Simulation of Nest Assessment Behavior by Ant Scouts
ANTS '02 Proceedings of the Third International Workshop on Ant Algorithms
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Using Artificial Physics to Control Agents
ICIIS '99 Proceedings of the 1999 International Conference on Information Intelligence and Systems
Distributed, Physics-Based Control of Swarms of Vehicles
Autonomous Robots
A Fluid Dynamics Approach to Multi-Robot Chemical Plume Tracing
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Numerical Mathematics and Computing
Numerical Mathematics and Computing
Ant based mechanism for crisis response coordination
ANTS'06 Proceedings of the 5th international conference on Ant Colony Optimization and Swarm Intelligence
An overview of physicomimetics
SAB'04 Proceedings of the 2004 international conference on Swarm Robotics
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This paper presents a novel theoretical framework for swarms of agents. Before deploying a swarm for a task, it is advantageous to predict whether a desired percentage of the swarm will succeed. The authors present a framework that uses a small group of expendable "scout" agents to predict the success probability of the entire swarm, thereby preventing many agent losses. The scouts apply one of two formulas to predict-the standard Bernoulli trials formula or the new Bayesian formula. For experimental evaluation, the framework is applied to simulated agents navigating around obstacles to reach a goal location. Extensive experimental results compare the mean-squared error of the predictions of both formulas with ground truth, under varying circumstances. Results indicate the accuracy and robustness of the Bayesian approach. The framework also yields an intriguing result, namely, that both formulas usually predict better in the presence of Lennard-Jones inter-agent forces than when their independence assumptions hold.