Measurements of collective machine intelligence

Intelligence is a fairly intuitive concept of our everyday life. As usually acknowledged in psychometrics, “intelligence is the ability measured by intelligence tests”. However, defining what exactly intelligence tests should measure is less obvious. During the last decade, computer scientists have attempted to provide a formal definition of intelligence. There seems now to be the tendency that intelligence should make reference to the formalism provided by the field of algorithmic information theory. Yet, a consensus is far from being reached. Independent from the still ongoing research in measuring individual intelligence, we anticipate and provide a framework for measuring collective intelligence. Collective intelligence refers to the idea that several individuals can collaborate in order to achieve high levels of intelligence. We present thus some ideas on how the intelligence of a group can be measured and simulate such tests. We will however focus here on groups of artificial intelligence agents (i.e., machines). We will explore how a group of agents is able to choose the appropriate problem and to specialize for a variety of tasks. This is a feature which is an important contributor to the increase of intelligence in a group (apart from the addition of more agents and the improvement due to common decision making). Our results reveal some interesting results about how (collective) intelligence can be modeled, about how collective intelligence tests can be designed and about the underlying dynamics of collective intelligence. As it will be useful for our simulations, we provide also some improvements of the threshold allocation model originally used in the area of swarm intelligence but further generalized here.