Learning automata: an introduction
Learning automata: an introduction
Hamiltonian cycles and Markov chains
Mathematics of Operations Research
Artificial Life
Stigmergy, self-organization, and sorting in collective robotics
Artificial Life
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
From Natural to Artificial Swarm Intelligence
From Natural to Artificial Swarm Intelligence
Multi-agent coordination and control using stigmergy applied to manufacturing control
Mutli-agents systems and applications
Ant Colony Optimization
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
Networks of Learning Automata: Techniques for Online Stochastic Optimization
Networks of Learning Automata: Techniques for Online Stochastic Optimization
Exploring selfish reinforcement learning in repeated games with stochastic rewards
Autonomous Agents and Multi-Agent Systems
AntNet: distributed stigmergetic control for communications networks
Journal of Artificial Intelligence Research
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Decentralized Learning in Markov Games
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Learning the global maximum with parameterized learning automata
IEEE Transactions on Neural Networks
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The concept of stigmergy provides a simple framework for interaction and coordination in multi-agent systems. However, determining the global system behavior that will arise from local stigmergetic interactions is a complex problem. In this paper we propose to use Game Theory to analyze stigmergetic interactions. We show that a system where agents coordinate by sharing local pheromone information can be approximated by a limiting pheromone game in which different pheromone vectors represent player strategies. This game view allows us to use established methods and solution concepts from game theory to describe the properties of stigmergy based systems. Our goal is to provide a new framework to aid in the understanding and design of pheromone interactions. We demonstrate how we can use this system to determine the long term system behavior of a simple pheromone model, by analyzing the convergence properties of the pheromone update rule in the approximating game. We also apply this model to cases where multiple colonies of agents concurrently optimize different objectives. In this case a limiting colony game can be linked to colony level interactions to characterize the global system behavior.