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The paper contributes to the study of belief merging in many-valued logics, taking both a theoretical and implementation approach motivated by applications in real-world scenarios. We focus, in particular, on the Horn fragment of signed logic. On the one hand, a characterization of the class of models of a signed regular Horn formula and a sufficient condition for a signed Horn merging operator to satisfy logical IC-postulates are provided. On the other hand, an implementation of the belief merging process in the signed regular Horn fragment is presented. [ABSTRACT FROM AUTHOR]

The merging/fusion of belief/data collections in propositional logic form is a topic that has received due attention within the domains of database and AI research. A distinction can be made between two types of scenarios to which the process of merging can be applied. In the first type, the collections represent preferences, such as the voting choices of a group of people, that need to be aggregated so as to give a consistent result that in some way best represents the collective judgement of the group. In the second type, the collections represent factual data that is to be aggregated with an aim of obtaining a result that maximises factual correctness. After introducing a general framework for belief merging via some prominent literature on the topic, this paper then introduces and considers a method for belief merging with the second type of scenario in mind. Its suitability is corroborated by demonstrating how it can be seen as a special case of a merging procedure that combines aggregation of probabilities and maximisation of expected truthlikeness. [ABSTRACT FROM AUTHOR]

Recently, belief change within the framework of fragments of propositional logic has gained increasing attention. Previous research focused on belief contraction and belief revision on the Horn fragment. However, the problem of belief merging within fragments of propositional logic has been mostly neglected so far. We present a general approach to defining new merging operators derived from existing ones such that the result of merging remains in the fragment under consideration. Our approach is not limited to the case of Horn fragment; it is applicable to any fragment of propositional logic characterized by a closure property on the sets of models of its formulæ. We study the logical properties of the proposed operators regarding satisfaction of merging postulates, considering, in particular, distance-based merging operators for Horn and Krom fragments. [ABSTRACT FROM AUTHOR]

The present paper introduces a belief merging procedure by majority using the standard format of Adaptive Logics. The core structure of the logic ADM c ( Adaptive Doxastic Merging by Counting) consists in the formulation of the conflicts arising from the belief bases of the agents involved in the procedure. A strategy is then defined both semantically and proof-theoretically which selects the consistent contents answering to a majority principle. The results obtained are proven to be equivalent to a standard majority operator for bases with partial support. [ABSTRACT FROM AUTHOR]

Abstract: Recently, several belief negotiation models have been introduced to deal with the problem of belief merging. A negotiation model usually consists of two functions: a negotiation function and a weakening function. A negotiation function is defined to choose the weakest sources and these sources will weaken their point of view using a weakening function. However, the currently available belief negotiation models are based on classical logic, which makes them difficult to define weakening functions. In this paper, we define a prioritized belief negotiation model in the framework of possibilistic logic. The priority between formulae provides us with important information to decide which beliefs should be discarded. The problem of merging uncertain information from different sources is then solved by two steps. First, beliefs in the original knowledge bases will be weakened to resolve inconsistencies among them. This step is based on a prioritized belief negotiation model. Second, the knowledge bases obtained by the first step are combined using a conjunctive operator which may have a reinforcement effect in possibilistic logic. [Copyright &y& Elsevier]

In this paper, we propose an adaptive algorithm for merging n (⩾2) prioritized knowledge bases which takes into account the degrees of conflict and agreement among these knowledge bases. The algorithm first selects largely partially maximal consistent subsets (LPMCS) of sources by assessing how (partially) consistent the information in the subset is. Then within each of these created subsets, a maximal consistent subset is further selected and knowledge bases in it are merged with a suitable conjunctive operator based on the degree of agreement among them. This result is then merged with the remaining knowledge bases in the corresponding LPMCS in the second step through the relaxation of the minimum operator. Finally, the knowledge bases obtained from the second step are merged by a maximum operator. In comparison with other merging methods, our approach is more context dependent and is especially useful when most sources of information are in conflict. [ABSTRACT FROM AUTHOR]