Proceedings of the Annual Meeting of the Cognitive Science Society

Causal models describe some part of the world to allow an information system to perform complex tasks such as diagnosis. However, as many researchers have discovered, such models are rarely complete or consistent. As well, the world may change slightly, making a previously complete model incomplete. A computational theory of the use of causal models must allow for completion and correction in the face of new evidence. This paper discusses these issues with respect to the evolution of a causal model in a diagnosis task. The reasoner's goal is to diagnose a fault in a malfunctioning automobile, and it improves its diagnostic model by comparing it with an instructor's. A general process model is presented with two implementations. Related work in explanation based learning and in incorrect causal models is discussed