Multiagent systems and societies of agents
Multiagent systems
Time, clocks, and the ordering of events in a distributed system
Communications of the ACM
Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence
Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence
Building Secure and Reliable Network Applications
Building Secure and Reliable Network Applications
MAAMAW '92 Selected papers from the 4th European Workshop on on Modelling Autonomous Agents in a Multi-Agent World, Artificial Social Systems
Partial-order planning with concurrent interacting actions
Journal of Artificial Intelligence Research
Protocol Based Communication for Situated Multi-Agent Systems
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
A Formal Model for Situated Multi-Agent Systems
Fundamenta Informaticae - Multiagent Systems (FAMAS'03)
A Framework for Situated Multiagent Systems
Software Engineering for Multi-Agent Systems V
A reference architecture for situated multiagent systems
E4MAS'06 Proceedings of the 3rd international conference on Environments for multi-agent systems III
Environments for multiagent systems state-of-the-art and research challenges
E4MAS'04 Proceedings of the First international conference on Environments for Multi-Agent Systems
ESAW'05 Proceedings of the 6th international conference on Engineering Societies in the Agents World
A Formal Model for Situated Multi-Agent Systems
Fundamenta Informaticae - Multiagent Systems (FAMAS'03)
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Agents of a multi-agent system (MAS) must synchronize whenever they want to perform simultaneous actions. In situated MASs, typically, the control over such synchronization is centralized, i.e. one synchronizer has the supervision on all agents of the MAS. As a consequence, all agents are forced to act at a global pace and that does not fit with autonomy of agents. Besides, global synchronization implies centralized control, in general an undesirable property of MASs. In this paper we present an algorithm that allows agents to synchronize with other agents within their perceptual range. The result of the algorithm is the formation of independent groups of synchronized agents. The composition of these groups depends on the locality of the agents and dynamically changes when agents enter or leave each others perceptual range. Since in this approach agents are only synchronized with colleagues in their region, the pace on which they act only depends on the acting speed of potential collaborating agents. The price for decentralization of synchronization is the communication overhead to set up the groups. In the paper, we discuss experimental results and compare the benefits of regional synchronization with its costs.