采用MSBN多智能体协同推理的智能农业车辆环境识别

为了解决智能农业车辆对所处复杂农田环境的识别信度定量分析困难的问题,提出了基于多连片贝叶斯网(MSBN)多智能体协同推理的目标识别算法.该方法把多智能体图像采集系统的局部信息表征在MSBN模型中,在观测不完备条件下,虽然单个智能体仅拥有目标的局部观测信息,但利用重叠子域信息的更新可以进行子网间消息的传播.利用MSBN局部推理和子网间信度通信的全局推理对多源信息进行融合,以提高识别性能.实验结果表明,与传统神经网络或BN方法相比,基于MSBN目标识别算法有效地对多源信息进行了补充,可以提高农业车辆在复杂环境进行识别的准确性.     

复杂系统的图模型多智能体协同故障诊断

为解决复杂、不确定系统的故障诊断实时推理问题,提出了基于图模型-多连片贝叶斯网络架构下多智能体协同推理的故障诊断方法。该方法将一个复杂贝叶斯网分割成若干有重叠的贝叶斯子网,使监控网络的单个智能体被抽象为一个拥有局部知识的贝叶斯网,利用成熟的贝叶斯网推理算法可完成智能体的自主推理。随后,通过重叠的子网接口进行多智能体间消息的传播,实现了多智能体协同故障诊断推理。实验结果表明了基于图模型多智能体的协同故障诊断方法的正确性和有效性。     

基于不完备信息预测的多智能体分布式协同

为了解决部分可观对抗环境中多智能体协同决策难题,受人大脑皮层通过记忆进行学习和推理功能启发,提出一种新的部分可观对抗环境下基于不完备信息预测的多智能体分布式协同决策框架。该框架可采用支持向量回归等多种预测方法通过历史记忆和当前观察信息对环境中不可见信息进行预测,并将预测信息和观察到的信息融合,作为协同决策的依据;再通过分布式多智能体强化学习进行协同策略学习得到团队中每个智能体的决策模型。使用该框架结合多种预测算法在典型的部分可观对抗环境中进行了多智能体协同决策的验证。结果表明,提出的框架对多种预测算法具有普适性,且在保证对不可见部分高预测精度时能将多智能体协同决策水平提升23.4%。     

基于规划识别的多智能体协作研究及应用

多智能体系统是规划识别的一个有效应用平台，提出一种基于规划识别多智能体协作算法，对对抗环境和非对抗环境中的基于规划识别的多智能体协作算法进行了分析，实现了对队友和对手行为目的的认识和建模，减少了协作主体间需要通信的时间厦难度。该协作算法应用到多智能体的有效测试平台机器人足球赛中，试验结果证明，该算法在通信受限、信息受限或信息延时的系统中可有效预测队友和对手的行为，从而实现智能体间的协作。     

基于遗传算法的多智能体协作行为研究

协作问题一直是多智能体系统研究的关键问题之一,该文给出了用遗传算法来实现多智能体协作的一种方法。该方法利用遗传算法来解决当多智能体系统无法得到环境信息或得到这些信息代价过高时,如何有效地产生它们的协同运动。利用该方法,对三个智能体协作把箱子搬到目标点,然后改变目标点,让智能体继续完成协作任务进行计算机仿真,结果表明遗传算法在动态环境下实现多智能体协作方面的可行性和有效性。     

基于多智能体协同的角色互换策略的研究应用

多智能体协同技术是人工智能领域的一个重要分支。机器人足球比赛为多智能体协同技术的研究提供了一个测试平台,仿真机器人足球比赛球员Agent具有号码属性与角色属性。文中以仿真机器人足球比赛中的球员Agent为研究对象,利用在线教练机制对球员Agent进行建模,提出了对手角色识别策略以及基于多智能体协同的球员Agent动态角色互换策略。在Agent2D底层中编程实现,与某球队进行测试,胜率大大增加,结果表明了该算法的有效性,该算法可提高球队的进攻能力。     

多智能体协作的两层强化学习实现方法

提出了多智能体协作的两层强化学习方法。该方法主要通过在单个智能体中构筑两层强化学习单元来实现,将该方法应用于3个智能体协作抬起圆形物体的计算机模拟中,结果表明比采用传统强化学习方法的智能体协作得更好。     

基于MADDPG的散装物料输送多智能体协同控制

为提升带式输送系统的智能化决策,提高生产效率,降低能耗,应用多智能体深度确定性策略梯度(MADDPG)算法,构建多输送机智能体协同控制系统。系统采用集中式结构控制多输送机,由输送机运行能耗模型,结合MADDPG算法结构,构建多智能体协同控制模型。通过训练模型,寻优输送机运行速度与煤流量最佳匹配关系,得出节能最优速度控制策略。与深度确定性策略梯度(DDPG)算法进行实验对比。结果表明,提出的多输送机智能体算法模型学习效率高,收敛速度快,具有较强的稳定性。     

多智能体协作模拟环境的设计与实现

提出了一种以可视化编程技术设计多智能体协作模拟环境的方案,通过模拟搬运物体的过程来演示智能体间的协作过程.提供了参数可调的环境模型,并对协作执行结果进行分析,为多智能体协作的探讨和研究提供了一个平台.     

多智能体差分进化算法

基于多智能体与差分进化算法的各自优势,充分地将对多智能体环境的感知和反作用于环境的能力与差分进化速度和全局寻优能力有机结合,提出一种多智能体差分进化算法.引入差分进化算子以提高智能体更新速度并保持群体多样性,同时应用正交交叉算子以改善智能体协作特性确保有效竞争,并通过局部寻优算子提高算法的寻优精度.对几种典型测试函数进行了测试,实验结果表明所提出的算法具有较强的全局寻优能力.     

Evaluating the trustworthiness of advice about seller agents in e-marketplaces: A personalized approach

In this paper, we present a model for evaluating the trustworthiness of advice about seller agents in electronic marketplaces. In particular, we propose a novel personalized approach for effectively handling unfair ratings of sellers provided to buyer agents from other buyers (called advisors). Our approach offers flexibility for buyers to weight their value for private and public knowledge about advisors. A personalized approach is proposed as well for buyers to model the trustworthiness of sellers, based on the advice provided. Experimental results demonstrate that our approach can effectively model trustworthiness for both advisors and sellers, even when there are large numbers of unfair ratings.     

A cooperative exchanging mechanism among seller agents for group-based sales

Agent-mediated electronic markets have been a growing area in intelligent agent research and development in recent years. Agents can act autonomously and cooperatively in an electronic market on behalf of their users. In such an electronic market, if a seller agent does not have enough of a particular item, it misses the opportunity to sell the item. Buyers also miss the opportunity to purchase the item. Namely, the overall negotiation utility is decreased. Thus, we propose a new cooperation mechanism among seller agents based on exchanging their goods in our agent-mediated electronic market system, G-Commerce. In G-Commerce, seller agents and buyer agents negotiate with each other. In our model, seller agents cooperatively negotiate in order to sell goods in stock. Buyer agents cooperatively form coalitions in order to buy goods based on discount prices. Seller agents’ negotiations are completed by using an exchanging mechanism for selling goods. Our experiments show that this exchanging mechanism enables seller agents to sell goods in stock effectively. We also demonstrate how our exchanging mechanism satisfies Pareto optimality.     

The Value of Temptation

There is an implicit assumption in electronic commerce that induces the buyers to believe that their deals will be handled appropriately. However, after a seller has already committed to a buyer, he may be tempted by several requests though he will not be able to supply them all. We analyze markets in which a finite set of automated buyers interacts repeatedly with a finite set of automated sellers. These sellers can satisfy one buyer at a time, and they can be tempted to break a commitment they already have. We have found the perfect equilibria that exist in markets with a finite horizon, and with an unrestricted horizon. A significant result stemming from our study reveals that sellers are almost always tempted to breach their commitments. However, we also show that if markets' designers implement an external mechanism that restricts the automated buyers actions, then sellers will keep their commitments.     

An economics-based negotiation scheme among mobile devices in mobile grid

In this paper, we propose an economics-based distributed negotiation scheme among mobile devices in mobile grid. In our model, there are energy negotiation and transactions between buyer devices and seller devices. Dynamic allocation of energy resources in mobile grid is performed through online transactions within markets. Mobile devices can be sellers and buyers that use optimization algorithms to maximize predefined utility functions during their transactions. Seller device agents sell the underlying energy resources of the mobile device. Buyer device agent makes buying decisions within the budget constraints to acquire energy resources. An economics-based negotiation algorithm among mobile devices is proposed. The proposed algorithm decomposes mobile grid system optimization problem into a sequence of two sub-problems. In the simulation, the performance evaluation of economics-based negotiation algorithm is evaluated.     

COMBINING TRUST MODELING AND MECHANISM DESIGN FOR PROMOTING HONESTY IN E‐MARKETPLACES

In this paper, we propose a novel incentive mechanism for promoting honesty in electronic marketplaces that is based on trust modeling. In our mechanism, buyers model other buyers and select the most trustworthy ones as their neighbors to form a social network which can be used to ask advice about sellers. In addition, however, sellers model the reputation of buyers based on the social network. Reputable buyers provide truthful ratings for sellers, and are likely to be neighbors of many other buyers. Sellers will provide more attractive products to reputable buyer to build their own reputation. We theoretically prove that a marketplace operating with our mechanism leads to greater profit both for honest buyers and honest sellers. We emphasize the value of our approach through a series of illustrative examples and in direct contrast to other frameworks for addressing agent trustworthiness. In all, we offer an effective approach for the design of e‐marketplaces that is attractive to users, through its promotion of honesty.     

The role of intermediaries in electronic marketplaces: developing a contingency model

Abstract. Early research in electronic markets seemed to suggest that e‐ commerce transactions would result in decreased costs for buyers and sellers alike, and would therefore ultimately lead to the elimination of intermediaries from electronic value chains. However, a careful analysis of the structure and functions of electronic marketplaces reveals a different picture. Intermediaries provide many value‐adding functions that cannot be easily substituted or ‘internalized’ through direct supplier–buyer dealings, and hence mediating parties may continue to play a significant role in the e‐commerce world. In this paper we provide an analysis of the potential roles of intermediaries in electronic markets and we articulate a number of hypotheses for the future of intermediation in such markets. Three main scenarios are discussed: the disintermediation scenario, in which market dynamics will favour direct buyer–seller transactions; the reintermediation scenario, in which traditional intermediaries will be forced to differentiate themselves and re‐emerge in the electronic marketplace; and the cybermediation scenario, in which wholly new markets for intermediaries will be created. The analysis suggests that the likelihood of each scenario dominating a given market is primarily dependent on the exact functions that intermediaries play in each case. A detailed discussion of such functions is presented in the paper, together with an analysis of likely outcomes in the form of a contingency model for intermediation in electronic markets.     

Extending the alternating-offers protocol in the presence of competition: models and theoretical analysis

The automation of bargaining is receiving a lot of attention in artificial intelligence research. Indeed, considering that bargaining is the most common form of economic transaction, its automation could lead software agents to reach more effective agreements. In the present paper we focus on the best-known bargaining protocol, i.e., the alternating-offers protocol. It provides an elegant mechanism whereby a buyer and a seller can bilaterally bargain. Although this protocol and its refinements have been studied extensively, no work up to the present provides an adequate model for bargaining in electronic markets. A result of these settings means that multiple buyers are in competition with each other for the purchase of a good from the same seller while, analogously, multiple sellers are in competition with each other for the sale of a good to the same buyer. The study of these settings is of paramount importance, as they will be commonplace in real-world applications. In the present paper we provide a model that extends the alternating-offers protocol to include competition among agents.¹ Our game theoretical analysis shows that the proposed model is satisfactory: it effectively captures the competition among agents, equilibrium strategies are efficiently computable, and the equilibrium outcome is unique. The main results we achieve are the following. 1) With m buyers and n sellers and when the outside option (i.e., the possibility of leaving a negotiation to start a new one) is inhibited, we show that it can be reduced to a problem of matching and that can be addressed by using the Gale-Shapley’s stable marriage algorithm. The equilibrium outcome is unique and can be computed in $O(l \cdot m\cdot n \cdot \overline T + (m+n)^2)$ , where l is the number of the issues and $\overline{T}$ is the maximum length of the bargaining. 2) With m buyers and one seller and when the seller can exploit the outside option, we show that agents’ equilibrium strategies can be computed in $O(l \cdot m \cdot \overline{T})$ and may be not unique. However, we show that a simple refinement of the agents’ utility functions leads to equilibrium uniqueness.     

Trust-oriented buyer strategies for seller reporting and selection in competitive electronic marketplaces

In competitive electronic marketplaces where some selling agents may be dishonest and quality products offered by good sellers are limited, selecting the most profitable sellers as transaction partners is challenging, especially when buying agents lack personal experience with sellers. Reputation systems help buyers to select sellers by aggregating seller information reported by other buyers (called advisers). However, in such competitive marketplaces, buyers may also be concerned about the possibility of losing business opportunities with good sellers if they report truthful seller information. In this paper, we propose a trust-oriented mechanism built on a game theoretic basis for buyers to: (1) determine an optimal seller reporting strategy, by modeling the trustworthiness (competency and willingness) of advisers in reporting seller information; (2) discover sellers who maximize their profit by modeling the trustworthiness of sellers and considering the buyers’ preferences on product quality. Experimental results confirm that competitive marketplaces operating with our mechanism lead to better profit for buyers and create incentives for seller honesty.     

Collaboration among mobile agents for efficient energy allocation in mobile grid

The use of mobile devices in grid environments may have two interaction aspects: devices are considered as users of grid resources or as grid resources providers. Due to the limitation constraints on energy and processing capacity of mobile devices, their integration into the Grid is difficult. In this paper, we investigate the cooperation among mobile devices to balance the energy consumption and computation workloads. Mobile devices can have different roles such as buyer devices and seller devices. In the mobile grid, the energies of mobile devices are uneven, energy-poor devices can exploit other devices with spare energy. Our model consists of two actors: A buyer device agent represents the benefits of mobile buyer device that intends to purchase energy from other devices. A seller device agent represents the profits of mobile seller device that is willing to sell spare energy to other devices. The objective of optimal energy allocation in mobile grid is to maximize the utility of the system without exceeding the energy capacity, expense budget and the deadline. A collaboration algorithm among mobile agents for efficient energy allocation is proposed. In the simulation, the performance evaluation of collaboration algorithm among mobile agents is conducted.