This paper explores the emergence of norms in agents' societies when agents play multiple -even incompatible- roles in their social contexts simultaneously, and have limited interaction ranges. Specifically, this article proposes two reinforcement learning methods for agents to compute agreements on strategies for using common resources to perform joint tasks. The computation of norms by considering agents' playing multiple roles in their social contexts has not been studied before. To make the problem even more realistic for open societies, we do not assume that agents share knowledge on their common resources. So, they have to compute semantic agreements towards performing their joint actions. The paper reports on an empirical study of whether and how efficiently societies of agents converge to norms, exploring the proposed social learning processes w.r.t. different society sizes, and the ways agents are connected. The results reported are very encouraging, regarding the speed of the learning process as well as the convergence rate, even in quite complex settings.
Effective norms, emerging from sustained individual interactions over time, can complement societ al rules and significantly enhance performance of individual agents and agent societies. Researchers have used a model that supports the emergence of social norms via learning from interaction experiences where each interaction is viewed as a stage game. In this social learning model, which is distinct from an agent learning from repeated interactions against the same player, an agent learns a policy to play the game from repeated interactions with multiple learning agents. The key research question is to characterize when and how the entire population of homogeneous learners converge to a consistent norm when multiple action combinations yield the same optimal payoff. In this paper we study two extensions to the social learning model that significantly enhances its applicability. We first explore the effects of heterogeneous populations where different agents may be using different learning algorithms. We also investigate norm emergence when agent interactions are physically constrained. We consider agents located on a grid where an agent is more likely to interact with other agents situated closer to it than those that are situated afar. The key new results include the surprising acceleration in learning with limited interaction ranges. We also study the effects of pure-strategy players, i.e., nonlearners in the environment. Social norms play a pivotal role in sustaining social order by regulating individual behaviors in a society. In normative multiagent systems, social norms have been used as an efficient mechanism to govern virtual agent societies towards cooperation and coordination. In this paper, we study the emergence of social norms via learning from repeated local interactions in networked agent societies. We propose a collective learning framework, which imitates the opinion aggregation process in human decision making, to study the impact of agent local collective behaviors on norm emergence in different situations. In the framework, each agent interacts repeatedly with all of its neighbors. At each step, an agent first takes a best-response action towards each of its neighbors and then combines all of these actions into a final action using ensemble learning methods. We conduct extensive experiments to evaluate the framework with respect to different network topologies, learning strategies, numbers of actions, and so on. Experimental results reveal some significant insights into norm emergence in networked agent societies achieved through local collective behaviors. Behavioral norms are key ingredients that allow agent coordination where societ al laws do not sufficiently constrain agent behaviors. Whereas social laws need to be enforced in a top-down manner, norms evolve in a bottom-up manner and are typically more self-enforcing. While effective norms can significantly enhance performance of individual agents and agent societies, there has been little work in multiagent systems on the formation of social norms. We propose a model that supports the emergence of social norms via learning from interaction experiences. In our model, individual agents repeatedly interact with other agents in the society over instances of a given scenario. Each interaction is framed as a stage game. An agent learns its policy to play the game over repeated interactions with multiple agents. We term this mode of learning social learning, which is distinct from an agent learning from repeated interactions against the same player. We are particularly interested in situations where multiple action combinations yield the same optimal payoff. The key research question is to find out if the entire population learns to converge to a consistent norm. In addition to studying such emergence of social norms among homogeneous learners via social learning, we study the effects of heterogeneous learners, population size, multiple social groups, etc.