The Emergence of Norms via Contextual Agreements in Open Societies

Abstract

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.

Norm emergence in multiagent systems: a viewpoint paper

“…Then finally, emergence where a percentage of agents execute this action for every instance of the triggering situation. This cycle is implemented in a large body of literature, from which we select Villatoro et al [101], Mukherjee et al [74], Savarimuthu et al [89] and Vouros [105].…”

“…Vouros [105] models agents that may be considered of medium-cognitive ability, in which an agent can fulfil a combination of different roles, which sometimes have conflicting or incompatible goals. In Vouros's scenario, agents must coordinate to carry out a joint task, or utilise a joint resource, by choosing a mutually convenient time to do so.…”

“…Agents have a specific strategy for scheduling, based on their role, and must decide which strategy to use to schedule the resource. They decide on a strategy that is either more suited to the team leader or to the coordinator role [105].…”

“…Results show that Q-learning evolves fastest to a norm, followed by WoLF-PHC, then Fictitious play. Similarly, Mukherjee et al [75] and Vouros [105] as part of their research study the effects of the same group of algorithms with identical results. However Vouros [105], in addition to the above mentioned three, also utilises Highest Cumulative Reward-based models.…”

“…Similarly, Mukherjee et al [75] and Vouros [105] as part of their research study the effects of the same group of algorithms with identical results. However Vouros [105], in addition to the above mentioned three, also utilises Highest Cumulative Reward-based models. Given that Q-Learning performs the best of the four algorithms, for those researches where the learning algorithm remains constant, it could potentially be the one of choice-see Table 3-as it is for Savarimuthu et al [91] and Beheshti et al [17].…”