The existing approaches that map the given explicit preferences into standard assumption‐based argumentation (ABA) frameworks reveal some difficulties such as generating a huge number of rules. To overcome them, we present an assumption‐based argumentation framework equipped with preferences (p_ABA). It increases the expressive power of ABA by incorporating preferences between sentences into the framework. The semantics of p_ABA is given by extensions, which are maximal among extensions of ABA with regard to the extension ordering “lifted” from the given sentence ordering. As a theoretical contribution of this study, we show that prioritized logic programming can be formulated as a specific form of p_ABA. The advantage of our approach is that not only does p_ABA enable us to express different kinds of preferences such as preferences over rules, over goals, or over decisions by means of sentence orderings but we can also successfully obtain solutions from extensions of the p_ABA expressing the respective knowledge for various applications such as epistemic reasoning, practical reasoning, and decision making with preferences in a uniform and domain‐independent way without suffering from difficulties of the existing approaches.

In this article, we introduce a new approach of preference-based argumentation frameworks. We first introduce an abstract preference-based argumentation framework (an abstract PAF) which handles static preferences. Naturally extending Dung's acceptability semantics, the semantics of an abstract PAF is given by P-extensions which ensure conflict-freeness w.r.t. the attack relation. Second, as the instantiation of the proposed abstract PAF, we present the non-abstract PAF along with the constrained argumentation framework (CAF) built from a prioritized logic program (PLP). As a result, the proposed framework is able to handle static preferences as well as integrity constraints in argumentation. Third, by providing a hierarchical PLP to deal with dynamic preferences in the context of logic programming, we extend the framework to a dynamic PAF built from a hierarchical PLP to deal with dynamic preferences in the context of argumentation. Fourth, we not only show the correspondence between stable extensions of the non-abstract argumentation framework and paraconsistent stable models of an extended logic program but also show the correspondence between the semantics of such PAFs and the semantics of the underling prioritized logic programs based on the order-preserving mapping functions. Finally, the proposed framework is also applied to interactions between agents in multi-agent systems.

Recently Web-pages on the World Wide Web are explosively increasing, and it is now required for portal sites such as Yahoo! service having directory-style search engines to classify Web-pages into many categories automatically. This paper investigates how rough settheory can help select relevant features for Web-page classification. Our experimental results show that the combination of the rough set-aided feature selection method and the Support Vector Machine with a linear kernel is quite useful in practice to classify Web-pages into many categories because not only the performance gives acceptable accuracy but also the high dimensionality reduction is achieved without depending on arbitrary thresholds for feature selection.