Layered Random Inference Networks

Abstract

Random Boolean Networks (RBN) have been used for decades to study the generic properties of genetic regulatory networks. This paper describes Random Inference Networks (RIN) where the aim is to study the generic properties of inference networks used in high-level information fusion. Previous work has discussed RIN with a linear topology, and this paper introduces RIN with a layered topology. RIN are related to RBN, and exhibit stable, critical and chaotic dynamical regimes. As with RBN, RIN have greatest information propagation in the critical regime. This raises the question as to whether there is a driver for real inference networks to be in the critical regime as has been postulated for genetic regulatory networks. Key Words: situation assessment, inference network, information propagation, criticality