10.4225/03/570EE6833FC2E Nader Chmait Nader Chmait David L Dowe David L Dowe David G Green David G Green Yuan-Fang Li Yuan-Fang Li Javier Insa-Cabrera Javier Insa-Cabrera Measuring Universal Intelligence in Agent-Based Systems Using the Anytime Intelligence Test Monash University 2016 universal intelligence anytime intelligence test agent Adaptive Agents and Intelligent Robotics Artificial Intelligence and Image Processing 2016-04-14 00:38:25 Report https://bridges.monash.edu/articles/report/Measuring_Universal_Intelligence_in_Agent_Based_Systems_Using_the_Anytime_Intelligence_Test/3175585 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>