Social or Individual Learning? An Aggregated Solution for Coordination in Multiagent Systems

Abstract

There are mainly two different ways of learning for animals and humans: trying on yourself through interactions or imitating/copying others through communication/observation. How these two learning strategies differ and what roles they are playing in achieving coordination among individuals are two challenging problems for researchers from various disciplines. In multiagent systems, most existing work simply focuses on individual learning for achieving coordination among agents. The social learning perspective has been largely neglected. Against this background, this article contributes by proposing an integrated solution to decision making between social learning and individual learning in multiagent systems. Two integration modes have been proposed that enable agents to choose in between these two learning strategies, either in a fixed or in an adaptive manner. Experimental evaluations have shown that these two kinds of leaning strategies have different roles in maintaining efficient coordination among agents. These differences can reveal some significant insights into the manipulation and control of agent behaviors in multiagent systems, and also shed light on understanding the social factors in shaping coordinated behaviors in humans and animals.