Cautious Monotonicity in Case-Based Reasoning with Abstract Argumentation - Paulino-Passos,G; Toni,F (2020)
Recently, abstract argumentation-based models of case-based reasoning($AA{\text -}CBR$ in short) have been proposed, originally inspired by thelegal domain, but also applicable as classifiers in different scenarios,including image classification, sentiment analysis of text, and in predictingthe passage of bills in the UK Parliament. However, the formal properties of$AA{\text -}CBR$ as a reasoning system remain largely unexplored. In thispaper, we focus on analysing the non-monotonicity properties of a regularversion of $AA{\text -}CBR$ (that we call $AA{\text -}CBR_{\succeq}$).Specifically, we prove that $AA{\text -}CBR_{\succeq}$ is not cautiouslymonotonic, a property frequently considered desirable in the literature ofnon-monotonic reasoning. We then define a variation of $AA{\text-}CBR_{\succeq}$ which is cautiously monotonic, and provide an algorithm forobtaining it. Further, we prove that such variation is equivalent to using$AA{\text -}CBR_{\succeq}$ with a restricted casebase consisting of all"surprising" cases in the original casebase.