Artificial Intelligence
Challenger: a multi-agent system for distributed resource allocation
AGENTS '97 Proceedings of the first international conference on Autonomous agents
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
A multiagent architecture for controlling the Palamede satellite
Web Intelligence and Agent Systems
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The paper presents a multi-agent system that learns to manage the re-sources of an unmanned spacecraft. Each agent controls a sub-system and learns to optimise its resources. The agents can coordinate their actions to satisfy user requests. Co-ordination is achieved by exchanging sched-uling information between agents. Resource management is implemented using two reinforcement learning techniques: the Monte-carlo and the Q-learning. The paper demonstrates how the approach can be used to model the imaging system of a spacecraft. The environment is represented by agents which control the spacecraft sub-systems involved in the imaging activity. The agent in charge of the resource management senses the information regarding the resource requested, the resource conflicts and the resource availability. Scheduling of resources is learnt when all subsystems are fully functional and when resources are reduced by random failures.