Flocks, herds and schools: A distributed behavioral model
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
Communications of the ACM - Robots: intelligence, versatility, adaptivity
Evolving Self-Organizing Behaviors for a Swarm-Bot
Autonomous Robots
Decentralized synchronization protocols with nearest neighbor communication
SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
Sensing-based shape formation on modular multi-robot systems: a theoretical study
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
Self-adapting modular robotics: a generalized distributed consensus framework
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Cellular automata models for cooperation in multirobot systems
IMMURO'12 Proceedings of the 11th WSEAS international conference on Instrumentation, Measurement, Circuits and Systems, and Proceedings of the 12th WSEAS international conference on Robotics, Control and Manufacturing Technology, and Proceedings of the 12th WSEAS international conference on Multimedia Systems & Signal Processing
Manipulating convention emergence using influencer agents
Autonomous Agents and Multi-Agent Systems
Anubis: Artificial neuromodulation using a bayesian inference system
Neural Computation
Learning influence in complex social networks
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Decentralized control for dynamically reconfigurable FPGA systems
Microprocessors & Microsystems
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Coordination within decentralized agent groups frequently requires reaching global consensus, but typical hierarchical approaches to reaching such decisions can be complex, slow, and not fault-tolerant. By contrast, recent studies have shown that in decentralized animal groups, a few individuals without privileged roles can guide the entire group to collective consensus on matters like travel direction. Inspired by these findings, we propose an implicit leadership algorithm for distributed multi-agent systems, which we prove reliably allows all agents to agree on a decision that can be determined by one or a few better-informed agents, through purely local sensing and interaction. The approach generalizes work on distributed consensus to cases where agents have different confidence levels in their preferred states. We present cases where informed agents share a common goal or have conflicting goals, and show how the number of informed agents and their confidence levels affects the consensus process. We further present an extension that allows for fast decision-making in a rapidly changing environment. Finally, we show how the framework can be applied to a diverse variety of applications, including mobile robot exploration, sensor network clock synchronization, and shape formation in modular robots.