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
Ant-like missionaries and cannibals: synthetic pheromones for distributed motion control
AGENTS '00 Proceedings of the fourth international conference on Autonomous agents
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Neuro-Dynamic Programming
Organisations in the Particular Class of Multi-agent Systems
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
Model of Cooperation in Multi-agent Systems with Fuzzy Coalitions
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
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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
Cooperative Multi-Agent Learning: The State of the Art
Autonomous Agents and Multi-Agent Systems
Collaborative foraging using beacons
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 3 - Volume 3
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In this paper we propose an algorithm for multi-agent Q-learning. The algorithm is inspired by the natural behaviour of ants, which deposit pheromone in the environment to communicate. The benefit besides simulating ant behaviour in a colony is to design complex multi-agent systems. Complex behaviour can emerge from relatively simple interacting agents. The proposed Q-learning update equation includes a belief factor. The belief factor reflects the confidence the agent has in the pheromone detected in its environment. Agents communicate implicitly to co-ordinate and co-operate in learning to solve a problem.