Abstract (EN)

Societal norms or conventions help identify one of many appropriate behaviors during an interaction between agents. The offline study of norms is an active research area where one can reason about normative systems and include research on designing and enforcing appropriate norms at specification time. In our work, we consider the problem of the emergence of conventions in a society through distributed adaptation by agents from their online experiences at run time. The agents are connected to each other within a fixed network topology and interact over time only with their neighbours in the network. Agents recognize a social situation involving two agents that must choose one available action from multiple ones. No default behavior is specified. We study the emergence of system-wide conventions via the process of social learning where an agent learns to choose one of several available behaviors by interacting repeatedly with randomly chosen neighbors without considering the identity of the interacting agent in any particular interaction. While multiagent learning literature has primarily focused on developing learning mechanisms that produce desired behavior when two agents repeatedly interact with each other, relatively little work exists in understanding and characterizing the dynamics and emergence of conventions through social learning. We experimentally show that social learning always produces conventions for random, fully connected and ring networks and study the effect of population size, number of behavior options, different learning algorithms for behavior adoption, and influence of fixed agents on the speed of convention emergence. We also observe and explain the formation of stable, distinct subconventions and hence the lack of emergence of a global convention when agents are connected in a scale-free network.