An integrated token-based algorithm for scalable coordination
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
IEEE/ACM Transactions on Networking (TON) - Special issue on networking and information theory
A mathematical analysis of collective cognitive convergence
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An investigation of the vulnerabilities of scale invariant dynamics in large teams
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Agents, pheromones, and mean-field models
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Multi-variate Distributed Data Fusion with Expensive Sensor Data
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
Between agents and mean fields
MABS'11 Proceedings of the 12th international conference on Multi-Agent-Based Simulation
Efficient opinion sharing in large decentralised teams
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Dynamic facts in large team information sharing
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Active sensing in complex multiagent environments
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
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Large heterogeneous teams will often be in situations where sensor data that is uncertain and conflicting is shared across a peer-to-peer network. Not every team member will have direct access to sensors and team members will be influenced mostly by teammates with whom they communicate directly. In this paper, we investigate the dynamics and emergent behaviors of a large team sharing beliefs to reach conclusions about the world. We find empirically that the dynamics of information propagation in such belief sharing systems are characterized by information avalanches of belief changes caused by a single additional sensor reading. The distribution of the size of these avalanches dictates the speed and accuracy with which the team reaches conclusions. A key property of the system is that it exhibits qualitatively different dynamics and system performance over small changes in system parameter ranges. In one particular range, the system exhibits behavior known as scale-invariant dynamics which we empirically find to correspond to dramatically more accurate conclusions being reached by team members. Due to the fact that the ranges are very sensitive to configuration details, the parameter ranges over which specific system dynamics occur are extremely difficult to predict precisely. In this paper we (a) develop techniques to mathematically characterize the dynamics of the team belief propagation (b) obtain through simulations the relation between the dynamics and overall system performance, and (c) develop a novel distributed algorithms that the agents in the team use locally to steer the whole team to areas of optimized performance.