Traditionally case-based reasoning (CBR) systems have relied on information manually provided by domain experts to form their knowledge bases. Additional domain knowledge is often used to improve performance of such systems. A less costly method of knowledge acquisition is automatic case elicitation, a learning technique in which a CBR system acquires knowledge automatically during real-time interaction with its environment with no prior domain knowledge (e.g., rules or cases). For problems that are observable, discrete and either deterministic or strategic in nature, automatic case elicitation can lead to the development of a self-taught knowledgeable agent. This paper describes the use of automatic case elicitation in CHEBR, a CHEckers case-Based Reasoner that employs self-taught knowledgeable agents. CHEBR was tested using model-based versus non-model-based matching to evaluate its ability to learn without predefined domain knowledge. The results suggest that additional experience can substitute for the inclusion of precoded model-based knowledge.