Exact learning Boolean functions via the monotone theory
Information and Computation
Artificial Intelligence
Modeling Legal Arguments: Reasoning with Cases and Hypotheticals
Modeling Legal Arguments: Reasoning with Cases and Hypotheticals
A Logical Representation for Relevance Criteria
EWCBR '93 Selected papers from the First European Workshop on Topics in Case-Based Reasoning
A Case Base Similarity Framework
EWCBR '96 Proceedings of the Third European Workshop on Advances in Case-Based Reasoning
Analysis of Case-Based Representability of Boolean Functions by Monotone Theory
ALT '98 Proceedings of the 9th International Conference on Algorithmic Learning Theory
Learning Taxonomic Relation by Case-Based Reasoning
ALT '00 Proceedings of the 11th International Conference on Algorithmic Learning Theory
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This paper gives a general framework for analyzing case-based reasoning to represent a lattice-based relation. This is a generalization of our previous work to analyze case-based reasoning over a boolean domain [Satoh98, Satoh00a] and a tree-structured domain [Satoh00b]. In these work, we use a set-inclusion based similarity which is a generalization of a similarity measure proposed in a legal domain [Ashley90, Ashley94]. We show representability of a lattice-based relation, approximation method of constructing a minimal casebase to represent a relation and complexity analysis of the method.