Social Reasoning in Multi-Agent Systems with the Expectation-Strategy-Behaviour Framework

Abstract

Multi-agent Systems (MAS) provide an increasingly relevant field of research due to their many applications to modelling real world situations where the behaviour of many individual, self-motivated, agents must be reasoned about and controlled. The problem of agent social reasoning is central to MAS, where an agent reasons about its actions and interactions with other agents. This is the most important component of MAS, as it is the interactions, cooperation and competition between agents that make MAS a powerful approach suited for tackling many complex problems. Existing work focuses either on specific types of social reasoning or general purpose agent practical reasoning - reasoning directed toward actions. This thesis argues that social reasoning should be considered separately from practical reasoning. There are many possible benefits to this separation compared to existing approaches. Principally, it can allow general algorithms for agent implementation, analysis and bounded reasoning. This viewpoint is motivated by the desire to implement social reasoning agents and allow for a more general theory of social reasoning in agents. This thesis presents the novel Expectation- Strategy-Behaviour (ESB) framework for social reasoning, which provides a generic way to specify and execute agent reasoning approaches. ESB is a powerful tool, allowing an agent designer to write expressive social reasoning specifications and have a computational model generated automatically. Through a formalism and description of an implemented reasoner based on this theory it is shown that it is possible and beneficial to implement a social reasoning engine as a complementary component to practical reasoning. By using ESB to specify, and then implement, existing social reasoning schemes for joint commitment and normative reasoning, the framework is shown to be a suitable general reasoner. Examples are provided of how reasoning can be bounded in an ESB agent and the mechanism to allow analysis of agent designs is discussed. Finally, there is discussion on the merits of the ESB solution and possible future work.