Multi-Agent Coalition Formation for Long Term Task or Mobile Network
Date Issued
2006
Date
2006
Author(s)
Chen, Chung-Hsien
DOI
en-US
Abstract
Coalition formation is a process to form a group and solve a problem via cooperation. Because the rising of network, each computing device can communicate through network. We can integrate resources of network and use it by coalition formation. In recent years, many researches focus on this topic. New method to decompose task, to gather resources and to form a coalition within various kind of environment are proposed. However, once a task requires a large amount of time to execute, we must form a coalition for a long period of time. Beside, in a high mobility network, forming a coalition and accomplish the task is challenge because the movable feature.
In this thesis, we propose a new model which integrates case-based reasoning, negotiation, and reinforcement learning to improve the coalition formation process. Coalitions in our model suit for executing long term task or for accomplishing a task in high mobility networks. In this model, we search for and reuse the past solutions to apply to the problem we are facing currently. When the solution is found, required resources are gathered through negotiation. Then, the coalition is formed and task is executed. No matter the execution is successful or not, we extract experiences from this coalition formation process by reinforcement learning and reuse it if similar problems appear in the future. In this way, we can form coalitions with long period of lifetime or with stable characteristic. Our experiments also show the advantage of our model in these two different environments.
Subjects
聯盟組成
整合案例式推理
談判商議
強化學習
coalition formation
case-based reasoning
negotiation
reinforcement learning
Type
thesis
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