Participant Selection for Short-term Collaboration in Open Multi-agent systems

In multi-agent systems, agents coordinate their behaviour and work together to achieve a shared goal through collaboration. However, in open multi-agent systems, selecting qualified participants to form effective collaboration communities is challenging. In such systems, agents do not have access to complete domain knowledge, they leave and join the systems unpredictably. More importantly, agents are mostly self-interested and have multiple goals and policies that may be even conflicting with others, which makes the participant selection even more challenging.Many of the current approaches are not applicable in constantly evolving open systems, where their performance will be affected by any unpredictable behaviour, agents’ lack of complete domain knowledge and the impossibility of having a central coordinator agent. In open systems, agents require a mechanism that enables them to dynamically change their perception of the environment and observe their neighbouring agents, so that they can identify qualified collaboration participants that have no conflicting goals and to balance their level of cooperation and self-interest.In this paper, we propose OPSCO, as a solution for On-demand Participant Selection for Short-term Collaboration in Open multi-agent systems. Unlike the existing research, we do not assume any predefined setting for agents’ structure in the system and do not have access to complete domain knowledge and allow each agent to build a dynamic dependency model and maintain when there is a change in the system. The model captures the agent’s most recent dependency structure of goals and policies with its neighbouring agents. It enables them to identify and select a qualified non-conflicting set of participants.OPSCO is evaluated in a real world open system smart grid and constrained resource sharing case studies. OPSCO outperforms other methods by selecting a qualified non-conflicting set of agents to collaborate. OPSCO balances the self-interest and level of cooperation and decreases failure in the overall agents’ goals (individual/shared).