25th International Workshop on Qualitative Reasoning

Reasoning with incomplete or potentially incorrect knowledge is an important challenge for Artificial Intelligence. This paper presents a system that revises its knowledge about dynamic systems by constructing and evaluating explanations. Conceptual knowledge is represented using compositional model fragments, which are used to explain phenomena via model formulation. Metareasoning is used to (1) score the resulting explanations numerically along several dimensions and (2) evaluate preferred explanations for global consistency. Global inconsistencies cause the system to favor alternative explanations and thereby change its beliefs. We simulate the belief changes of several students during clinical interviews about how the seasons change. We show that qualitative models reasonably represent student knowledge, and that our system revises its beliefs in a fashion similar to the students.