Flocks, herds and schools: A distributed behavioral model
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
A Formal Model for Situated Multi-Agent Systems
Fundamenta Informaticae - Multiagent Systems (FAMAS'03)
Agent coordination by trade-off between locally diffusion effects and socially structural influences
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Convergence at prominent agents: a non-flat synchronization model of situated multi-agents
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3
A model for collective strategy diffusion in agent social law evolution
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Antisocial Behavior of Agents in Scheduling Mechanisms
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
An efficient algorithm to find k-nearest neighbors in flocking behavior
Information Processing Letters
Expert Systems with Applications: An International Journal
A simple heuristic to find efficiently k-nearest neighbors in flocking behaviors
AMERICAN-MATH'12/CEA'12 Proceedings of the 6th WSEAS international conference on Computer Engineering and Applications, and Proceedings of the 2012 American conference on Applied Mathematics
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In previous work on collective synchronization of multi-agents, they always follow the assumptions that the synchronization is flat where all agents have the same synchronization capacity and the final synchronization result always converges on a common average strategy. However, in many circumstances the above assumption does not match the peculiarities of real multi-agent societies where each agent plays a different role in the synchronization. To make up the restrictions of related work, this paper presents a non-flat synchronization model where the synchronization capacity of each agent is different regarding its social rank and strategy dominance. In the presented model, all agents are situated in a synchronization field where each agent can sense the collective synchronization forces from other agents; if some agents are more prominent than other ordinary agents (e.g., they have the dominance of social ranks or behavior strategies), they will have strong synchronization capacities in the field; and finally the collective synchronization results may incline to converge at such prominent agents' strategies, which is called prominence convergence in collective synchronization and can be proved by our theoretical analyses and experimental results.