Tractable approximate knowledge fusion using the Horn fragment of serial propositional dynamic logic

  • Authors:

  • Barbara Dunin-Keplicz;Linh Anh Nguyen;Andrzej Szałas

  • Affiliations:

  • Institute of Informatics, University of Warsaw, Banacha 2, 02-097 Warsaw, Poland and Institute of Computer Science, Polish Academy of Sciences, Ordona 21, 01-237 Warsaw, Poland;Institute of Informatics, University of Warsaw, Banacha 2, 02-097 Warsaw, Poland;Institute of Informatics, University of Warsaw, Banacha 2, 02-097 Warsaw, Poland and Department of Computer and Information Science, Linköping University, SE-581 83 Linköping, Sweden

  • Venue:
  • International Journal of Approximate Reasoning
  • Year:
  • 2010

Quantified Score

Hi-index 0.00

Visualization

Abstract

In this paper we investigate a technique for fusing approximate knowledge obtained from distributed, heterogeneous information sources. This issue is substantial, e.g., in modeling multiagent systems, where a group of loosely coupled heterogeneous agents cooperate in achieving a common goal. Information exchange, leading ultimately to knowledge fusion, is a natural and vital ingredient of this process. We use a generalization of rough sets and relations [30], which depends on allowing arbitrary similarity relations. The starting point of this research is [6], where a framework for knowledge fusion in multiagent systems is introduced. Agents' individual perceptual capabilities are represented by similarity relations, further aggregated to express joint capabilities of teams. This aggregation, expressing a shift from individual to social level of agents' activity, has been formalized by means of dynamic logic. The approach of Doherty et al. (2007) [6] uses the full propositional dynamic logic, which does not guarantee tractability of reasoning. Our idea is to adapt the techniques of Nguyen [26-28] to provide an engine for tractable approximate database querying restricted to a Horn fragment of serial dynamic logic. We also show that the obtained formalism is quite powerful in applications.