A Novel Robust Metric for Comparing the Intelligence of Two Cooperative Multiagent Systems

Cooperative multiagent systems are used for solving many computational hard problems. In the scientific literature, the intelligence of cooperative multiagent systems is considered at the systems' level and is based on the “intelligent problem solving” consideration (highly efficient and flexible problems solving; difficult problem solving, with missing or erroneous data; efficient solving of NP - hard problems). In this paper, we propose a novel accurate metric called MetrIntComp (Metric for Cooperative Multiagent Systems Intelligence Comparison) for a robust comparison of two cooperative multiagent system's intelligence, effective even in the case of small differences in intelligence between the considered systems. For proving the effectiveness of the metric we considered an illustrative case study for two cooperative multiagent systems composed of simple agents, in that the intelligence emerge at the systems' level, each of them specialized on solving the same type of computational difficult, NP-hard problem. The conclusion of the case study was that the metric is able to make a differentiation between the two multiagent systems even the numerical difference between the measured intelligence is small. Based on this fact, the two multiagent systems could not be considered that belong to the same class of intelligence.