Intelligent agent-based systems (IABSs) that could be intelligent agents who solve individually problems or collaborative or cooperative multiagent systems are applied for a very large diversity of real-life difficult problem-solving. The emergent development of this broad research field can be predicted for the upcoming years.

Measuring the machine intelligence quotient (MIQ) using intelligence metrics is very important since it allows the differentiation between IABSs based on their intelligence in difficult problems-solving. The most important property of an intelligence metric must be universality. Developing universal intelligence metrics is difficult based on the very large diversity of intelligent systems. 

The first objective of this community consists of presenting state-of-the-art theoretical and applicative results regarding intelligent system developments that represent significant advances in the field. There are presented diverse methods, and algorithms based on Artificial Intelligence, Machine Learning, Data Science, and Statistics with that can be endowed components of complex IABSs for solving diverse types of problems.   

The second objective consists of presenting state-of-the-art methods that can be used as tools by intelligent systems developers in measuring the intelligence of the systems that they develop.