

1. Case-based reasoning

2. The omnipresence of case-based reasoning

2.1. Omniprescence in science

2.1.1. Cognitive psychology

2.1.2. Pattern recognition

2.1.3. Machine learning

2.1.3.1. Eager realizations of lazy approaches

2.1.3.2. Lazy realizations of eager approaches

2.1.3.3. Loose integrations of lazy and eager approaches

2.1.4. Summary

2.2. Omnipresence in application

2.2.1. Feature selection

2.2.2. Robotic navigation

2.2.3. Interactive troubleshooting

2.2.4. Summary

3. Successes and failures of case-based reasoning

3.1. Successes

3.1.1. Interactive troubleshooting

3.1.2. Recommenders

3.1.3. Internet commerce

3.2. Failures

3.2.1. Corporate support

3.2.2. Knowledge acquisition

3.2.3. Scope of applicability

4. Predictions for case-based reasoning

5. Summary

Acknowledgements

References

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