A traditional Belief Rule Base (BRB) is constructed under the conjunctive assumption (conjunctive BRB), which requires covering the traversal combinations of the referenced values for the attributes. Consequentially, a traditional conjunctive BRB may have to face the combinatorial explosion problem when there are too many attributes and/or referenced values for the attributes. It is difficult or at least expensive to construct a complete conjunctive BRB, while it is easy to derive one or several conjunctive rules. Comparatively, a BRB under the disjunctive assumption (disjunctive BRB) requires only covering the referenced values for the attributes instead of the traversal combination of them. Thus, the combinatorial explosion problem can be avoided. However, it is difficult to directly obtain a disjunctive BRB from either historical data or experts' knowledge. To combine the advantages of both conjunctive and disjunctive BRBs, a new approach is proposed to construct a disjunctive BRB using a limited number of conjunctive rules (insufficient to construct a complete conjunctive BRB). In the new disjunctive BRB modeling approach, each disjunctive rule is derived by quantifying its correlation with one or multiple conjunctive rules. To do so, two means for belief generation are proposed, namely, equal probability and self-organizing mapping (SOM). Two cases are studied for validating the efficiency of the proposed approach. The results by the disjunctive BRB show consistency with those derived by the conjunctive BRB as well as other approaches, which validates the efficiency of the proposed approach considering that the disjunctive BRB is constructed with only a limited number of conjunctive rules.

This paper concerns a distributed argumentation system where different agents are equipped with argumentative knowledge base (henceforth referred as KB) within which conflict arguments are represented using attacking relations. This paper proposes the notion of "defensibility" of an argument in a distributed argumentation system and a multi-party dialogue game to compute the defensibility of an argument. In our approach, we have proposed the notion of critical factor, legal move function and critical countermeasure, for avoiding idle attack and invalid attack in the course of dialogues. It is anticipated that this research will contribute to argumentation research in MAS.

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One of the important issues faced in the domain of target classification in wireless sensor networks is the restricted lifetime of individual sensors, caused by limited battery capacity. Although the base station usually has sufficient energy supply and computational power, it is often deemed to be the object of enemy invading hostile terrain. Hence, minimizing energy consumption of sensors while maintaining a given classification accuracy is a key problem in this research area, especially for sensitive data applications. This paper proposes a rule based feature selection approach rather than all-features approach that aims at increasing the energy efficiency of the system without losing much classification accuracy. In experiments, the feasibility and effectiveness of our approach are demonstrated empirically.

It is a great challenge to achieve interpretable collaborative object classification in multisensor networks. In this situation, argumentation-based object classification has been considered a promising paradigm, due to its natural means of justifying and explaining complicated decision making within multiple agents. However, disagreements between sensor agents are often encountered because of various object category levels. To address this category of granularity inconsistent problem in multi-sensor collaborative object classification tasks, we propose a cognitive context knowledge-enriched method for classification conflict resolution. The cognitive context is concerned, in this paper, to investigate how rich contextual knowledge-equipped cognitive agents can facilitate semantic consensus in argumentation-based object classification. The empirical evaluation demonstrates the effectiveness of our method with improvement over state-of-the-art, especially in the presence of noisy sensor data, while giving argumentative explanations. Therefore, it is suggested that people who can benefit from the proposed method in this paper are the human user of multi-sensor object classification systems, in which explaining decision support is one of the important factors concerned.

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