Evaluating the Effectiveness of Exploration and Accumulated Experience in Automatic Case Elicitation

Abstract

Abstract. Non-learning problem solvers have been applied to many interesting and complex domains. Experience-based learning techniques have been developed to augment the capabilities of certain non-learning problem solvers in order to improve overall performance. An alternative approach to enhancing preexisting systems is automatic case elicitation, a learning technique in which a case-based reasoning system with no prior domain knowledge acquires knowledge automatically through real-time exploration and interaction with its environment. In empirical testing in the domain of checkers, results suggest not only that experience can substitute for the inclusion of pre-coded model-based knowledge, but also that the ability to explore is crucial to the performance of automatic case elicitation.

A general-purpose framework for learning by observation

“…Automatic Case Acquisition (ACE) is an approach for generating cases that has been used in the games of chess [43] and checkers [44]. When presented with a problem to solve, ACE randomly selects an action to perform and uses that problem-solution pair to create a new case.…”