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A Multi-Agent Coalition Formation Method Based on Preference Models

机译:基于偏好模型的多Agent联盟形成方法

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Coordination is one of the fundamental research issues in distributed artificial intelligence and multi-agent systems. Current multi-agent coalition formation methods present two major problems. First, some of these methods can be applied only to cooperative multi-agent systems; second, the algorithms proposed may fail in the formation of the coalitions. This article proposes two methods for problems of agent coalition formation in cooperative and non-cooperative multi-agent systems. These methods are based on agent preference models and on preference aggregation using the Choquet integral. Our first method based on the evolutionary system design (ESD) methodology proposed by M. Shakun is required for competitive multi-agent systems, whereas our second method is powerful for cooperative multi-agent systems where the agents can exchange their information.
机译:协调是分布式人工智能和多智能体系统中的基础研究问题之一。当前的多主体联盟形成方法存在两个主要问题。首先,其中一些方法只能应用于协作式多智能体系统;其次,提出的算法可能无法形成联盟。本文提出了两种解决合作和非合作多智能体系统中智能体联盟形成问题的方法。这些方法基于代理偏好模型和基于Choquet积分的偏好聚合。我们的第一种方法基于M. Shakun提出的进化系统设计(ESD)方法,是竞争性多代理系统所必需的,而我们的第二种方法对于协作多代理系统(其中代理可以交换信息)具有强大的作用。

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