DDSS中多Agent协商联盟的构建与算法分析

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。     

一种多agent系统框架与协商机制研究*

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。     

多Agent多问题协商模型

在多agent环境中,协商是多agent系统能够成功运转的关键.根据参与协商agent的数目和协商问题的数目,多agent环境中的协商可以分为双边-单问题协商、双边-多问题协商、多边-单问题协商、多边-多问题协商.前3种协商是多边-多问题协商在不同维上的简化.利用协商-协商过程-协商线程的概念建立了一个多边-多问题协商模型MMN(multi-agent multi-issue negotiation).该模型通过提供一个灵活的协商协议支持多agent环境中的不同协商形式,并且支持agent在协商过程中的学习.     

AODE中的多agent协商与学习

AODE是我们研制的一个面向agent的智能系统开发环境,本文以AODE为平台研究了多agent环境下的协商与学习本文利用协商-协商过程-协商线程的概念建立了多边-多问题协商模型MMN,该协商模型支持多agent环境中的多种协商形式及agent在协商过程中的学习,系统中的学习agent采用状态概率聚类空间上的多agent强化学习算法.该算法通过使用状态聚类方法减少Q值表存储所需空间,降低了经典Q-学习算法由于使用Q值表导致的对系统计算资源的要求,且该算法仍然可以保证收敛到最优解.     

基于学习的多议题协商

随着在线交易越来越普遍,如何有效地将先进的agent技术运用于电子商务协商领域,已经成为经济学家和计算机学者共同研讨的一个主要方向。文中从买方agent的观点出发,在协商过程中采用贝叶斯学习机制进行预测和更新对方agent的信念,使得每个agent通过学习来协调自身的行为,从而缩短了协商时间,提高了协商效率,更能有效地完成协商目的。并实验说明了其可行性。     

基于多议题协商的贝叶斯学习

随着Internet的日益完善和电子商务的普及,如何快速、高效地进行agent协商学习是必须面对和解决的一个重要问题。文中从买方agent的观点出发,在协商过程中采用贝叶斯学习机制进行在线更新对方agent的信念,从而缩短了协商时间,提高了协商效率,并实验说明了其可行性。     

合同网协议中的Agent可信度模型

针对经典的合同网协议（CNP），提出非合作型多agent系统环境下自私agent的可信度模型。为追求利益最大化，自私agent在自己能力不足的情况下仍有可能对宣布的任务进行投标。通过引入可信度模型，在对标书进行评价时将结合自私agent投标过程的历史记录进行决策，从而减少因自私agent能力不足而多次协商导致系统性能下降、任务完成质量不高等缺点。为说明可信度模型的性质，在JATLite平台上实现了基于可信度模型的合同网交互协商过程，并进行了对比实验。实验结果表明，基于可信度模型的agent协商策略在保证任务完成质量的基础上，尤其是在任务数目较大的情况下，能显著提高系统性能。     

基于多智能体的虚拟企业环境下自治agent的协商

讨论了基于多智能体(Multi-agent)的虚拟企业环境下自治agent的协商,针对虚拟企业环境下协商的特点,提出了虚拟企业环境下自治agent的协商通用形式化模型,并在模型的基础上给出了一系列协商策略与协商战略.使用这些策略与战略不仅可以帮助agent生成建议与反建议,而且可以帮助agent对收到的建议做出评价,以作为生成反建议或终止协商的依据.     

基于多议题协商的贝叶斯学习

随着Internet的日益完善和电子商务的普及，如何快速、高效地进行agent协商学习是必须面对和解决的一个重要问题。文中从买方agent的观点出发，在协商过程中采用贝叶斯学习机制进行在线更新对方agent的信念，从而缩短了协商时间，提高了协商效率，并实验说明了其可行性。     

一种协商在线增量学习算法及其在基于智能体的电子商务中的应用

随着多智能体系统MAS的迅猛发展,常常需要进行在线的协商。然而由于协商中不完全信息的存在,常常会大大影响协商的效果。该文提出一种多智能体协商中的动态在线增量学习算法,采用Q-学习机制来学习agent协商中的不完全信息。该文将这种学习算法应用基于智能体agent的电子商务中。实验证明算法可以加速协商的过程,提高协商的效果。     

A utility concession curve data fitting model for quantitative analysis of negotiation styles

A reciprocal function is proposed for defining the utility concession curve of a negotiation participant. The curve has only one free parameter and can fit the complete range of negotiation styles from extremely competitive to extremely collaborative. Various equations are derived, including the definition of a utility concession curve center which permits intuitive quantifying of a utility concession curve. Subsequently, an optimization model is proposed to fit the curve to a set of offers. Using the proposed model, a set of negotiations is mined for utility concession curves which are then used for clustering and hypothesis testing. Three negotiations styles seem to emerge from the data; slightly collaborative, neutral and quite competitive. It is also shown quantitatively that the level of competitiveness of the counterpart is negatively correlated with the agreement rate, and this is validated against the experimental treatment. Additionally, by the use of an experimental treatment, it is shown that the level of competitiveness of the counterpart has a positive causal impact on the negotiator’s style, causing him to become more competitive or collaborative. The data fitting model can also be used for incrementally fitting the curve in real-time during a negotiation to provide an estimate of the negotiation style which may help in the negotiation process.     

A conceptual model for negotiating in service-oriented environments

In recent years, Web services have been developed as a fundamental technique for the new generation of B2B or EAI applications. As they have matured and a new vision of service-oriented computing has emerged, the software industry has shifted its attention from developing required software to delivering desired services. In order to benefit from such a service-oriented model of software, several critical issues must be addressed in a service-oriented environment such as differentiation of services by various attributes, dynamic selection and provision of services in a supply chain style, and commitment of services with prescribed rules. From the managerial perspective, these issues are concerned with a process of negotiating desired services in a service-oriented environment. In this paper, we propose an object-oriented model that specifies such a negotiation process by architectural constructs where these critical issues are adequately addressed. The model contains a use case diagram that depicts requirements for the negotiation process, an architecture diagram that describes collaborative components for satisfying these requirements, and a class/sequence diagram that specifies class objects in these components to perform all behaviors occurring within the negotiation process. For illustration, the model is applied to an exemplified negotiation process for book publishing.     

Ontologies for supporting negotiation in e-commerce

In this paper we present our experience in applying Semantic Web technology to automated negotiation. This result is a novel approach to automated negotiation, that is particularly suitable to open environments such as the Internet. In this approach, agents can negotiate in any type of marketplace regardless of the negotiation mechanism in use. In order to support a wide variety of negotiation mechanisms, protocols are not hard-coded in the agents participating to negotiations, but are expressed in terms of a shared ontology, thus making this approach particularly suitable for applications such as electronic commerce. The paper describes a novel approach to negotiation, where the negotiation protocol does not need to be hard-coded in agents, but it is represented by an ontology: an explicit and declarative representation of the negotiation protocol. In this approach, agents need very little prior knowledge of the protocol, and acquire this knowledge directly from the marketplace. The ontology is also used to tune agents’ strategies to the specific protocol used. The paper presents this novel approach and describes the experience gained in implementing the ontology and the learning mechanism to tune the strategy.     

Compromising Adjustment Strategy Based on TKI Conflict Mode for Multi‐Times Bilateral Closed Negotiations

Bilateral multi‐issue closed negotiation is an important class for real‐life negotiations. Usually, negotiation problems have constraints such as a complex and unknown opponent's utility in real time, or time discounting. In the class of negotiation with some constraints, the effective automated negotiation agents can adjust their behavior depending on the characteristics of their opponents and negotiation scenarios. Recently, the attention of this study has focused on the interleaving learning with negotiation strategies from the past negotiation sessions. By analyzing the past negotiation sessions, agents can estimate the opponent's utility function based on exchanging bids. In this article, we propose a negotiation strategy that estimates the opponent's strategies based on the past negotiation sessions. Our agent tries to compromise to the estimated maximum utility of the opponent by the end of the negotiation. In addition, our agent can adjust the speed of compromise by judging the opponent's Thomas–Kilmann conflict mode and search for the Pareto frontier using past negotiation sessions. In the experiments, we demonstrate that the proposed agent has better outcomes and greater search technique for the Pareto frontier than existing agents in the linear and nonlinear utility functions.     

多Agent系统中双边多指标自动协商的ACEA算法

自动协商是多Agent系统中的一个中心议题,它是在Agent间建立一种合作合约,多数情况下这种合约包含多个协商指标,而多指标的协商比单一指标的协商要复杂得多·因此,如何快速、高效地进行Agent间的多指标自动协商是多Agent系统中必须解决的一个问题·给出了一个Agent间多指标协商的模型(MN),并在此基础上提出了双边—多指标协商的一种加速混沌进化算法(ACEA)·ACEA算法首先将混沌机制引入进化计算,然后采用压缩技术对算法进行加速,这样既克服了进化计算过早收敛到局部Nash平衡点的缺点,又解决了多指标协商繁杂的计算和引入混沌后带来的收敛速度慢的问题·理论分析和仿真实验表明,ACEA算法以概率1收敛到全局最优解·     

基于神经网络的协商学习机制

多agent协商研究中,如何通过学习提高协商效率是一个重要的课题,目前的研究多采用简单的学习算法,学习效果不好。论文首先提出了一个两方多回合交互协商框架,然后依据协商历史结果、协商双方初次出价等信知,对协商结果信息进行预测,从而确定协商交互中的推理策略,并利用BP神经网络的自适应、自学习能力对协商结果预测机制进行学习。随后的验证系统表明,该机制通过对协商结果的有效预测,提高了协商交互的效率和协商个体的效用。     

Development of a DRAM spots negotiation system using core hierarchy negotiation mechanism

The major factors of the DRAM negotiable transactions system in this research are the concession strategy and the negotiation mechanism of the sellers and buyers. In terms of negotiation mechanism, the core hierarchy model negotiation mechanism makes use of multistage sub-negotiates procedure to do multi-attribute negotiation for the sellers and buyers. The negotiable mechanism can further increase the successful rate of negotiation and the acceptable degree. In terms of concession strategy, research in the Liang and Doong, the concession strategy is constituted by times of negotiation, method of concession and times of persistence. There are less interactive concession for the sellers and buyers in the concession method of the negotiation in the related research. This research is to study the negotiable results that using various combinations of interactive concession strategy and the core hierarchy model negotiation mechanism. Be used as the reference of negotiation for the DRAM to trade on network.     

基于贝叶斯的多议题协商优化

在限时条件下的Agent之间的多议题协商中,虽然最差的结果是没有达成协定,而达成了一个使自己潜在利益受损的协定未必就是好的选择。在很多情况下,由于推理策略和交互机制的不完善使得Agent个体失去自己应得的利益。论文使用贝叶斯方法对协商对手进行预测,尽量使自己的初始信念准确反映对手的意识形态;并在此基础之上提出了一个优化的协商交互模型。在此模型中,Agent个体充分利用自己的预测结果,在协商成功的基础上获得尽可能多的利益。     

一种基于多样化历史信息的自动信任协商策略

针对自动信任协商的协商效率问题,提出了一种基于多样化历史信息的自动信任协商策略。本策略将历史信息作用于协商过程中,利用策略有向图来完成协商;利用票证来存储历史协商信息,并采用数字签名技术来保证信息的真实性和完整性。根据历史协商信息在产生方式上的不同,提出了信任票证和历史票证,并结合其特点设计了相关的格式以及验证和工作过程。最后进行了实验仿真,结果表明该模型可以提高重复协商的效率。     

一个基于多阶段的多Agent多问题协商框架

多问题协商是电子交易中的关键问题．多Agent技术的不断成熟为这个问题的解决提供了有效的途径．提出了一个以理性Agent为基础的基于多阶段的多问题协商框架,该框架在时间约束下适用于信息不完全的场景,它描述了多问题的价格协商．为了降低多问题协商的复杂性,它将多问题协商分解为多阶段协商,每个阶段的大小（问题数）相同．阶段数和顺序在协商前确定,每个阶段中的问题顺序在协商中确定．在阶段大小相同的情况下,对给定协商问题的分解,框架能给出优化协商议程（agenda）．尤其是框架能为参与协商的Agent建立学习系统（LS）,以增强Agent的学习能力．最后基于这个框架实现了一个原型系统,原型系统证明这个框架是有效的．