多智能体并发协商框架

Luo He, Yang Shanlin, Hu Xiaoxuan
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引用次数: 5

摘要

在多智能体系统中,任务的资源和单个智能体的能力可能是有限的。一个代理不能单独完成整个任务,它需要其他代理的帮助。因此,需要将任务分解为几个子任务。一些子任务将由原始代理在本地执行,而其他子任务则需要由其他代理在非本地完成。因此,在协商过程中,agent必须确保每个子任务的顺序和所有子任务的特征分配。在本文中,我们主要关注谈判的高水平,并讨论如何使谈判更有效率。提出了并行协商的建议,并利用并行行动模型提出了基于半马尔可夫决策过程的并行协商框架。该框架帮助代理在谈判中决定何时做出决策。实验结果验证了该模型的有效性,并与差分终止方案的结果进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Framework for Multi Agent Concurrent Negotiation
In the multi-agent systems, the resources for the task and the ability of each single agent may be limited.An agent cannot do the whole task by itself, it need the help of other agents. So, the task needs to be decomposed into several subtasks. Some of the subtasks will be carried out locally by the original agent,while others need to be done non-locally with other agents. So, during the negotiation process, agents must make sure the order of each subtask and the assignment of the features for all the subtasks. In this paper, we are mainly concerning about the high level of the negotiation, and discuss about how to make the negotiation more efficient. We suggest doing negotiations concurrently, and propose a concurrent negotiation framework based on the Semi Markov Decision Process by using the concurrent action model.The framework helps agents to decide when to make decision in the negotiation. Experimental results show the performance of the model, and compare the outcome with difference termination scheme.
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