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Dynamic Model for Social Coalition Formation Based on Expertise, Temporal Reputation and Time Commitment

机译:基于专长,时间信誉和时间承诺的社会联盟形成动态模型

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Existing approaches to coalition formation are generally gross simplifications of real problems of resource allocation where experience, reputation, and time optimization should be considered, although they are not usually studied together. To overcome this issue, this study proposes a dynamic and distributed social coalition formation model, that reproduces real-world environments where interactions are ruled by an underlying network that adapts itself based on the best updated reputation of local neighbors, in order to bring together individuals better suited for efficient cooperation. In this environment, agents possessing different levels of expertise must be organized to provide the most advantageous partnerships for the purpose of solving tasks, and an execution order of task's subtasks is defined to favor the use and release of agents' resources. To achieve this objective, we based our proposal on a coalitional skill game (CSG) approach, which organizes the use of resources by time commitment, and calculates and exploits the temporal reputation of heterogeneous agents to improve the utility of coalitions. Our experiments with different initial social networks allowed us to evaluate the effectiveness of this proposal and provided elements to exploit the advantages of an optimized social structure in a connected world.
机译:现有的联盟形成方法通常是对资源分配的实际问题进行粗略的简化,其中应考虑经验,声誉和时间优化,尽管通常不会一起研究它们。为了克服这个问题,本研究提出了一个动态的,分布式的社会联盟形成模型,该模型重现了现实环境,其中交互作用是由底层网络统治的,该底层网络根据本地邻居的最佳更新信誉来适应自身,以使个人聚在一起更适合高效合作。在这种环境下,必须组织具有不同专业知识水平的代理以提供最有利的伙伴关系,以解决任务,并且定义任务子任务的执行顺序以利于代理资源的使用和释放。为了实现此目标,我们的建议基于联盟技能博弈(CSG)方法,该方法通过时间投入来组织资源的使用,并计算和利用异构代理的时间声誉来提高联盟的效用。我们在不同的初始社交网络上进行的实验使我们能够评估该建议的有效性,并提供了在互联世界中利用优化的社会结构优势的要素。

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