Estimating global stress environment by observing local behavior in distributed multiagent systems

A multiagent system can be considered survivable if it adapts itself to varying stresses without considerable performance degradation. Such an adaptivity comprises of identifying the behavior of the agents in a society, relating them to stress situations, and then invoking control rules. This problem is a hard one, especially in distributed multiagent systems wherein the agent behaviors tend to be nonlinear and dynamic. In this paper, we study a supply chain planning system implemented in COUGAAR (Cognitive Agent Architecture) and develop a methodology for identifying the behavior of agents through their behavioral parameters, and relating those parameters to stress situations. One important aspect of our approach is that we identify the stress situations of agents in the society by observing local behavior of one representative agent. This approach is motivated by the fact that a local time series can have the information of the dynamics of the entire system in deterministic dynamical systems. We validate our approach empirically through identifying the stress situations using k-nearest neighbor algorithm based on the behavioral parameters.