Abstract
In the literature there have been a few works [1,2,3,4] that have dealt with obtaining metrics from associative, commutative, and monotonically increasing fuzzy logic connectives such as t-norms, t-conorms, copulas, and quasi-copulas. Recently, it has been shown [9] that a distance function \(d_I\) can also be obtained from fuzzy implications which do not satisfy any of the above properties. This work studies the above distance along two aspects. Firstly, we investigate those implications I that satisfy a particular form of transitivity, viz. the \(S_\mathbf{LK}\) transitivity, that is both necessary and sufficient for the proposed distance to be a metric. In the recent past, monodistances w.r.t. a ternary relation, called the betweenness relation, defined on a set, have garnered a lot of attention for their important role in decision making and penalty-based data aggregation. One of the major challenges herein is that of obtaining monodistances on a given betweenness set (\(\mathcal {X},\mathrm {B}\)). By characterising betweenness relations that can be obtained from a bounded below poset, our second contribution in this work is in showing that a monodistance on such betweenness sets (\(\mathcal {X},\mathrm {B}\)) can be obtained through \(d_I\). Our work seems to suggest that fuzzy implications are rather a natural choice for constructing monodistances.
Supported by SERB under the project MTR/2020/000506.
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Nanavati, K., Gupta, M., Jayaram, B. (2022). Monodistances from Fuzzy Implications. In: Ciucci, D., et al. Information Processing and Management of Uncertainty in Knowledge-Based Systems. IPMU 2022. Communications in Computer and Information Science, vol 1601. Springer, Cham. https://doi.org/10.1007/978-3-031-08971-8_15
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