Chmait, Nader Dowe, David L Green, David G Li, Yuan-Fang Insa-Cabrera, Javier Measuring Universal Intelligence in Agent-Based Systems Using the Anytime Intelligence Test Abstract. This paper aims to quantify and analyze the intelligence of artificial agent collectives. A universal metric is proposed and used to empirically measure intelligence for several different  agent  decision  controllers.  Accordingly,  the  effectiveness  of  various  algorithms  is evaluated on a per-agent basis over a selection of abstracted, canonical tasks of different algorithmic complexities. Results reflect the different settings over which cooperative multiagent systems can be significantly more intelligent per agent than others. We identify and discuss some of the factors in influencing the collective performance of these systems <br> universal intelligence;anytime intelligence test;agent;Adaptive Agents and Intelligent Robotics;Artificial Intelligence and Image Processing 2016-04-14
    https://bridges.monash.edu/articles/report/Measuring_Universal_Intelligence_in_Agent_Based_Systems_Using_the_Anytime_Intelligence_Test/3175585
10.4225/03/570EE6833FC2E