Abstract
In this paper we investigate a technique for fusing approximate knowledge obtained from distributed, heterogeneous information sources. We use a generalization of rough sets and relations [14], which depends on allowing arbitrary similarity relations. The starting point of this research is [2], where a framework for knowledge fusion in multi-agent systems is introduced. Agent’s individual perceptual capabilities are represented by similarity relations, further aggregated to express joint capabilities of teams. This aggregation, allowing a shift from individual to social level, has been formalized by means of dynamic logic. The approach of [2] uses the full propositional dynamic logic, not guaranteeing the tractability of reasoning. Therefore the results of [11, 12, 13] are adapted to provide a technical engine for tractable approximate database querying restricted to a Horn fragment of serial PDL. We also show that the obtained formalism is quite powerful in applications.
Supported by the MNiSW grants N N206 399134 and N N206 399334.
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Dunin-Kȩplicz, B., Nguyen, L.A., Szałas, A. (2009). Fusing Approximate Knowledge from Distributed Sources. In: Papadopoulos, G.A., Badica, C. (eds) Intelligent Distributed Computing III. Studies in Computational Intelligence, vol 237. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03214-1_8
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DOI: https://doi.org/10.1007/978-3-642-03214-1_8
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