Experience Fusion as Integration of Distributed Structured Knowledge

Provide in this paper we address and discuss the problem of learning and fusion of graphical models using structure learning algorithms. We present a new enhanced parameterized structure learning and experience fusion approach. A concurrent fusion method to aggregate expert knowledge stored in distributed knowledge bases or probability distributions is also described. Experimental results of a case study show that our approach can improve the efficiency of learning structure algorithms for knowledge fusion applications.