Abstract
BackgroundThe cooperation of cells in biological systems is similar to that of agents in cooperative multi-agent systems. Research findings in multi-agent systems literature can provide valuable inspirations to biological research. The well-coordinated states in cell systems can be viewed as desirable social norms in cooperative multi-agent systems. One important research question is how a norm can rapidly emerge with limited communication resources.ResultsIn this work, we propose a learning approach which can trade off the agents’ performance of coordinating on a consistent norm and the communication cost involved. During the learning process, the agents can dynamically adjust their coordination set according to their own observations and pick out the most crucial agents to coordinate with. In this way, our method significantly reduces the coordination dependence among agents.ConclusionThe experiment results show that our method can efficiently facilitate the social norm emergence among agents, and also scale well to large-scale populations.
Highlights
The cooperation of cells in biological systems is similar to that of agents in cooperative multi-agent systems
Considering the fact that the communications between the cells are limited in biological systems, we develop a learning approach based on the individually learning methods and the DCOP algorithm under limited communication bandwidth to facilitate the norm emergence in agent societies
Results and discussion we evaluate the performance of our algorithm on a large single-state problem
Summary
The cooperation of cells in biological systems is similar to that of agents in cooperative multi-agent systems. The well-coordinated states in cell systems can be viewed as desirable social norms in cooperative multi-agent systems. If location 1, location 2 and location 3 always have targets with resulting reward +30, +50, +40 respectively, In the research of the cooperative MAS, social norms play an important role in regulating agents’ behaviors to ensure coordination among the agents. In our life, we should drive on the left (or right) according to the traffic rules. When it comes to biological systems, this corresponds to coordinating on the well-coordinated states for better survival. Different cells are designed for different functions and cells should coordinate their functions to ensure that the overall biological system functions correctly
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