The Adaptation Knowledge Bottleneck: How to
Ease it by Learning from Cases

Kathleen Hanney and Mark T. Keane

Dept. of Computer Science, Trinity College, Dublin, Ireland
E-mail: kathleen.hanney, mark.keane@cs.tcd.ie


Abstract. Assuming that adaptation knowledge will continue to be an
important part of CBR systems, a major challenge for the area is to
overcome the knowledge-engineering problems that arise in its acquisition. 
This paper describes an approach to automating the acquisition
of adaptation knowledge overcoming many of the associated knowledge-engineering 
costs. This approach makes use of inductive techniques, which
learn adaptation knowledge from case comparison. We also show how this
adaptation knowledge can be usefully applied and report on how available 
domain knowledge might be exploited in such an adaptation-rule
learning-system.


References

1.	Agrawal R., Mannila H., Srikant R., Toivonen H., Verkamo A.: Fast Discovery of
Association Rules. In Fayyad U., Piatetsky-Shapiro G., Smyth P., Uthurusamy R.
(Ed.) Advances in Knowledge Discovery and Data Mining. AAAI Press / The MIT
Press (1996)
2.	Hammond K. :Case-Based Planning: Viewing Planning as a Memory Task. Boston:
Academic Press (1989)
3.	Hanney K., Keane M.T., Smyth B., Cunningham P.: Systems, Tasks and Adaptation
Knowledge: Revealing some Revealing dependencies. In Proceedings of the First
International Conference on Case-based Reasoning (1995) 461470.
4.	Hanney K.and Keane M.T.: Learning Adaptation Rules from a Case-Base. In Proceedings 
of the Third European Workshop on Case-based Reasoning (1996) 179192.
5.	Hanney K.: Learning Adaptation Rules from Cases. MSc Thesis, Computer Science
Department, Trinity College Dublin
6.	Langley, P.: Elements of Machine Learning. Morgan Kaufmann (1996)
7.	Leake D., Kinley A., Wilson D.: Learning to Improve Case Adaptation by Introspective 
Reasoning and CBR. In Proceedings of the First International Conference
on Case-based Reasoning (1995) 229240.
8.	Michalski R.: A Theory and Methodology of Inductive Learning. In R. Michalski, J.
Carbonell, T. Mitchell (Ed.) Machine Learning: An Artificial Intelligence Approach
Vol. 1. Morgan Kaufmann (1983)
9.	Smyth B. 1996. Case-based Design. Ph.D. Dissertation, Computer Science Department, 
Trinity College Dublin.
10.	Smyth B., Keane M.T.: Retrieving Adaptable Cases: The Role of Adaptation
Knowledge in Case Retrieval. In Topics in Case-Based Reasoning: Lecture Notes
in Artifical Intelligence 837. Springer Verlag (1994) 209220
11.	Smyth B., Keane M.T.: Remembering to Forget: A Competence-Preserving Deletion 
Policy in Case-Based Systems. In Proceedings International Joint Conference
on Artificial Intelligence, Montreal. (1995)
12.	Sycara E.P.: Using Case-Based Reasoning for Plan Adaptation and Repair. In
Proceedings: Case-Based Reasoning Workshop (1988) 425-434.
