基于Agent和博弈分析的B2B协商系统模型研究

针对B2B电子商务在交易自动化和智能化方面的不足,提出了一种融合多Agent和鲁宾斯坦讨价还价博弈的B2B电子商务协商系统模型。在多Agent环境下,该系统可以降低网络的通信流量,提高交易效率,实现交易的自动化和智能化;鲁宾斯坦讨价还价博弈为协商系统提供了具体的协商策略。对B2B协商系统模型的协商流程、协商算法以及协商结果进行了讨论。     

多Agent多阶段一对多协商模型

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.     

基于Q-强化学习的多Agent协商策略及算法

针对传统Agent协商策略学习能力不足,不能满足现代电子商务环境需要的问题,采用Q-强化学习理论对Agent的双边协商策略加以改进,提出基于Q-强化学习的Agent双边协商策略,并设计实现该策略的算法。通过与时间协商策略比较,证明改进后的Agent协商策略在协商时间、算法效率上优于未经学习的时间策略,能够增强电子商务系统的在线学习能力,缩短协商时间,提高协商效率。     

一种求解饲料配方问题的优化算法

分析了饲料配方问题现有的求解策略,在Bruin提出的多Agent协商求解一种简化线性规划问题的理论模型的基础上,利用多Agent协商理论和线性规划理论,建立了Agent模型,提出了基于多Agent协商策略的饲料配方问题求解的优化算法,并证明了算法的正确性和可行性。实验结果表明,该算法具有更强的求解能力。     

协商Agent的历史学习算法研究

文章以买方Agent的观点对交易平台上获得的对方Agent历史协商信息进行分析,并根据其特点做初步过滤。在此基础上,该文针对现有协商模型中存在的问题,提出了一个Agent协商历史学习算法,并实验说明了其可行性。该算法可用于Agent协商前初始信念的创建,对Agent在协商中策略的选择、执行具有指导作用。     

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。     

基于GAI多属性依赖的协商模型

多属性之间的依赖关系增加协商Agent效用函数的复杂性,从而也增加多属性协商问题的复杂度,本文提出一种基于GAI多属性依赖的协商模型,该模型使用GAI分解将协商Agent的非线性效用函数表示为依赖属性子集的子效用之和,在协商过程中,协商双方采用不同的让步策略和提议策略来改变提议的内容,卖方Agent利用本文提出的GAI网合并算法将协商双方的GAI网合并,并利用生成的GAI树产生使社会福利评估值最大的提议,实验表明当买方Agent采用局部让步策略且卖方Agent采用全局让步策略时,协商双方能够在有限的协商步内达到接近Pareto最优的协商结局.     

对抗环境下多Agent协商问题的研究

利用多Agent系统具有的自治性和实时反应性,探讨对抗环境下的多Agent协商决策问题,提出一种混合式的多Agent结构协商模型,给出以最大团队效益为前提的协商求解策略和协商角色交换算法。通过协商,对抗环境中的Agent成员能够很好地进行动作策略选择和移动,能更好地进行进攻和防守。仿真实验验证了算法的可行性和有效性,结果表明其在一定程度上解决了多Agent系统中实时动态和受限通信对抗环境下的多Agent决策与合作问题。     

基于学习的多Agent多议题协商优化研究

以买方Agent的观点,对从交易平台上获得的卖方Agent的历史协商信息进行分析,并根据其特点做初步过滤。在此基础上,针对现有协商模型中存在的问题,提出了一个Agent协商对手选择算法和相应的交互机制,并验证了其可行性。该算法可用于Agent协商开始前协商对手的选择和初始信念的更新,对Agent在协商中策略的选择和执行具有指导作用,能有效提高Agent在协商中的效用及效率。     

基于Nearest-Biclusters协作过滤技术的效用图结构学习算法

在多议题协商研究中,议题之间的依赖关系增加了协商Agent效用函数的复杂性,从而使得多议题协商变得更加困难.基于效用图的多议题依赖协商模型是体现议题间依赖关系的多议题协商模型.在该协商模型中,协商双方仅需要较少的协商步数就能够找到满足Pareto效率的协商结局.如何有效地学习买方Agent的效用图结构是该协商模型的关键.文中基于Nearest-Biclusters协作过滤技术的思想提出了一种新的效用图结构学习算法(NBCFL算法).该算法首先利用Nearest-Biclusters协作过滤技术发现买方偏好的局部匹配特性,提取与当前买方Agent类型相同的买方Agent所产生的协商历史记录,然后通过计算各议题间的依赖度学习买方Agent的效用图结构.实验表明在参与协商的买方Agent类型不同的条件下,NBCFL算法比IBCFL算法能更好地学习买方Agent的效用图结构.     

基于博弈分析的自动协商系统

自动协商作为一个热点已经研究了很多年。大多数研究工作都着重于研究独立协商应用的抽象和理论模型，而对于实际算法的应用性只做了很少的工作。主要提出了一种基于博弈论的比较有效的协商模型来解决协商中的冲突。在该模型中利用遗传算法进行策略优化，而利用另外一个算法对已有的No-Fear-of-Deviation（NFD）算法进行了改进。     

Addressing stability issues in mediated complex contract negotiations for constraint-based, non-monotonic utility spaces

Negotiating contracts with multiple interdependent issues may yield non- monotonic, highly uncorrelated preference spaces for the participating agents. These scenarios are specially challenging because the complexity of the agents’ utility functions makes traditional negotiation mechanisms not applicable. There is a number of recent research lines addressing complex negotiations in uncorrelated utility spaces. However, most of them focus on overcoming the problems imposed by the complexity of the scenario, without analyzing the potential consequences of the strategic behavior of the negotiating agents in the models they propose. Analyzing the dynamics of the negotiation process when agents with different strategies interact is necessary to apply these models to real, competitive environments. Specially problematic are high price of anarchy situations, which imply that individual rationality drives the agents towards strategies which yield low individual and social welfares. In scenarios involving highly uncorrelated utility spaces, “low social welfare” usually means that the negotiations fail, and therefore high price of anarchy situations should be avoided in the negotiation mechanisms. In our previous work, we proposed an auction-based negotiation model designed for negotiations about complex contracts when highly uncorrelated, constraint-based utility spaces are involved. This paper performs a strategy analysis of this model, revealing that the approach raises stability concerns, leading to situations with a high (or even infinite) price of anarchy. In addition, a set of techniques to solve this problem are proposed, and an experimental evaluation is performed to validate the adequacy of the proposed approaches to improve the strategic stability of the negotiation process. Finally, incentive-compatibility of the model is studied.     

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%。     

STRATUM: A METHODOLOGY FOR DESIGNING HEURISTIC AGENT NEGOTIATION STRATEGIES

Automated negotiation is a powerful (and sometimes essential) means for allocating resources among self-interested autonomous software agents. A key problem in building negotiating agents is the design of the negotiation strategy, which is used by an agent to decide its negotiation behavior. In complex domains, there is no single, obvious optimal strategy. This has led to much work on designing heuristic strategies, where agent designers usually rely on intuition and experience. In this article, we introduce STRATUM, a methodology for designing strategies for negotiating agents. The methodology provides a disciplined approach to analyzing the negotiation environment and designing strategies in light of agent capabilities and acts as a bridge between theoretical studies of automated negotiation and the software engineering of negotiation applications. We illustrate the application of the methodology by characterizing some strategies for the Trading Agent Competition and for argumentation-based negotiation.     

The Influences of Deception and Computer-Mediation on Dyadic Negotiations

This article reports on an experiment investigating the differences between computer-mediated and face-to-face negotiations and between negotiators being deceptive about hidden agendas and negotiators without hidden agendas. Our results supported the hypotheses that individuals negotiating via instant messaging are more likely to use forcing negotiating, experience more tension, and have lower deception detection accuracy than individuals negotiating face-to-face. Unexpectedly, it was found that individuals negotiating via instant messaging were more satisfied with the negotiation process than were face-to-face negotiators. Finally, results supported the hypothesis that those being deceptive about hidden agendas experienced higher tension than those without hidden agendas. These findings have several implications for organizations: higher levels of tension from computer-mediated negotiations and from deception can affect the long-term effectiveness of employees, undetected deception in computer-mediated negotiations can have a negative impact on negotiations, and computer-mediation can lead to the use of a forcing negotiation style, which may improve the effectiveness of negotiators with individualistic goals.     

求解组合拍卖NP问题的遗传算法

多智能体系统采用多件物品组合拍卖协商协议能高效地实现组合资源及多任务的分配,但在传统组合拍卖协商协议中拍卖方选择买方以实现最大赢利的过程是一个NP问题,本文提出了利用改进的遗传算法来求解该NP问题的新方法,并应用于一个企业供应链管理的自动协商交易系统中。实验表明,该算法具有较优的性能。     

Classification-based self-adaptive differential evolution and its application in multi-lateral multi-issue negotiation

Multi-lateral multi-issue negotiations are the most complex realistic negotiation problems. Automated approaches have proven particularly promising for complex negotiations and previous research indicates evolutionary computation could be useful for such complex systems. To improve the efficiency of realistic multi-lateral multi-issue negotiations and avoid the requirement of complete information about negotiators, a novel negotiation model based on an improved evolutionary algorithm p-ADE is proposed. The new model includes a new multi-agent negotiation protocol and strategy which utilize p-ADE to improve the negotiation efficiency by generating more acceptable solutions with stronger suitability for all the participants. Where p-ADE is improved based on the well-known differential evolution (DE), in which a new classification-based mutation strategy DE/rand-to-best/pbest as well as a dynamic self-adaptive parameter setting strategy are proposed. Experimental results confirm the superiority of p-ADE over several state-of-the-art evolutionary optimizers. In addition, the p-ADE based multiagent negotiation model shows good performance in solving realistic multi-lateral multi-issue negotiations.     

The importance of language familiarity in global business e-negotiation

This study explores the influence of language familiarity on online persuasion behavior based on subjective measurements and objective actual negotiation behavior. It was designed to test whether negotiating in a non-native language decreases the negotiation self-efficacy, given that the increasing use of global e-marketplaces and popularity of international business trades make negotiation in a non-native language inevitable. An online experiment was conducted using a text-based asynchronous e-negotiation system, with two groups of subjects negotiating in native and non-native languages separately in purchasing negotiations. The analysis results show that language familiarity plays a critical role in inducing persuasion behavior in e-negotiations, with a higher language familiarity leading to higher language self-efficacy and negotiation self-efficacy. However, only negotiation self-efficacy affects e-negotiation communication efficiency and effectiveness, both of which increase online persuasion behavior. Based on actual negotiation behavior, the results show that non-native language negotiators are less active than native language negotiators in negotiations. However, the negotiation outcome did not differ significantly between the two groups, suggesting that the final outcome is also influenced by other factors. The results also showed that language familiarity has a greater effect on the buyer than on the seller.     

Theoretical and Experimental Investigation of Driver Noncooperative-Game Steering Control Behavior

This paper investigates two noncooperative-game strategies which may be used to represent a human driver’s steering control behavior in response to vehicle automated steering intervention. The first strategy, namely the Nash strategy is derived based on the assumption that a Nash equilibrium is reached in a noncooperative game of vehicle path-following control involving a driver and a vehicle automated steering controller. The second one, namely the Stackelberg strategy is derived based on the assumption that a Stackelberg equilibrium is reached in a similar context. A simulation study is performed to study the differences between the two proposed noncooperative- game strategies. An experiment using a fixed-base driving simulator is carried out to measure six test drivers’ steering behavior in response to vehicle automated steering intervention. The Nash strategy is then fitted to measured driver steering wheel angles following a model identification procedure. Control weight parameters involved in the Nash strategy are identified. It is found that the proposed Nash strategy with the identified control weights is capable of representing the trend of measured driver steering behavior and vehicle lateral responses. It is also found that the proposed Nash strategy is superior to the classic driver steering control strategy which has widely been used for modeling driver steering control over the past. A discussion on improving automated steering control using the gained knowledge of driver noncooperative-game steering control behavior was made.