Turtles, termites, and traffic jams: explorations in massively parallel microworlds
Turtles, termites, and traffic jams: explorations in massively parallel microworlds
Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
Whistling in the dark: cooperative trail following in uncertain localization space
AGENTS '00 Proceedings of the fourth international conference on Autonomous agents
Digital pheromone mechanisms for coordination of unmanned vehicles
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
Autonomous Robots
Phe-Q: A Pheromone Based Q-Learning
AI '01 Proceedings of the 14th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
An Improved Q-Learning Algorithm Using Synthetic Pheromones
CEEMAS '01 Revised Papers from the Second International Workshop of Central and Eastern Europe on Multi-Agent Systems: From Theory to Practice in Multi-Agent Systems
A Pheromone-Based Utility Model for Collaborative Foraging
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
Distributed Path Planning for Robots in Dynamic Environments Using a Pervasive Embedded Network
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
MASON: A Multiagent Simulation Environment
Simulation
A probabilistic movement model for shortest path formation in virtual ant-like agents
Proceedings of the 2007 annual research conference of the South African institute of computer scientists and information technologists on IT research in developing countries
Digital pheromones for coordination of unmanned vehicles
E4MAS'04 Proceedings of the First international conference on Environments for Multi-Agent Systems
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A classic example of multiagent coordination in a shared environment involves the use of pheromone deposits as a communication mechanism. Due to physical limitations in deploying actual pheromones, we propose a sparse representation of the pheromones using movable beacons. There is no communication between the beacons to propagate pheromones; instead, robots make movement and update decisions based entirely on local pheromone values. Robots deploy the beacons throughout the environment, and subsequently move them and update them using a variation of value iteration. Simulation results show that our approach is effective at finding good trails, locally improving them, and adapting to dynamic changes in the environments.