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We propose an efficient team formation method for multi-agent systems consisting of self-interested agents in task-oriented domains where agents have no prior knowledge of the resources/abilities of the other agents. Internet services based on services computing and cloud computing, which have been rapidly increasing, are usually achieved by combining a number of service elements that are distributed...
We propose a team formation method that integrates the estimating of the resources of neighboring agents in a hierarchically structured agent network in order to allocate tasks to the agents that have sufficient capabilities for doing tasks. A task for providing the required service in a distributed environment is often achieved by a number of subtasks that are dynamically constructed on demand in...
We propose the team formation method for task allocations in agent networks by reinforcement learning based on communication delay and by reorganization of agent networks. A task in a distributed environment like an Internet application, such as grid computing and service-oriented computing, is usually achieved by doing a number of subtasks. These subtasks are constructed on demand in a bottom-up...
We propose a method of distributed team formation that uses reinforcement learning and dynamic reorganization by taking into account communication delay in multi-agent systems (MAS). A task in a distributed environment is usually achieved by doing a number of subtasks that require different functions and resources. These subtasks have to be processed cooperatively in the appropriate team of agents...
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