Reasonign Symbolically About Partially Matched Cases

摘要

In teaching case-based argumentation skills, the CATO progrma, an intelligent learning environmetn, guides studients' assessments of partial matches between problems and cases by generating alternative itnerpretations of the similarities and differences. CATO's Factor Hierarchy captures information about the significance of similarities and differences given the normative pruposes of the domain classification. Its algorithms for emphasizing or downplaying significance tailor interpretation to the comparison context, block interpretations strongly contradicted by other factors and strategically determine how and how abstractly to characterize a difference. An empirical evalaution confirmed CATO's effectiveness is teaching basic argumentation skills.