---
title: "Reasoning Symbolically About Partially Matched Cases"
authors:
  - "Kevin D. Ashley"
  - "Vincent Aleven"
doi: ""
url: "https://ijcai.org/proceedings/1997/50"
pdf: "https://ijcai.org/proceedings/1997/0050.pdf"
venue: "Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence"
year: 1997
pages: "335-340"
track: "Computer-aided Education"
keywords:
---

# Reasoning Symbolically About Partially Matched Cases

Kevin D. Ashley, Vincent Aleven

*Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence*, pages 335-340.

## Abstract

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