基于遗传算法的Agent多边多议题协商模型

提出了一种基于遗传算法的多边多议题协商模型,该模型能够根据协商过程中对手提议的变化情况动态估计对手偏好,提高了适应度函数的判断能力,促使协商达成一致.实验分析表明,该模型使各Agent均能在短时间内协商成功,并且各Agent获得的效用总和近似于最高社会效益,从而验证了该模型的有效性.     

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.     

基于自适应差分进化算法的多边多议题协商

为提高基于智能体(agent)的电子商务多边多议题协商的效率及稳定性,提出改进的自适应差分进化算法(ADE)并将其引入到合作环境下的多边多议题协商问题中。差分进化(DE)算法是目前求解连续空间内全局优化问题性能最优的进化优化算法之一。利用该算法收敛速度快、收敛精度高、全局寻优能力强等特点加快多边多议题协商的速度,使协商效率更高、稳定性更强。通过与目前解决多边多议题协商问题效果最好的混合遗传算法(HGA)对比,实验结果表明,自适应差分进化算法具有更快的收敛速度和更好的稳定性,可以使多边多议题协商中的各智能体达到协商最优解,并有效地减少协商次数,提高协商的效率和稳定性。     

基于贝叶斯分类的增强学习协商策略

为了帮助协商Agent选择最优行动实现其最终目标,提出基于贝叶斯分类的增强学习协商策略。在协商过程中,协商Agent根据对手历史信息,利用贝叶斯分类确定对手类型,并及时动态地调整协商Agent对对手的信念。协商Agen、通过不断修正对对手的信念,来加快协商解的收敛并获得更优的协商解。最后通过实验验证了策略的有效性和可用性。     

基于对手案例学习的多议题协商决策模型

为了解决多Agent系统(MAS)协商双方在信息对称情况下的自动协商问题,提出了一种用基于支持向量机算法的间接学习对手协商态度的协商方法,提出了不完全信息条件下基于案例和对策论的Agent多议题Pareto最优协商模型,通过支持向量机的方法来学习协商轨迹,得到协商对手在每个协商项的态度,然后利用学习得到的对手协商态度,构造了一个协商的决策模型,此模型能同时基于对手的态度和自身的偏好来做出协商决策。最后通过实验验证了该方法的先进性。     

基于Multi-Agent的军事油料虚拟仓库结盟协商研究

针对军事油料虚拟仓库的成员选择与构建实际,提出了基于Multi-Agent的虚拟仓库结盟协商模型,介绍了一种面向虚拟仓库的开放式异构多智能体协商协作环境,探讨了多智能体协作通信语言以及交互机制.最后提出了一个面向虚拟仓库协作的多阶段协商模型,分析了盟主与成员之间交互的协商策略以及各成员之间的多边协商过程.     

一种优化的基于增强学习协商策略

增强学习可以帮助协商Agent选择最优行动实现其最终目标。对基于增强学习的协商策略进行优化,在协商过程中充分利用对手的历史信息,加快协商解的收敛和提高协商解的质量。最后通过实验验证了算法的有效性和可用性。     

基于偏好的双边多议题协商优化

提出一种优化的自动协商模型。Agent在信知不完全的情况下通过学习交互历史和在线协商信息获取对手的偏好,结合贝叶斯方法和支持向量机学习对手偏好,基于保留值和权重提出一种决策模型。通过实验比较和分析,该模型能有效降低协商次数,提高协商双方的联合效用。在信息保密和先验知识未知的环境下,该模型仍然表现出了较高的效用和效率。     

基于SOA的多边多议题协商模型的研究

提出了一种新的协商模型--基于面向服务架构(SOA)的多边多议题协商模型.利用面向服务架构(SOA)技术来构建松散耦合的协商模型,并在协商模型中应用本体的基本理论和相关技术来定义提议、议题等本体模型,以实现协商议题的多样性和可变性,最后在SOA的框架下以Web服务方式使用经SOAP封装的通信原语实现多边多议题协商模型.     

基于对策论的Multi—Agent多边协商机制研究

针对决策支持系统中智能化程度低和协作困难的问题,特别是对多层次、多目标的决策困难问题,提出了基于对策论的Multi-Agent多边协商机制完成决策支持任务;探讨了基于对策论的多边协商机制,给出了Agent协商联盟的构成条件,提出了一个基于对策论的Multi—Agent多边协商机制,设计了基于Multi—Agent多边协商联盟的构成算法。该算法利用Multi-Agent的多边协商解决了多层次、多目标、多属性的竞争决策问题。最后通过算法验证实例证明了协商机制能更好地帮助用户进行决策,提高了决策系统的灵活性和准确性。     

多Agent自动协商的公平性研究

为了能够快速、高效地进行Agent协商,构建一个优化的多Agent协商模型。在这个模型的基础上,提出了一个基于协商各方公平性的协商学习算法。算法采用基于满意度的思想评估协商对手的提议,根据对方Agent协商历史及本次协商交互信息,通过在线学习机制预测对方Agent协商策略,动态得出协商妥协度并向对方提出还价提议。最后,通过买卖协商仿真实验验证了该算法的收敛性,表明基于该算法的模型工作的高效性、公平性。     

BLGAN: Bayesian Learning and Genetic Algorithm for Supporting Negotiation With Incomplete Information

Automated negotiation provides a means for resolving differences among interacting agents. For negotiation with complete information, this paper provides mathematical proofs to show that an agent's optimal strategy can be computed using its opponent's reserve price (RP) and deadline. The impetus of this work is using the synergy of Bayesian learning (BL) and genetic algorithm (GA) to determine an agent's optimal strategy in negotiation (N) with incomplete information. BLGAN adopts: (1) BL and a deadline-estimation process for estimating an opponent's RP and deadline and (2) GA for generating a proposal at each negotiation round. Learning the RP and deadline of an opponent enables the GA in BLGAN to reduce the size of its search space (SP) by adaptively focusing its search on a specific region in the space of all possible proposals. SP is dynamically defined as a region around an agent's proposal P at each negotiation round. P is generated using the agent's optimal strategy determined using its estimations of its opponent's RP and deadline. Hence, the GA in BLGAN is more likely to generate proposals that are closer to the proposal generated by the optimal strategy. Using GA to search around a proposal generated by its current strategy, an agent in BLGAN compensates for possible errors in estimating its opponent's RP and deadline. Empirical results show that agents adopting BLGAN reached agreements successfully, and achieved: (1) higher utilities and better combined negotiation outcomes (CNOs) than agents that only adopt GA to generate their proposals, (2) higher utilities than agents that adopt BL to learn only RP, and (3) higher utilities and better CNOs than agents that do not learn their opponents' RPs and deadlines.     

Quickly locating efficient, equitable deals in automated negotiations under two-sided information uncertainty

This paper develops an automated negotiation procedure inclusive of mechanism design and agent design for bilateral multi-issue negotiations under two-sided information uncertainty. The proposed negotiation mechanism comprises a protocol called MUP (Monotonic Utility-granting Protocol) and a matching strategy called WYDIWYG (What You Display Influences What You Get). The proposed preference elicitation procedure makes the agents faithful surrogates of the user they represent while the proposed Frontier Tracking Proposal Construction Algorithm (FTPCA) makes them learn the opponent's flexibility in negotiation and respond appropriately. The mechanism design and the agent design together help in locating efficient and equitable deals quickly. The efficiency, stability, simplicity, distribution symmetry and incentive compatibility of the proposed procedure are demonstrated through negotiation simulation experiments.     

一种基于相似度方法的并发协商策略

多Agent协商是目前人工智能、电子商务等领域研究的热点问题。在电子商务活动中为了达到更好的效果,获得更多的利益,买方希望与多个卖方进行并发协商(与多个卖方同时进行协商),从中选择最佳的交易解决方案,这种需求广泛存在。本文利用相似度方法(similarity criteria)发展和评价了一种双边多议题多Agent并发协商策略,一方面使用相似度函数来计算对手的偏好类型,从而选择相应的协商策略;另一方面协商一方采用该策略后能够使得双方达成一致的可能性最大并且收益最大,从而使得一个Agent能够在半竞争、信息不完全和不确定以及存在最大协商时间的情况下,更为有效地完成多Agent并发协商。     

Negotiating SLAs-An Approach for a Generic Negotiation Framework for WS-Agreement

The current Web Services Agreement specification draft proposes a simple request-response protocol for agreement creation only addressing bilateral offer exchanges. This paper proposes a framework augmenting this WS-Agreement to enable negotiations according to a variety of bilateral and multilateral negotiation protocols. The framework design is based on a thorough analysis of taxonomies for negotiations from the literature in order to allow for capturing a variety of different negotiation models within a single, WS-Agreement compatible, framework. In order to provide for the intended flexibility, the proposed protocol takes a two-stage approach: a meta-protocol is conducted among interested parties to agree on a common negotiation protocol first before the real negotiation is carried out in the second step due to the protocol established in the first step.     

Fuzzy Constraint-Based Agent Negotiation

Conflicts between two or more parties arise for various reasons and perspectives. Thus, resolution of conflicts frequently relies on some form of negotiation. This paper presents a general problem-solving framework for modeling multi-issue multilateral negotiation using fuzzy constraints. Agent negotiation is formulated as a distributed fuzzy constraint satisfaction problem (DPCSP). Fuzzy constrains are thus used to naturally represent each agent's desires involving imprecision and human conceptualization, particularly when lexical imprecision and subjective matters are concerned. On the other hand, based on fuzzy constraint-based problem-solving, our approach enables an agent not only to systematically relax fuzzy constraints to generate a proposal, but also to employ fuzzy similarity to select the alternative that is subject to its acceptability by the opponents. This task of problem-solving is to reach an agreement that benefits all agents with a high satisfaction degree of fuzzy constraints, and move towards the deal more quickly since their search focuses only on the feasible solution space. An application to multilateral negotiation of a travel planning is provided to demonstrate the usefulness and effectiveness of our framework.     

A Logic-Program-Based Negotiation Mechanism

This paper presents a logic-program-based mechanism of negotiation between two agents.In this mechanism an extended logic program(ELP) is regarded as an agent.The negotiation process between two agents is then modelled as multiple encounters between two ELPs,each of which selects an answer set as its initial demand.Both agents mutually revise the original sets of demands through accepting part of the opponent's demand and/or giving up part of its own demand.The overall dynamics can be regarded as mutual ...     

基于交错螺旋矩阵加密的自动信任协商模型

针对自动信任协商(ATN)中的敏感信息保护问题,提出了基于交错螺旋矩阵加密(ISME)的自动信任协商模型。此模型采用交错螺旋矩阵加密算法以及策略迁移法,对协商中出现的3种敏感信息进行保护。与传统的螺旋矩阵加密算法相比,交错螺旋矩阵加密算法增加了奇偶数位和三元组的概念。为了更好地应用所提模型,在该协商模型的证书中,引入了属性密钥标志位的概念,从而在二次加密时更有效地记录密钥所对应的加密敏感信息,同时列举了在协商模型中如何用加密函数对协商规则进行表示。为了提高所提模型协商成功率和效率,提出了0-1图策略校验算法。该算法利用图论中的有向图构造了6种基本命题分解规则,可以有效地确定由访问控制策略抽象而成的命题种类。之后为了证明在逻辑系统中此算法的语义概念与语法概念的等价性,进行了可靠性、完备性证明。仿真实验表明,该模型在20次协商中策略披露的平均条数比传统ATN模型少15.2条且协商成功率提高了21.7%而协商效率提高了3.6%。