Distributed Process Monitoring for Multiagent Systems Through Cognitive Learning

Abstract

Multi-agent systems are usually large-scaled with a growing degree of intelligence and integration. Direct applications of traditional (centralized) methods will become incompetent for effective process monitoring of multi-agent systems. It necessitates the cognitive learning strategies that determine the effective interactions among subsystems or individuals. Therefore, in order to improve monitoring performance, this paper targets the development of a new distributed process monitoring method that has the cognitive learning ability by embedding an adaptive pickup rule. The proposed cognitive learning-based method can reduce the computation loads in both off-line and online phases because only necessary information exchange (or communication topology) is involved. Furthermore, the threshold used for system monitoring is obtained by developing a fast search algorithm based on statistical learning theory. Case studies on the wastewater treatment system, which can be regarded as a typical multi-agent system, demonstrate the superiority of the proposed distributed process-monitoring method.