Selecting and Comparing Multiple Cases to Maximise
Result Quality after Adaptation in Case-Based Adaptive
Scheduling

Steve Scott, Hugh Osborne, and Ron Simpson

School of Computing and Mathematics, University of Huddersfield
Queensgate, Huddersfield, HDI 3DH, UK,
{s.scott,h.r.osborne.r.s.simpson}@hud.ac.uk



Abstract. Recent Case-Based Reasoning research has begun to refocus
attention on the problem of automatic adaptation of the retrieved case to
give a fuller solution to the new problem. Such work has highlighted
problems with the usefulness of similarity assessment of cases where
adaptation is involved. As a response to this, methods of case selection
are evolving that take adaptation into account. This current work looks
more closely at the relationship between selection and adaptation. It
considers experimental evidence considering adaptation of multiple
cases for one problem. It argues that selection of the best case after
adaptation will often make more efficient use of case knowledge than
any attempt to pre-select a single case for adaptation.
References

1.	Bergmann, R., Wilke, W., Towards a New Formal Model of Transformational Adaptation
in Case-Based Reasoning, in Proceedings of ECAI98, Prade (Ed.) Wiley & Sons, 1998
2.	Bergmann, R., Stahl, A., Similarity Measures for Object Oriented Case Representations
in Advances in Case-Based Reasoning, Proceedings of the third European Workshop on
Case-Based Reasoning, Lecture Notes in Artificial Intelligence, Springer Verlag 1996
3.	Cunningham, P., Smyth, B., On the use of CBR in Optimisation Problems such as the
TSP, in Case-Based Reasoning Research and Development, Veloso, Aamodt (eds.),
Lecture Notes in Artificial Intelligence, Springer Verlag 1995
4.	Hanney, K., Keane, M. T., Ease it by Learning from Cases, in Case-Based Reasoning
Research and Development, Lecture Notes in Artificial Intelligence, Springer Verlag 1997
5.	Janetko, D., Wess, S., Melis, E., Goal Driven Similarity Assessment, Technical Report
SR-92-05, Universitt des Saarlandes, Saarbrcken, Germany, 1992
6.	Kontkanen, P., Myllymki, P., Silander, T., Tirri, H., On Bayesian Case Matching, in
Advances in Case-Based Reasoning, Smyth & Cunningham (eds.), Lecture Notes in
Artificial Intelligence, Springer Verlag 1998
7.	Osborne, H. R., Bridge, D., Similarity Metrics: A Formal Unification of Cardinal and
Non-Cardinal Similarity Measures, in Case-Based Reasoning Research and
Development, Lecture Notes in Artificial Intelligence, Springer Verlag 1997
8.	Richter, M. M., Classification and Learning of similarity Measures in Proceedings der
Jahrestangung der Gesellschaft fur Klassifikation, Opiz, Lausen Klar (ed.) Studies in
Classification, Data Analysis and Knowledge Organisation, Springer Verlag, 1992
9.	Smyth, B., Keane, M. T., Adaptation Guided Retrieval: questioning the similarity
assumption in reasoning, in Artificial Intelligence, Volume 102, pp 249-293, 1998
10.	Scott, S. Separating Constraint Dimensions in a Scheduling Problem to Reduce Search
Space, In Proceedings of ECAI98, Prade (Ed.) Wiley & Sons, 1998
11.	Scott, S., Simpson, R. M. Case-Bases Incorporating Scheduling Constraint Dimensions:
Experiences in Nurse Rostering, In Advances in Case-Based Reasoning, Smyth &
Cunningham (eds.), Lecture Notes in Artificial Intelligence, Springer Verlag 1998
12.	Scott, S., Osborne, H., Simpson, R., Assessing Case Value in Case-Based Reasoning with
Adaptation, In Proceedings of the World Multiconference on Systems, Cybernetics and
Informatics, IIS, 1999
13.	Scott, S., Osborne, H., Simpson, R., The Case Selection Problem in Case-Based
Reasoning - Making the best use of Case Knowledge for Adaptive Scheduling, under
consideration.
14.	Sushil, J. L., Li, G., Augmenting Genetic Algorithms with Memory to Solve Travelling
Salesman Problems, Proceedings of the Joint Conference on Information Sciences, Duke
University, 1997
15.	Watson, I., Applying Case-Based Reasoning, Morgan Kaufmann, 1997
16.	Dietterich, T. G., Machine Learning: Four Current Directions in AI Magazine. 18 (4),
97-136,	1997
