一种多Agent多议题的协商模型

协商是多Agent系统实现协作、协调和冲突消解的关键技术。本文分析了协商问题的实质和协商过程,提出了一种支持多轮协商的多Agent多议题协商模型。模型中引入了Agent类型的概念,在信息不完全的条件下,协商Agent通过推测协商对手的类型来指导自身的提议策略和协商战术,使提议更具针对性,避免了盲目性,从而节约了协商时间,提高了协
商质量。     

基于多Agent的虚拟企业结构与信息交互

虚拟企业具有分布性、自主性，互操作性，开放性等特点，传统的企业信息系统已经不能满足虚拟企业的这些特殊要求，在分析了多代理技术在虚拟企业中应用的必要性和可行性基础上，将多代理技术引入到虚拟企业信息系统中，构造了一个基于市场机制（Agora）的多Agent虚拟企业体系结构，在此基础上给出了Agent间的合作与协商模型，并描述了Agent之间的信息交互过程，最后对支持虚拟企业生命周期的原型系统的实现过程及信息交互过程进行了描述。     

一个基于Agent的轿车市场自动协商模型

该文提出了一个针对轿车市场中交易协商的双边多议题自动协商模型，该模型具有如下特点：用基于效用的相似度比较法实现Agent智能搜索；模型采用学习机制包括历史学习和Q-学习，历史学习机制用于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。Q-学习机制用于生成协商提议，使得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，更为有效地完成多议题协商。     

多Agent自动协商中机器学习的应用研究

目前将机器学习理论应用到多Agent自动协商系统中已成为电子商务领域的最新研究课题。本文即是利用贝叶斯法则来更新协商中的环境信息（即信念），利用强化学习中的Q学习算法生成协商中的提议，建立了一个具有学习机制的多Agent自动协商模型。并且封传统Q学习算法追行了扩充，设计了基于Agent的当前信念和最近探索盈余的动态Q学习算法。实验验证了算法的收敛性。     

一种具有自主学习能力的并发协商模型

提出一种具有自主学习能力的并发协商模型,通过使用增强学习方法的Q学习算法生成协商提议,使用相似度方法评价提议,使得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,更为有效地完成多议题多Agent并发协商。     

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.     

多议题协商服务平台的研究与实现

基于面向服务架构(SOA)的多Agent多议题协商模型融合了面向服务架构和多Agent多议题协商系统的特点.在协商服务平台中应用本体的基本概念和相关技术来定义协商的提议和议题,可实现协商议题的个性化和按需变化的动态性,提高了协商Agent的能力、效率和协商的有效性,让协商不再局限于某些特定协商对象.     

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.     

资源分配问题的协商模型

研究Agent多次协商过程中的策略调整问题,目的是使得Agent在协商过程中具有自学能力,对环境和协商对手更敏感。结合资源分配问题,讨论Agent协商过程中的学习问题,基于博弈论分别分析了单次协商和多次协商模型,给出了协商过程中在不同信息条件下遵循的策略,并进行了证明。     

Learning about the opponent in automated bilateral negotiation: a comprehensive survey of opponent modeling techniques

A negotiation between agents is typically an incomplete information game, where the agents initially do not know their opponent’s preferences or strategy. This poses a challenge, as efficient and effective negotiation requires the bidding agent to take the other’s wishes and future behavior into account when deciding on a proposal. Therefore, in order to reach better and earlier agreements, an agent can apply learning techniques to construct a model of the opponent. There is a mature body of research in negotiation that focuses on modeling the opponent, but there exists no recent survey of commonly used opponent modeling techniques. This work aims to advance and integrate knowledge of the field by providing a comprehensive survey of currently existing opponent models in a bilateral negotiation setting. We discuss all possible ways opponent modeling has been used to benefit agents so far, and we introduce a taxonomy of currently existing opponent models based on their underlying learning techniques. We also present techniques to measure the success of opponent models and provide guidelines for deciding on the appropriate performance measures for every opponent model type in our taxonomy.     

基于时间约束的不完全信息的战略协商模型

在实证的一对一协商中，协商Agent不仅要面临自己的最后期限的压力，同时又要预测协商对手的最后期限和其类型，协商Agent的协商战略必须满足理性与均衡的要求。提出了通过形式化的方法建立轮流出价协商模型，给出了轮流出价协商战略均衡的条件定义，求出了基于时间限制的不完全信息环境下满足均衡组合的协商战略，建立了依据均衡战略的实用化协商算法，最后分析了该算法产生的实验数据，并在相同环境下与Zeus协商模型比较显示，依从本模型的均衡战略的协商Agent能根据对对手的不确定信息的信念动态地采取行动，以获得最大的期望收益。     

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。     

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.     

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。     

Learning opponent’s beliefs via fuzzy constraint-directed approach to make effective agent negotiation

This work presents a general framework of agent negotiation with opponent learning via fuzzy constraint-directed approach. The fuzzy constraint-directed approach involves the fuzzy probability constraint and the fuzzy instance reasoning. The proposed approach via fuzzy probability constraint can not only cluster the opponent’s information in negotiation process as proximate regularities to improve the convergence of behavior patterns, but also eliminate the noisy hypotheses or beliefs to enhance the effectiveness on beliefs learning. By using fuzzy instance method, our approach can reuse the prior opponent knowledge to speed up the problem-solving, and reason the proximate regularities to acquire desirable results on predicting opponent behavior. In addition, the proposed interaction method enables the agent to make a concession dynamically based on expected objectives. Moreover, experimental results suggest that the proposed framework allows an agent to achieve a higher reward, a fairer deal, or a smaller cost of negotiation.     

Agent-based optimisation of logistics and production planning

Manufacturing and logistics service provision enterprises are currently moving towards open virtual enterprise collaboration networks to meet the needs of the Global Economy. In such networks, manufacturing and logistics planning and scheduling is challenging due to the difficulties in integrating information from different partners and in exploring a large and dynamically changing number of planning and scheduling alternatives. Agent-based technology is considered suitable to support planning and scheduling in such enterprises because agents can dynamically adapt their behaviour to changing requirements and they can reduce the number of planning and scheduling alternatives via negotiation.This paper presents an agent-based approach for supporting logistics and production planning, taking into account not only production schedules but also availability and cost of logistic service providers. This is achieved through efficient negotiation mechanisms based on an extended contracting protocol. The agent infrastructure is being developed within the context of Agentcities, a successful EU-funded initiative to build a world-wide distributed and open platform which provides agent-based services.The proposed approach is illustrated in a case study concerning optimisation of production planning of a virtual manufacturing enterprise in relation to sub-contracted logistic services used to transport materials between the enterprise units.     

The impact of available information on negotiation results

Negotiation is the most famous tool for reaching an agreement between parties. Usually, the different parties can be modeled as a buyer and a seller, who negotiate about the price of a given item. In most cases, the parties have incomplete information about one another, but they can invest money and efforts in order to acquire information about each other. This leads to the question of how much each party will be willing to invest on information about its opponent, prior to the negotiation process. In this paper, we consider the profitability of automated negotiators acquiring information on their opponents. In our model, a buyer and a seller negotiate on the price of a given item. Time is costly, and incomplete information exists about the reservation price of both parties. The reservation price of the buyer is the maximum price it is willing to pay for an item or service, and the reservation price of the seller is the minimum price it is willing to receive in order to sell the item or service. Our research is based on Cramton’s symmetrical protocol of negotiation that provides the agents with stable and symmetric strategies, and involves a delay in proposing an offer for signaling. The parties in Cramton’s model delay their offers in order to signal their strength, and then an agreement is reached after one or two offers. We determine the Nash equilibrium for agents that prefer to purchase information. Then, in addition to the theoretical background, we used simulations to check which type of equilibrium will actually be obtained. We found that in most of the cases, each agent will prefer to purchase information only if its opponent does. The reason for these results lies in the fact that an agent that prefers to purchase information according to a one-side method, signals its weakness and thereby reduces its position in the negotiation. Our results demonstrate the efficiency of joint information acquisition by both agents, but they also show that one-sided information purchasing may be inefficient, if the acquisition activity is revealed by the opponent, which causes it to infer that the informed agent is relatively weak.     

电子市场排污权交易的多智能体协商方法

为了平衡经济发展和污染治理之间的矛盾,将地理位置相对集中的排污企业建立专业电子市场用于排污权的交易。在控制排污总量的前提下,通过合理安排生产来最大化排污企业的收益。基于市场交互机制,给出了一个多智能体协商框架。每个智能体代表一家企业参与协商,在不暴露企业商业信息前提下,在当前污染物价格下通过规划方法计算污染物需求量。市场端接收每种污染物需求量并计算每种污染物价格,然后作为公共信息加以发布。仿真实验表明,相对于按比例单独缩减排污量,协商结果使得污染企业的个体收益都有所提高,从而增大了社会收益。