Convention emergence and destabilisation in multi-agent systems

Ensuring coordination amongst individual agents in multi-agent systems (MAS) helps to reduce clashes between them that waste resources and time and facilitates the capability of the agent population to solve mutually beneficial problems. Determining this coordinated behaviour is not always possible a priori due to technical issues such as lack of access to individual agents or computational issues due to the large number of possible clashing actions. Additionally, in systems lacking centralised authorities, dictating rules in a top-down perspective is difficult or impossible. Conventions represent a light-weight, decentralised and emergent solution to this problem. Acting as a socially-accepted rule on expected behaviour they help to focus and constrain agent interactions to facilitate coordination. Understanding how these conventions emerge and how they might be encouraged allows scalable coordination of behaviour within MAS with little computational or logistical overhead. In this thesis we consider how fixed strategy Intervention Agents (IAs) may be used to encourage and direct convention emergence in MAS. We explore their efficacy in doing so in various topologies, both static and time-varying dynamic networks, and propose a number of methods and techniques to increase this efficacy further. We consider how these IAs might be used to destabilise an existing convention, replacing it with a more desirable one and highlight the different methods required to do this. We also explore how various limitations such as time or observability of topological structure can impact the emergence of conventions and provide mechanisms to counteract these issues