On the Effectiveness of Automatic Case Elicitation in a More Complex Domain

Automatic case elicitation (ACE) is a learning technique in which a case-based reasoning system acquires knowledge automatically from scratch through repeated real-time trial and error interaction with its environment without dependence on pre-coded domain knowledge. ACE represents an alternative to manually constructed case bases and domain specific techniques, and is generally applicable to any domain for which knowledge can be obtained from a series of observations of an environment (e.g., checkers or massively multiplayer games). A priority is placed on maintaining the flexibility necessary to learn new domains with only negligible manual configuration. We found during testing that the current approach to ACE with a reliance on experience and exploration, while quite capable in the domain of checkers, did not perform adequately in the exponentially more complex domain of chess. Our results suggest that experience alone, without the ability to adapt for case differences between new and prior cases, is insufficient in more complex domains.