What Could Models of Superorganismal Cognition Offer to Embodied AI?

Abstract

Abstract Superorganisms such as ant or honeybee colonies exhibit extraordinary collective intelligence, such as an ability to identify and choose the best available nest site in an uncertain world. This collective cognition is inextricably reliant on the embodiment of individual agents, specifically their movement through space. We have recently developed models of superorganismal cognition based on a compelling analogy with techniques in Bayesian statistics, which are likewise aimed at grappling with the uncertainty and incompleteness of real data sources. These models foreground some potential lessons for the design of embodied artificial intelligences, such as robot swarms. For example, the spatial distribution of independently judging agents can convey valuable information, relaxing expectations that regular inter-agent (‘inter-neuronal’) communication is necessary for cognition, which points to the potential of minimal field swarm robotics. Meanwhile, the importance of individual heterogeneity to effective and resilient collective cognition in biology suggests great potential in this area for engineering.