Engineering Social Learning Mechanisms for Minimalistic Multi-agent Robots

Social learning, which can include complex or simple social mechanisms, allow us to understand cooperation and communication in animals, giving them better chances to survive for longer and thrive as a society. In order to translate this understanding into socially rich behavior among multi-agent robots, this study utilizes social learning mechanisms that are simple yet quite effective. Small and simple swarm robots are utilized to understand how such social mechanisms might play a role in establishing rules for emergent group behavior and how social rules might be engineered to gain useful effects in a group of robots. The study investigates exploratory behavior without interaction (asocial) and with interaction (social) among a group of robots. The results from this exploratory study suggest that deterministic asocial exploration is best performed by Spiral exploration mechanisms. However, these asocial exploration strategies are eclipsed by certain types of social reward sharing strategies as long as sharing occurs for at least half the lifetime of the robots. Sharing locations of reward caches for all time is of course the most optimal, but comes at the cost of communicating longer and hence using more energy both on the sender and receiver's end. An analysis of a compromise strategy between completely asocial exploration and social reward location sharing is performed using strategies termed critical and conditional learning. It is found that the number of reward caches located through critical and conditional learning are intermediary to the two extremes, namely completely asocial and completely social foraging.