How Case-Based Reasoning and Cooperative
Query Answering Techniques Support RICAD?

Jirapun Daengdej and Dickson Lukose

Department of Mathematics, Statistics and Computing Science
The University of New England
Armidale, NSW 2351, Australia
E-mail: {jirapun, lukose}@neumann.une.edu.au


Abstract. Many Case-Based Reasoning (CBR) systems are built using
the conventional database systems as their case memory. Even though
these Database Management Systems (DBMSs) provide a large number
of advantages, CBR systems developers face one major draw back. That
is, partial-match retrieval is not supported by most of the conventional
DBMSs. To overcome this limitation, we investigated contemporary research 
in CBR and Cooperative Query Answering (CQA). Our finding
indicates that there are a number of issues in CQA that can be solved by
applying some of the innovative techniques developed by the CBR community, 
on the other hand, the CQA provide a number of new features
which enable easy development of CBR systems. The main contribution
of this paper is in explicating how CBR can benefit from the CQA research, 
and how CQA techniques can enhance the CBR systems. Frther,
it describes the CQA features in RICAD (Risk Cost Advisor, our experimental 
CBR system), and how these features enhance its performance.
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