Identifying opportunities and constraints for goal achievement through relationship analysis

The ability of agents to interact in order to collectively achieve goals is one of the central arguments for the utility of multi-agent systems. Such interactions take place whenever one agent performs an action which, intentionally or not, affects one or more other agents. Thus, when agents interact we can say that they are related by virtue of the fact that they are affecting each other. Identifying, analysing and understanding the implications of the various types of such relationships is of critical importance, since they can have both beneficial and adverse effects on the performance of the overall system and the individuals within it. Therefore, if coordination and regulation of agents is to be achieved as an agent society evolves, either by external intervention or through interventions by the agents themselves, we require some means of identifying the relationships between agents at run-time. Of course, this information is only useful if we are also able to determine how the identified relationships may impact on individual agent operation and the system as a whole. Thus, we also require a principled and comprehensive typology for characterising agent relationships. In this paper we outline just such a model.