Diversity is pervasive in human nature and culture, and is deeply rooted in the variation of natural traits and experience among individuals, the collectives they form, and the environments they inhabit. When humans reason individually, they maintain different representations, conceptualisations, and theories, and apply different rules of inference, learning, and decision making. When they interact with each other to combine their skills or resources, to coordinate their activities, and to resolve conflicts between their individual objectives, they exchange information and knowledge, negotiate and align their individual views, and adapt to each other’s behaviour dynamically. Arguably, diversity is not only a phenomenon that humans have to deal with, but it is also the vehicle for achieving some of the most impressive products of human intelligence.

Artificial Intelligence, on the other hand, has so far largely relied on a certain degree of homogeneity, not necessarily in terms of the components involved in a method or system, but in terms of the process that combines them. While various areas within AI have already developed methods that can cope with and/or exploit diversity to some extent, for example

• electronic markets where individual agents have different goals and aim to maximise their own profit,

• hybrid robot architectures that involve different layers of representation and reasoning,

• knowledge sharing infrastructures where different agents use different domain ontologies, and

• machine learning systems that combine different sources of data and/or learning units,

more often than not, these systems still involve a “monolithic”, global approach to integration. This usually derives from a global task context, a common intermediate representation layer, or a global output to be produced by the integrated system.
We believe that there is a huge potential in bringing the insights from work on problems that involve diversity – like those listed in the examples above – together to gain a deeper understanding of the phenomenon of diversity, as well as to develop principled methodological approaches that will enable us to better utilise diversity in future AI systems.