 In the similarity approach, as developed in Niiniluoto (1987), closeness to the truth is explicated “locally” by means of the distances of partial answers \(g\) in \(D(B)\) to the target \(h^*\) in a cognitive problem \(B\). For this purpose, we need a function \(d\) which expresses the distance \(d(h_{i}, h_{j}) =: d_{ij}\) between two arbitrary elements of \(B\). By normalization, we may choose \(0 \le d_{ij} \le 1\). The choice of \(d\) depends on the cognitive problem \(B\), and makes use of the metric structure of \(B\) (e.g., if \(B\) is a subspace of the real numbers \(\Re)\) or the syntactic similarity between the statements in \(B\). Then, for a partial answer \(g\), we let \(D_{\min}(h_{i}, g)\) be the minimum distance of the disjuncts in \(g\) from \(h_{i}\), and \(D_{\rmsum}(h_{i}, g)\) the normalized sum of the distances of the disjuncts of \(g\) from \(h_{i}\). Then \(D_{\min}(h_{i}, g)\) tells how close to \(h_{i}\) hypothesis \(g\) is, so that the degree of approximate truth of \(g\) (relative to the target \(h^*\)) is \(1 - D_{\min}(h^*, g)\). On the other hand, \(D_{\rmsum}(h_{i}, g)\) includes a penalty for all the mistakes that \(g\) allows relative to \(h_{i}\). The min-sum measure 