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<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/dlce7tvip5e5rlsv2ty3chnfpu" style="color: black;">Künstliche Intelligenz</a>
We introduce and investigate formal quantitative measures of inconsistency between the beliefs of agents in multi-agent systems. We start by recalling a well-known model of belief in multi-agent systems, and then, using this model, present two classes of inconsistency metrics. First, we consider metrics that attempt to characterise the overall degree of inconsistency of a multi-agent system in a single numeric value, where inconsistency is considered to be individuals within the system having<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s13218-014-0306-3">doi:10.1007/s13218-014-0306-3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/i2ioplm5nveexev4tok5dpdn7y">fatcat:i2ioplm5nveexev4tok5dpdn7y</a> </span>
more »... ntradictory beliefs. While this metric is useful as a high-level indicator of the degree of inconsistency between the beliefs of members of a multi-agent system, it is of limited value for understanding the structure of inconsistency in a system: it gives no indication of the sources of inconsistency. We therefore introduce metrics that quantify for a given individual the extent to which that individual is in conflict with other members of the society. These metrics are based on power indices, which were developed within the cooperative game theory community in order to understand the power that individuals wield in cooperative settings.
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