This paper brings together factor-based models of case-based reasoning (CBR) and the logical specification of classifiers. Horty [8] has developed the factor-based models of precedent into a theory of precedential constraint. In this paper we combine binary-input classifier logic (BCL) to classifiers and their explanations given by Liu & Lorini [13,14] with Horty’s account of factor-based CBR, since both a classifier and CBR map sets of features to decisions or classifications. We reformulate case bases in the language of BCL, and give several representation results. Furthermore, we show how notions of CBR can be analyzed by notions of classifier explanation.

LIU, X., LORINI, E., ROTOLO, A., SARTOR, G. (2022). Modelling and Explaining Legal Case-based Reasoners through Classifiers. Amsterdam : IOS Press [10.3233/FAIA220451].

Modelling and Explaining Legal Case-based Reasoners through Classifiers

LORINI, Emiliano;ROTOLO, Antonino;SARTOR, Giovanni
2022

Abstract

This paper brings together factor-based models of case-based reasoning (CBR) and the logical specification of classifiers. Horty [8] has developed the factor-based models of precedent into a theory of precedential constraint. In this paper we combine binary-input classifier logic (BCL) to classifiers and their explanations given by Liu & Lorini [13,14] with Horty’s account of factor-based CBR, since both a classifier and CBR map sets of features to decisions or classifications. We reformulate case bases in the language of BCL, and give several representation results. Furthermore, we show how notions of CBR can be analyzed by notions of classifier explanation.
2022
Legal Knowledge and Information Systems
83
92
LIU, X., LORINI, E., ROTOLO, A., SARTOR, G. (2022). Modelling and Explaining Legal Case-based Reasoners through Classifiers. Amsterdam : IOS Press [10.3233/FAIA220451].
LIU, Xinghan ; LORINI, Emiliano ; ROTOLO, Antonino ; SARTOR, Giovanni
File in questo prodotto:
File Dimensione Formato  
Modelling and Explaining Legal Case-based Reasoners through Classifiers.pdf

accesso aperto

Tipo: Versione (PDF) editoriale
Licenza: Licenza per Accesso Aperto. Creative Commons Attribuzione - Non commerciale (CCBYNC)
Dimensione 268.45 kB
Formato Adobe PDF
268.45 kB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/907848
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 8
  • ???jsp.display-item.citation.isi??? ND
social impact