共查询到20条相似文献,搜索用时 79 毫秒
1.
多Agent的自动协商 总被引:9,自引:1,他引:9
协商是多Agent系统实现协调、协作和冲突消解的关键环节。如何构造有效的协商模型来提高Agent的协商能力,是多Agent系统研究中待解决的问题之一。文章主要讨论了双边多项目协商问题,提出了相应的协商模型、协议和协商算法,具有一定的通用性。 相似文献
2.
3.
4.
通过对DDSS中的多agent协商机制的分析,阐述了agent系统的协商联盟的概念,对构建agent协商联盟作了说明,并对协商联盟进行了公式化描述;提出了多方协商算法构建agent协商联盟,给出了算法的流程图和文字描述。协商联盟的建立是Agent协商机制的关键,也是提高系统的性能,增强其解决问题能力的关健,并使DDSS具有更好的灵活性。 相似文献
5.
为解决多Agent一对多、多议题协商问题,提出了具有议题属性协商阶段的多阶段协商模型,设计了一种根据Agent让步幅度变化所形成的曲线来判定Agent类型和使用何种协商方法的协商策略.详细地分析了多Agent、多阶段一对多协商的协商过程.将三角模糊数多属性决策方法引入到多Agent协商过程中降低了决策者评估对方所提出方案的难度,能更自然地对不同方案的优劣进行排序.模拟算例表明,该模型有效且可行,为多Agent协商提供可参考的模型和求解算法. 相似文献
6.
网络购物是当今社会发展的必然趋势,如何在丰富的网络资源中选择自己需求的商品达成交易是关键。因此根据Agent技术的特点,采用Agent技术对网络资源进行收集、选择、提取,获得用户满意的商品信息,并提出了一种多Agent协商策略模型,收到对方提出的意见后通过经验值和互助机制作出一定的判断,看是否达到预期的效果给出相应的反映。该模型主要通过经验值的积累,准确掌握对方的信息,制定出一套协商策略,采用利益随机调整方式选择对策,促进协商成功。经过实验证明,此算法有效。 相似文献
7.
高坚 《计算机应用与软件》2004,21(12):97-98
网络的普及促进了电子商务的发展,而智能代理是电子商务的关键,如何实现智能代理的快速、高效协商是一个很重要的问题。本文在加速模拟退火策略的基础上,提出了一种Agent协商优化算法。理论分析和仿真实验都表明该算法是一个快速、有效的方法。 相似文献
8.
9.
协商是多Agent研究领域的热点方向,对策论是目前多Agent协商领域研究的主要方法之一,而拍卖属于对策论研究范畴。总结了多Agent协商领域中一些重要的拍卖形式,并给出了拍卖过程的模型和一般的算法流程。 相似文献
10.
11.
针对当前电子商务中基于Agent的谈判系统的谈判策略的静态性问题,提出基于市场驱动的谈判策略。Agent在谈判中能根据变化的市场情况做出可以调整比率的让步,帮助用户做出最优的交易决策,且自动地选择合适的策略。实验结果表明,采用基于市场驱动的策略比采用固定的策略的谈判结果更让用户感到满意。 相似文献
12.
针对多议题协商中的僵局问题,提出了一个基于议题权值的优化策略。利用学习机制预测对手议题权值,并考虑多议题协商中各议题之间的相关性,在保证协商参与者利益的前提下,根据议题的权值,有针对性地调整议题预保留值的取值,从而能够打破僵局,并快速消解协商僵局,促使协商双方得到合理协商解,使得协商效率大大提高。 相似文献
13.
从基于动态、异构网络上快速构建稳健的多agent系统出发,设计了多agent远程过程调用通信模型,定义了三种基本类型的agent,对KQML消息规范进行扩展,增加了对消息生存周期的控制,设计了双缓存消息推送器以实现agent消息的主动推送,并在WCF的基础上实现了该通信框架。针对同目标多agent协作系统提出了基于开销均衡的agent系统交互协商策略,通过实例证明相对于独立运行和基于正交互协商策略的agent系统,本协商策略可有效降低系统总开销,并可使运行负载更为均衡。 相似文献
14.
15.
16.
协商是人们就某些议题进行交流寻求一致协议的过程.而自动协商旨在通过协商智能体的使用降低协商成本、提高协商效率并且优化协商结果.近年来深度强化学习技术开始被运用于自动协商领域并取得了良好的效果,然而依然存在智能体训练时间较长、特定协商领域依赖、协商信息利用不充分等问题.为此,本文提出了一种基于TD3深度强化学习算法的协商策略,通过预训练降低训练过程的探索成本,通过优化状态和动作定义提高协商策略的鲁棒性从而适应不同的协商场景,通过多头语义神经网络和对手偏好预测模块充分利用协商的交互信息.实验结果表明,该策略在不同协商环境下都可以很好地完成协商任务. 相似文献
17.
18.
Automated negotiation is a key form of interaction in systems that are composed of multiple autonomous agents. The aim of such interactions is to reach agreements through an iterative process of making offers. The content of such proposals are, however, a function of the strategy of the agents. Here we present a strategy called the trade-off strategy where multiple negotiation decision variables are traded-off against one another (e.g., paying a higher price in order to obtain an earlier delivery date or waiting longer in order to obtain a higher quality service). Such a strategy is commonly known to increase the social welfare of agents. Yet, to date, most computational work in this area has ignored the issue of trade-offs, instead aiming to increase social welfare through mechanism design. The aim of this paper is to develop a heuristic computational model of the trade-off strategy and show that it can lead to an increased social welfare of the system. A novel linear algorithm is presented that enables software agents to make trade-offs for multi-dimensional goods for the problem of distributed resource allocation. Our algorithm is motivated by a number of real-world negotiation applications that we have developed and can operate in the presence of varying degrees of uncertainty. Moreover, we show that on average the total time used by the algorithm is linearly proportional to the number of negotiation issues under consideration. This formal analysis is complemented by an empirical evaluation that highlights the operational effectiveness of the algorithm in a range of negotiation scenarios. The algorithm itself operates by using the notion of fuzzy similarity to approximate the preference structure of the other negotiator and then uses a hill-climbing technique to explore the space of possible trade-offs for the one that is most likely to be acceptable. 相似文献
19.
Resolving crises through automated bilateral negotiations 总被引:2,自引:0,他引:2
We describe the development of an automated agent that can negotiate efficiently with people in crises. The environment is characterized by two negotiators, time constraints, deadlines, full information, and the possibility of opting out. The agent can play either role, with communications via a pre-defined language. The model used in constructing the agent is based on a formal analysis of the crises scenario using game-theoretic methods and heuristics for bargaining. The agent receives messages sent by its opponent, analyzes them and responds. It also initiates discussion on one or more parameters of an agreement. Experimental results of simulations of a fishing dispute between Canada and Spain indicate that the agent played at least as well as, and in the case of Spain, significantly better than a human player. 相似文献