Using symbolic descriptions to explain similarity on CBR

Authors:
Eva Armengol;Enric Plaza
Affiliations:
Artificial Intelligence Institute (IIIA-CSIC);Artificial Intelligence Institute (IIIA-CSIC)
Venue:
Proceedings of the 2005 conference on Artificial Intelligence Research and Development
Year:
2005

Citing 6
Cited 1

Case-based reasoning: foundational issues, methodological variations, and system approaches

AI Communications
Explaining collaborative filtering recommendations

CSCW '00 Proceedings of the 2000 ACM conference on Computer supported cooperative work
Bottom-Up Induction of Feature Terms

Machine Learning
Inductive Logic Programming: Techniques and Applications

Inductive Logic Programming: Techniques and Applications
ExpertClerk: A Conversational Case-Based Reasoning Tool forDeveloping Salesclerk Agents in E-Commerce Webshops

Artificial Intelligence Review
The Explanatory Power of Symbolic Similarity in Case-Based Reasoning

Artificial Intelligence Review

Discovering Plausible Explanations of Carcinogenecity in Chemical Compounds

MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

The explanation of the results is a key point of automatic problem solvers. CBR systems solve a new problem by assessing its similarity with already solved cases and they commonly show the user the set of cases that have been assessed as the most similar to the new problem. Using the notion of symbolic similarity, our proposal is to show the user a symbolic description that makes explicit what the new problem has in common with the retrieved cases. In particular, we use the notion of anti-unification to build this symbolic description.