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.     

An agent-based system for bilateral contracts of energy

In this paper, an agent-based system for bilateral contracts of energy is proposed. The generating companies submit their offers to the demand companies. The demand companies also submit their bids to the generators. Each load or generator’s agent wants to match with an opponent, which offers the most valuable proposal. However, the problem of simultaneous decision-making causes decision conflicts among the agents. To overcome this conflict, we assume loads as the leaders and generators as the followers. We use Stackelberg game to match the seller and buyer agents. The negotiation process between a buyer and its potential seller will determine the power price between them. This process is carried out through a proposed combined time-behavioral protocol (TBP). With negligible changes in around the agreed price, this protocol can reduce the negotiation time considerably. After successful negotiation, the seller and buyer agents could sign a bilateral contract of energy if the market conditions allow it. The applicability of the proposed method is illustrated through a case study.     

A context-aware approach to automated negotiation using reinforcement learning

Agents negotiate depending on individual perceptions of facts, events, trends and special circumstances that define the negotiation context. The negotiation context affects in different ways each agent’s preferences, bargaining strategies and resulting benefits, given the possible negotiation outcomes. Despite the relevance of the context, the existing literature on automated negotiation is scarce about how to account for it in learning and adapting negotiation strategies. In this paper, a novel contextual representation of the negotiation setting is proposed, where an agent resorts to private and public data to negotiate using an individual perception of its necessity and risk. A context-aware negotiation agent that learns through Self-Play and Reinforcement Learning (RL) how to use key contextual information to gain a competitive edge over its opponents is discussed in two levels of temporal abstraction. Learning to negotiate in an Eco-Industrial Park (EIP) is presented as a case study. In the Peer-to-Peer (P2P) market of an EIP, two instances of context-aware agents, in the roles of a buyer and a seller, are set to bilaterally negotiate exchanges of electrical energy surpluses over a discrete timeline to demonstrate that they can profit from learning to choose a negotiation strategy while selfishly accounting for contextual information under different circumstances in a data-driven way. Furthermore, several negotiation episodes are conducted in the proposed EIP between a context-aware agent and other types of agents proposed in the existing literature. Results obtained highlight that context-aware agents do not only reap selfishly higher benefits, but also promote social welfare as they resort to contextual information while learning to negotiate.     

Automatic price negotiation on the web: An agent-based web application using fuzzy expert system

A traditional internet auction is restricted by the limitation of time. It is necessary to conduct an internet auction in a certain time period. The final trading price is determined until this certain period ends. This study improves this situation by removing the time limitation. Based on the fuzzy inference theory, this paper proposes an agent-based price negotiation system for on-line auctions. Mainly, three agents are used in the study: a seller agent, a buyer agent, and a mediator agent. The proposed system provides an easy-to-use environment and good customizability for users (buyers or sellers) to customize their price negotiation strategies using user-defined fuzzy rules. The final negotiated price is immediately determined after the buyer sends his bids to the proposed system. This study develops a Java-based computer package to implement the price negotiation system where Model-View-Controller (MVC) design pattern is employed in design of the package. Unified Modeling Language (UML) is also utilized to describe the structures and behaviors of the package. To validate the proposed system, this study built an on-line auction website with the proposed price negotiation mechanism for internet users to buy or sell their merchandises. An evaluation was finally conducted to investigate the users’ satisfaction with the proposed system.Briefly, the proposed system is featured by: (1) instantly getting negotiated price without waiting; (2) conducting price negotiation at any time; (3) determining strategy rules easily, and (4) using customizable negotiation strategies defined by users.     

A Negotiation Meta Strategy Combining Trade-off and Concession Moves

In this paper we present a meta strategy that combines two negotiation tactics. The first one based on concessions, and the second one, a trade-off tactic. The goal of this work is to demonstrate by experimental analysis that the combination of different negotiation tactics allows agents to improve the negotiation process and as a result, to obtain more satisfactory agreements. The scenario proposed is based on two agents, a buyer and a seller, which negotiate over four issues. The paper presents the results and analysis of the meta strategy’s behaviour.     

Pricing with local interactions on agent-based electronic marketplaces

This paper investigates the optimal pricing strategies of a selling agent that is randomly matched with several heterogeneous buying agents whose reservation prices are initially unknown. The seller perceives the behaviors of the buying agents through a logistic distribution with unknown parameters. We study the optimal learning by experimentation model of the logistic distribution. We extend this framework to a dynamic pricing model in which the selling agent is randomly matched with buying agents that are able to communicate their purchase experience to other buying agents. We carry out multi-agent system simulations of this dynamic pricing decision problem and we discuss some properties of the price dynamics one can observe on such marketplaces.     

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。     

基于补偿度和合作度的重复谈判研究

电子商务活动中,买卖双方由于需求进行重复谈判的情形广泛存在.利用Agent在以前谈判做出的让步和对手合作度评价形成多议题重复谈判策略,一方面使用让步度计算以前谈判中Agent所做出的让步,形成下次谈判中对提议的评价标准;另一方面通过对手的合作度决定下次谈判中对其的补偿程度,使得Agent能够对自己的所得到的补偿在以后的谈判中给予让步,形成一种稳定高效的谈判环境.     

多智能体协同选择的智能导购模型

基于多智能体协同选择提出了一种导购选择模型,该模型可识别其他可信买方智能体("值得信赖的朋友"),并将它们关于卖方的信息结合自身关于卖方的信息综合起来协同选择质高价低的卖方,从而实现高质量的导购性能。构建了一个存在多种类型的买方和卖方的购物模拟环境,并进行了多组实验。实验结果表明,该模型可以准确地识别可信买方智能体,并可在复杂的购物环境中高效地选择出优质卖方。此外,实验结果还表明,有了该模型,单个买方智能体选择优质卖方的能力要明显高于无多智能体协同选择情况下单个买方智能体的选择能力。     

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.     

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.     

Argumentation-based negotiation planning for autonomous agents

When we negotiate, the arguments uttered to persuade the opponent are not the result of an isolated analysis, but of an integral view of the problem that we want to agree about. Before the negotiation starts, we have in mind what arguments we can utter, what opponent we can persuade, which negotiation can finish successfully and which cannot. Thus, we plan the negotiation, and in particular, the argumentation. This fact allows us to take decisions in advance and to start the negotiation more confidently. With this in mind, we claim that this planning can be exploited by an autonomous agent. Agents plan the actions that they should execute to achieve their goals. In these plans, some actions are under the agent's control, while some others are not. The latter must be negotiated with other agents. Negotiation is usually carried out during the plan execution. In our opinion, however, negotiation can be considered during the planning stage, as in real life. In this paper, we present a novel approach to integrate argumentation-based negotiation planning into the general planning process of an autonomous agent. This integration allows the agent to take key decisions in advance. We evaluated this proposal in a multiagent scenario by comparing the performance of agents that plan the argumentation and agents that do not. These evaluations demonstrated that performance improves when the argumentation is planned, specially, when the negotiation alternatives increase.     

A protocol for multi-agent negotiation in a group-choice decision making process

Negotiation is one of the most important features of agent interactions found in multi-agent systems, because it provides the basis for managing the expectations of the individual negotiating agents, and it enables selecting solutions that satisfy all the agents as much as possible. In order for negotiation to take place between two or more agents there is need for a negotiation protocol that defines the rules of the game; consequently, a variety of agent negotiation protocols have been proposed in literature. However, most of them are inappropriate for Group-Choice Decision Making (GCDM) because they do not explicitly exploit tradeoff to achieve social optimality, and their main focus is solving two-agent negotiation problems such as buyer–seller negotiation. In this paper we present an agent negotiation protocol that facilitates the solving of GCDM problems. The protocol is based on a hybrid of analytic and artificial intelligence techniques. The analytic component of the protocol utilizes a Game Theory model of an n-person general-sum game with complete information to determine the agreement options, while the knowledge-based (artificial intelligence) component of the protocol is similar to the strategic negotiation protocol. Moreover, this paper presents a tradeoff algorithm based on Qualitative Reasoning, which the agents employ to determine the ‘amount’ of tradeoff associated with various agreement options. Finally, the paper presents simulation results that illustrate the operational effectiveness of our agent negotiation protocol.     

Adaptive Probabilistic Behavioural Learning System for the effective behavioural decision in cloud trading negotiation market

In cloud e-commerce application, building an automated negotiation strategy by understanding the uncertain information of the opponent preferences, utilities, and tactics is highly challenging. The key issue is to analyse and predict the uncertain behaviour of the opponent tactics to suggest the appropriate counter tactics that can reach maximum consensus. To handle such uncertain information, negotiation strategies follow several tactics with and without learning ability. Strategies without learning ability are restricted to negotiate with the opponent having only deterministic behaviour. To overcome this problem most researchers exploited the negotiation strategies with fixed learning ability using Bayesian learning, neural network learning, and genetic tactics. These tactics can learn the opponent’s behaviour and cannot guarantee to generate suitable counter-offer for all offers submitted by the opponent cloud service provider. This limitation motivates to propose a novel Adaptive Probabilistic Behavioural Learning System for managing the opponent having unpredictable random behaviours. The proposed Adaptive Probabilistic Behavioural Learning System contains a Behavioural Inference Engine to analyse the sequence of negotiation offer received by the broker for effectively learning the opponent’s behaviour over several stages of negotiation process. It also formulates the multi-stage Markov decision problem to suggest the broker with appropriate counter-offer behavioural tactics generation based on the adaptive probabilistic decision taken over the corresponding negotiation stage. Therefore, this research work can outperform the existing fixed behavioural learning tactics and hence maximize the utility value and success rate of negotiating parties without any break-off.     

Improving negotiations with bar charts: The advantages of priority awareness

Negotiations seldom lead to optimal results for the negotiators. The missing knowledge about the priorities of the negotiating parties is one known reason for this. This experimental study examines the effects of priority awareness on different measures of negotiation outcomes. Priority awareness is the awareness of one negotiator about the priorities of the other negotiator. One hundred thirty-two participants were randomly assigned to negotiation pairs in an experimental condition with priority awareness – created implicitly through the usage of an ordinary bar chart – or a control condition without priority awareness. They took over the roles of a car seller or buyer and negotiated within an experimental negotiation support system. They were neither explicitly instructed to use the bar chart in the negotiation or about its benefits, nor were they restricted in sharing any kind of information. The experimental condition showed not only a significantly higher negotiation performance in the form of joint outcome and pareto efficiency than the control condition, but also a higher impasse rate. Creating awareness about each other's priorities in a negotiation has a positive effect on the negotiation performance without noticeable negative effects on satisfaction with, or fairness and duration of, the negotiation.     

一种零售电子市场中的商品交易自动协商模型

本文提出了一个针对零售电子市场中商品交易协商的双边多议题自动协商模型。基于实质利益协商法的原则,综合采用带优先级的模糊约束满足问题(PFCSP)和多属性效用理论(MAUT)的思想对协商进行建模,给出了买卖双方Agent的形式化模型,对协商双方的行为和策略进行了算法描述,并对协商可能获得的结果进行了分析。     

A fuzzy system approach to multilateral automated negotiation in B2C e-commerce

Software agents in e-commerce systems are assigned to the participants. Buyer and supplier agents into multi-agent system architecture of the e-commerce system negotiate with others through an automated negotiation mechanism. In this study, an automated negotiation to interact between buyer and supplier and attain agreement for both is presented. A fuzzy inference system was used to automate negotiation process and consider two effective factors in the negotiation process: requirements and preferences. Requirements are qualitative or quantitative values which the participants assign to the issues of negotiation. Preferences of the participants are priorities assigned by them to issues. These values express an importance measure of issues from a participant perspective. Proposed model applies different fuzzy inference system (FIS) schemes for qualitative and quantitative negotiation issues to enhance the satisfaction level of the buyer and supplier. The FISs infer based on the preferences and requirements of both parties. Additionally, analytic hierarchy process was used to get preferences of the issues. In this proposal, mediator uses issue trade-offs strategy in which multiple issues are traded-offs against one another. The model applies a fuzzy system approach to make trade-offs.     

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.     

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。     

基于拍卖博弈的电子商务自动协商

为提高电子商务自动协商系统效率,本文以拍卖博弈理论为基础,提出并实现了一种基于拍卖博弈的自动协商Agent模型,并在此基础上实现了一个基于拍卖博弈的电子商务自动协商原型系统,应用于一个企业敏捷供应链管理系统中实现自动协商交易。