In this paper, we propose a granularity-based framework of deduction, induction, and abduction using variable precision rough set models proposed by Ziarko and measure-based semantics for modal logic proposed by Murai et al. The proposed framework is based on α-level fuzzy measure models on the basis of background knowledge, as described in the paper. In the proposed framework, deduction, induction, and abduction are characterized as reasoning processes based on typical situations about the facts and rules used in these processes. Using variable precision rough set models, we consider β-lower approximation of truth sets of nonmodal sentences as typical situations of the given facts and rules, instead of the truth sets of the sentences as correct representations of the facts and rules. Moreover, we represent deduction, induction, and abduction as relationships between typical situations. 相似文献
The nucleophilic living ring‐opening polymerization of N‐substituted glycine N‐carboxyanhydrides using solid‐phase synthesis resins is reported. By variation of experimental parameters, products with near Poisson distributions are obtained. As opposed to reversible deactivation radical polymerization, the living polymerization is demonstrated to be viable to high monomer conversion and through multiple monomer addition steps. Successful preparation of a multiblock copolypeptoid is proof for a highly living and robust character of the solid‐phase peptoid polymerization.
