Reinforcement Learning for Cooperating and Communicating Reactive Agents in Electrical Power Grids
Balancing Reactivity and Social Deliberation in Multi-Agent Systems, From RoboCup to Real-World Applications (selected papers from the ECAI 2000 Workshop and additional contributions)
Distributed Learning and Control for Manufacturing Systems Scheduling
Proceedings of the 14th International conference on Industrial and engineering applications of artificial intelligence and expert systems: engineering of intelligent systems
Cooperative Multi-Agent Learning: The State of the Art
Autonomous Agents and Multi-Agent Systems
Design and Implementation of Adaptive Agents for Complex Manufacturing Systems
HoloMAS '07 Proceedings of the 3rd international conference on Industrial Applications of Holonic and Multi-Agent Systems: Holonic and Multi-Agent Systems for Manufacturing
Multi-robot task allocation through vacancy chain scheduling
Robotics and Autonomous Systems
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Adaptive job routing and scheduling
Engineering Applications of Artificial Intelligence
Computers and Operations Research
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Multi-machine scheduling, that is, the assigment of jobs to machines such that certain performance demands like cost and time effectiveness are fulfilled, is a ubiquitous and complex activity in everyday life. This paper presents an approach to multi-machine scheduling that follows the multi-agent learning paradigm known from the field of Distributed Artificial Intelligence. According to this approach the machines collectively and as a whole learn and iteratively refine appropriate schedules. The major characteristic of this approach is that learning is distributed over several machines, and that the individual machines carry out their learning activities in a parallel and asynchronous way.