An Empirical Evaluation of Automated Knowledge Discovery in a Complex Domain

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Automatically acquiring knowledge in complex and possibly dynamic domains is an interesting, non-trivial problem. Case-based reasoning (CBR) systems are particularly well suited to the tasks of knowledge discovery and exploitation, and a rich set of methodologies and techniques exist to exploit the existing knowledge in a CBR system. However, the process of automatic knowledge discovery appears to be an area in which little research has been conducted within the CBR community. An approach to automatically acquiring knowledge in complex domains is automatic case elicitation (ACE), a learning technique whereby a CBR system automatically acquires knowledge in its domain through real-time exploration and interaction with its environment. The results of empirical testing in the domain of chess suggest that it is possible for a CBR system using ACE to successfully discover and exploit knowledge in an unsupervised manner. Results also indicate that the ability to explore is crucial for the success of an unsupervised CBR learner, and that exploration can lead to superior performance by discovering solutions to problems which would not otherwise be suggested or found by static or imperfect search mechanisms.