Resource allocation games with changing resource capacities
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Towards Understanding the Role of Learning Models in the Dynamics of the Minority Game
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
An Agent-based Resource Allocation Model for computational grids
Multiagent and Grid Systems
Decentralized, adaptive resource allocation for sensor networks
NSDI'05 Proceedings of the 2nd conference on Symposium on Networked Systems Design & Implementation - Volume 2
An Adaptive Strategy for Resource Allocation Modeled as Minority Game
SASO '07 Proceedings of the First International Conference on Self-Adaptive and Self-Organizing Systems
An adaptive strategy for minority games
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Grand canonical minority games with variable strategy spaces
JSAI'05 Proceedings of the 2005 international conference on New Frontiers in Artificial Intelligence
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In the traditional minority game, each agent chooses the highest-score strategy at every time step from its initial strategies which are allocated randomly. How can one agent manage to outperform its competitors and maximise its own utility in this competing and dynamic environment? In this paper, we study a version of the minority game in which one privileged agent is allowed to join the game with larger memory size and free to choose any strategy, while the other agents own small number of strategies. Simulations show that the privileged agent using the intelligent strategy outperforms the other agents in the same model and other models proposed in previous work in terms of individual payoff. We also investigate how the number of strategies and the length of memory affect the privileged agent's performance.