Multiagent Coalition Formation for Distributed, Adaptive Resource Allocation

We present a distributed, adaptive resource allocat i n approach for multiagent systems called ARAMS. ARAMS allows a collection of agents to adaptively allocat e CPU resource among themselves to handle dynamic events encountered in a noisy and uncertain environment in real-time manner. Each event encountered may incur a CPU shortage crisis in an agent. ARAMS is aimed to reduce the occurrence and amount of shortage crises o f each agent as well as the entire system as a whole. Th underlying problem-solving strategy of ARAMS is the integration of a monitor-reactive cycle and a goaldirected coalition formation model. The monitor-rea ctive cycle requires the agent to monitor the crisis and ttempt to fix it on its own. The goal-directed coalition f ormation allows the agent to ask for help from other agents ra ionally once it has the resources to do so. Agents als o learn how to form better coalitions faster from their pas t experience. We conducted a series of experiments and the experimental results show that our approach to CPU resource allocation is able to learn and adapt cohere ntly, reacting to and planning for CPU shortages.

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