In this section
@inproceedings{Paulino:2023:10.3233/FAIA230950,
author = {Paulino, Passos G and Toni, F},
doi = {10.3233/FAIA230950},
pages = {95--1000},
publisher = {IOS Press},
title = {Learning case relevance in case-based reasoning with abstract argumentation},
url = {http://dx.doi.org/10.3233/FAIA230950},
year = {2023}
}
TY - CPAPER
AB - Case-based reasoning is known to play an important role in several legal settings. We focus on a recent approach to case-based reasoning, supported by an instantiation of abstract argumentation whereby arguments represent cases and attack between arguments results from outcome disagreement between cases and a notion of relevance. We explore how relevance can be learnt automatically with the help of decision trees, and explore the combination of case-based reasoning with abstract argumentation (AA-CBR) and learning of case relevance for prediction in legal settings. Specifically, we show that, for two legal datasets, AA-CBR with decision-tree-based learning of case relevance performs competitively in comparison with decision trees, and that AA-CBR with decision-tree-based learning of case relevance results in a more compact representation than their decision tree counterparts, which could facilitate cognitively tractable explanations.
AU - Paulino,Passos G
AU - Toni,F
DO - 10.3233/FAIA230950
EP - 1000
PB - IOS Press
PY - 2023///
SN - 0922-6389
SP - 95
TI - Learning case relevance in case-based reasoning with abstract argumentation
UR - http://dx.doi.org/10.3233/FAIA230950
UR - http://hdl.handle.net/10044/1/114935
ER -
Artificial Intelligence Network
South Kensington Campus
Imperial College London
SW7 2AZ
To reach the elected speaker of the network, Dr Rossella Arcucci, please contact:
To reach the network manager, Diana O'Malley - including to join the network - please contact: