Nectar of the Bots: Evolving Bidirectional Referential Communication

Referential communication is central to social and collective behaviour, for example honey bees communicating nectar locations to each other or co-workers gossiping about a colleague. Since such behaviour typically is considered to be ‘representation hungry’, it is often assumed to require the possession of complex cognitive machinery capable of manipulating symbolic representations of the world. However, a series of simulation studies have shown that it can be achieved by very simple embodied artificial agents controlled by evolved recurrent artificial neural networks that are challenging to interpret in symbol-processing terms. In this paper, we extend this paradigm to explore scenarios in which a pair of agents, each of which is privy to a different piece of private information, must jointly solve a task that requires both pieces of information to be communicated, compared and acted upon, i.e., each agent must simultaneously play the role of both signaller and receiver during an unstructured referential communication interaction that is bidirectional. We demonstrate evolved agents that are able to solve this task, and analyse the extent to which their situated, embedded and embodied communicative behaviour can be considered to be a step towards understanding the minimal cognitive basis for human language.