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。     

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。     

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。     

技术创新平台中基于Agent的多议题协商算法与策略

针对技术创新平台应用背景下的技术对接协商问题,结合智能感知Agent技术,分析并设计了多议题协商算法与策略。由技术创新平台中技术对接的实际环境,充分地利用平台中的历史技术对接提议,并考虑到技术对接双方的技术对接效益,设计技术对接中基于智能感知Agent的多议题协商算法,并在此基础上设计提议生成策略,提出技术对接协商中的建议解。保证了技术对接过程中技术交易双方的综合效益最优,使得技术交易双方能够在技术对接协商中达到效益“双赢”。通过技术创新平台中的技术对接的实际算例,例证了该协商算法与协商策略对技术创新平台中技术对接环境的适用性、合理性、可行性和有效性。     

一种基于信任的多Agent供应链协商关系网

Agent技术已被广泛用于供应链伙伴的协商。协商前如何选择协商Agent对提高协商效率有着重要的意义。提出了一种基于信任的多Agent协商关系网及其形成和更新算法,并对该协商关系网的特点进行了深入的研究。模拟表明,提出的协商关系网能有效地促进Agent之间的协商,提高协商成功率。     

一个信任和声誉模型及其应用

购物Agent在基于Web的电子市场中携带着用户的需求选择合适的销售者,在信息不完全的情况下,购买者需要利用信任和声誉在销售者中选择自己信任并能给自己带来较高效用的销售者进行协商。论文提出了一个信任和声誉度量模型,并为购物Agent提出了一个建立信任和声誉模型的算法,购物Agent以协商历史为基础利用这个算法来选择适合自己的销售者,从而提高协商效率,最后给出一个实例的分析。     

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Agent的多问题并行遗传谈判模型

Agent谈判增加了电子商务系统的主动性,一个有效的谈判模型是系统实现的关键。分析了已有Agent谈判模型的特点,设计的基于Agent的多问题并行谈判模型解决了已有谈判模型中存在的谈判问题单一、非并行、不考虑对手收益和固定权重等问题。模型中产生谈判方案的算法的自适应性体现在问题权重、遗传参数和收益偏差的动态调整上,给出了问题实数编码和权重调整公式。最后,设计了一个面向三个问题的电子谈判实例,验证了谈判模型的可行性和有效性。     

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。     

Agents that Learn How to Generate Arguments from Other Agents

Learning how to argue is a key ability for a negotiator agent. In this paper, we propose an approach that allows agents to learn how to build arguments by observing how other agents argue in a negotiation context. Particularly, our approach enables the agent to infer the rules for argument generation that other agents apply to build their arguments. To carry out this goal, the agent stores the arguments uttered by other agents and the facts of the negotiation context where each argument is uttered. Then, an algorithm for fuzzy generalized association rules is applied to discover the desired rules. This kind of algorithm allows us (a) to obtain general rules that can be applied to different negotiation contexts; and (b) to deal with the uncertainty about the knowledge of what facts of the context are taken into account by the agents. The experimental results showed that it is possible to infer argument generation rules from a reduced number of observed arguments.     

Algorithm selection in bilateral negotiation

Despite the abundance of strategies in the multi-agent systems literature on repeated negotiation under incomplete information, there is no single negotiation strategy that is optimal for all possible domains. Thus, agent designers face an “algorithm selection” problem—which negotiation strategy to choose when facing a new domain and unknown opponent. Our approach to this problem is to design a “meta-agent” that predicts the performance of different negotiation strategies at run-time. We study two types of the algorithm selection problem in negotiation: In the off-line variant, an agent needs to select a negotiation strategy for a given domain but cannot switch to a different strategy once the negotiation has begun. For this case, we use supervised learning to select a negotiation strategy for a new domain that is based on predicting its performance using structural features of the domain. In the on-line variant, an agent is allowed to adapt its negotiation strategy over time. For this case, we used multi-armed bandit techniques that balance the exploration–exploitation tradeoff of different negotiation strategies. Our approach was evaluated using the GENIUS negotiation test-bed that is used for the annual international Automated Negotiation Agent Competition which represents the chief venue for evaluating the state-of-the-art multi-agent negotiation strategies. We ran extensive simulations using the test bed with all of the top-contenders from both off-line and on-line negotiation tracks of the competition. The results show that the meta-agent was able to outperform all of the finalists that were submitted to the most recent competition, and to choose the best possible agent (in retrospect) for more settings than any of the other finalists. This result was consistent for both off-line and on-line variants of the algorithm selection problem. This work has important insights for multi-agent systems designers, demonstrating that “a little learning goes a long way”, despite the inherent uncertainty associated with negotiation under incomplete information.     

基于模糊相似关系的自动协商系统

基于模糊相似关系,提出一种面向电子商务实际应用的多属性自动协商系统方法。该方法在交易过程中依据单调让步协议,按照一定规则作出适当让步,在保证自身效用前提下,利用启发函数搜索与对方上次提议最接近的提议,从而最大化某一社会福利函数。将该方法与博弈论中经典的Zeuthen策略做了对比实验,仿真方法验证了该方法的有效性,说明了参数对算法性能的影响。     

A Balanceable Task Allocation Algorithm of Mobile Agent Based on Ad Hoc Network

A novel algorithm of task allocalion of mobile agent in ad hoc network is introduced in this paper. This algorithm extends the task oriented negotiation method to task allocation of n-people cooperation, makes the allocation balanceable to satisfy agents as much as possible. This algorithm also reduces the whole cooperation cost a lot. The simulation result is given at the end, and it proves that this algorithm is very useful in ad hoc network.     

Efficient Management of Multi-Linked Negotiation Based on a Formalized Model

A Multi-linked negotiation problem occurs when an agent needs to negotiate with multiple other agents about different subjects (tasks, conflicts, or resource requirements), and the negotiation over one subject has influence on negotiations over other subjects. The solution of the multi-linked negotiations problem will become increasingly important for the next generation of advanced multi-agent systems. However, most current negotiation research looks only at a single negotiation and thus does not present techniques to manage and reason about multi-linked negotiations. In this paper, we first present a technique based on the use of a partial-order schedule and a measure of the schedule, called flexibility, which enables an agent to reason explicitly about the interactions among multiple negotiations. Next, we introduce a formalized model of the multi-linked negotiation problem. Based on this model, a heuristic search algorithm is developed for finding a near-optimal ordering of negotiation issues and their parameters. Using this algorithm, an agent can evaluate and compare different negotiation approaches and choose the best one. We show how an agent uses this technology to effectively manage interacting negotiation issues. Experimental work is presented which shows the efficiency of this approach.