Proceedings of the Annual Meeting of the Cognitive Science Society

When solving a complex problem, gathering relevant information to understand the situation and imposing appropriate interpretations on that information are critical to problem solving success. These two tasks are especially difficult in weak-theory domains -- domains in which knowledge is incomplete, uncertain, and contradictory. In such domains, experts may rely on experience for all aspects of problem solving. We have developed a case-based approach to problem elaboration and interpretation in such domains. An experience-based problem-solver should be able to incrementally acquire information and, in the course of that acquisition, be reminded of multiple cases in order to present multiple viewpoints to problems that present multiple faults. We are addressing issues of 1) elaboration and interpretation of complex problem situations; 2) multiple interpretations; and 3) the role of categories as the foci of reasoning in the context of the Organizational Change Advisor (ORCA). Its model of incremental reminding is a plausible mechanism for this sort of expert problem solving behavior, and one that works well in weak theory domains. Because there is an implicit cost associated with retrieving a complex case, O R C A implements a retrieval time similarity function that requires both general expectations and specific situational relevance be considered before a story is told to the user; this increases the chances that a retrieved case will be useful.