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Electronic negotiation systems have been devised to create an electronic marketplace for bargaining, auctions, reverse auctions, and exchanges between multiple buyers and sellers. Most studies of negotiation systems concentrate on negotiation process modeling and data modeling - rather than on strategies and efficiency - for a multiple-criteria decision making (MCDM) problem in which many criteria are taken into account as attributes for decision making. This study proposes an active collaboration and negotiation framework (ACNF), which is a negotiation support system that uses active documents with embedded business logics or business rules that can adapt to different collaborative strategies in a business-to-business (B2B) environment. The risk preferences of negotiators are modeled and measured by utility functions that provide mathematical tools to compute the relative value of different courses of action. The system is demonstrated, and three experiments are conducted to validate its performance. The experiments show that the negotiation process is very efficient, and the results are both close to the efficient point - and the Pareto frontier - and are fair to both negotiating parties. The framework can be used to efficiently and effectively achieve a settlement in various multiple-criteria bargaining schemes in the electronic marketplace     

On Negotiations and Deal Making in Electronic Markets

Negotiation has traditionally been an important element in all types of commerce. As electronic commerce systems become generally available on the Internet, there is a need to support negotiation in the context of deal making. However, as in the physical world, the type of negotiation mechanism required is context dependent. In particular, we distinguish between the support required in the context of single deal and support required in coordinating negotiations across multiple deals. A framework is presented to describe deal making and negotiation in the context of a single deal. It is used to illustrate four representative Internet-based automated trading scenarios and to help understand the success of the scenario featuring online auctions. While popular, online auctions are limited in that they permit negotiation only along a single dimension such as price. We present ongoing work on multi-attribute negotiation mechanisms, and outline important new concepts relevant to supporting coordination across multiple deals.     

Real time prediction of closing price and duration of B2B reverse auctions

Nowadays, online auctions have become the most successful business model in the electronic marketplace. To the best of the authors’ knowledge, no other work has been devoted to the prediction of closing price and duration of Business-to-Business (B2B) English reverse online auctions in which goods or service providers compete with each other to win contracts by lowering offering prices with each bid, which is conducted on a virtual platform hosted on the Internet. This research designs and proposes a new methodology to predict closing prices and duration within the first few bids of the corresponding auctions based on real time bidding information rather than static auction information. In this article, we employ real time information and prediction rules to forecast the behavior of live auctions. This is in contrast to the static prediction approach that takes into consideration only information available at the beginning of an auction such as products, item features, or the seller’s reputation. This simulation is based on discretized auction data derived from a B2B online auction marketplace over a two-year period. Three measurements including accuracy, coverage, and benefit are used to evaluate the methodology. Results show that after observing the first 4 bids, this methodology can predict closing prices and duration with 84.6 and 71.9% accuracy, respectively.     

Application of strategic fuzzy games to wage increase negotiation and decision problems

We propose a flexible decision support scheme which could be used in managing the wage negotiation between employers and employees. This scheme uses fuzzy inference systems and game theory concepts in arriving at decisions on future wage increase which could be more mutually agreeable. For example, rather than specifying 5% yearly increase of wages, we propose that the uncertain factors which are mostly difficult to predict and that could affect wage decisions need to be taken into consideration by the wage formula. These include business revenues or (profit), inflation rate, number of competitors, cost of production, and other uncertain factors that may affect business operations. The accuracy of the fuzzy rule base and the game strategies will help to mitigate the adverse effects that a business may suffer from these uncertain factors. Based on our scheme, we propose that employers and employees should calculate their future wage by using a fuzzy rule base and strategies that take into consideration these uncertain variables. The proposed approach is illustrated with a case study and the procedure and methodology may be easily implemented by business organizations in their wage bargaining and decision processes.     

基于Agent和CBR的电子商务谈判系统模型研究

提出一种融合了多Agent和案例推理（CBR）技术的电子商务谈判系统模型,在多Agent环境下应用CBR技术捕获并重用以前成功的谈判案例,从中提取适应性策略来为交易提供决策支持,这些策略可以根据所处环境的改变动态生成。对相关问题进行了讨论,包括谈判案例的匹配和谈判策略的选择。     

Ontologies for supporting negotiation in e-commerce

In this paper we present our experience in applying Semantic Web technology to automated negotiation. This result is a novel approach to automated negotiation, that is particularly suitable to open environments such as the Internet. In this approach, agents can negotiate in any type of marketplace regardless of the negotiation mechanism in use. In order to support a wide variety of negotiation mechanisms, protocols are not hard-coded in the agents participating to negotiations, but are expressed in terms of a shared ontology, thus making this approach particularly suitable for applications such as electronic commerce. The paper describes a novel approach to negotiation, where the negotiation protocol does not need to be hard-coded in agents, but it is represented by an ontology: an explicit and declarative representation of the negotiation protocol. In this approach, agents need very little prior knowledge of the protocol, and acquire this knowledge directly from the marketplace. The ontology is also used to tune agents’ strategies to the specific protocol used. The paper presents this novel approach and describes the experience gained in implementing the ontology and the learning mechanism to tune the strategy.     

一个基于多阶段的多Agent多问题协商框架

多问题协商是电子交易中的关键问题．多Agent技术的不断成熟为这个问题的解决提供了有效的途径．提出了一个以理性Agent为基础的基于多阶段的多问题协商框架,该框架在时间约束下适用于信息不完全的场景,它描述了多问题的价格协商．为了降低多问题协商的复杂性,它将多问题协商分解为多阶段协商,每个阶段的大小（问题数）相同．阶段数和顺序在协商前确定,每个阶段中的问题顺序在协商中确定．在阶段大小相同的情况下,对给定协商问题的分解,框架能给出优化协商议程（agenda）．尤其是框架能为参与协商的Agent建立学习系统（LS）,以增强Agent的学习能力．最后基于这个框架实现了一个原型系统,原型系统证明这个框架是有效的．     

Fuzzy measures and integrals in evaluation of strategies

Artificial intelligence (AI) techniques have been applied to games for a long time. In this paper, we will explore the use of fuzzy measures and integrals to evaluate strategies in games. We will show their use in decision making when modeling auctions.The paper describes the role of fuzzy measures and integrals in decision making and reviews some computational aspects which are important for building real applications.     

Autonomous Adaptive Agents for Single Seller Sealed Bid Auctions

In developing open, heterogeneous and distributed multi-agent systems researchers often face a problem of facilitating negotiation and bargaining amongst agents. It is increasingly common to use auction mechanisms for negotiation in multi-agent systems. The choice of auction mechanism and the bidding strategy of an agent are of central importance to the success of the agent model. Our aim is to determine the best agent learning algorithm for bidding in a variety of single seller auction structures in both static environments where a known optimal strategy exists and in complex environments where the optimal strategy may be constantly changing. In this paper we present a model of single seller auctions and describe three adaptive agent algorithms to learn strategies through repeated competition. We experiment in a range of auction environments of increasing complexity to determine how well each agent performs, in relation to an optimal strategy in cases where one can be deduced, or in relation to each other in other cases. We find that, with a uniform value distribution, a purely reactive agent based on Cliff’s ZIP algorithm for continuous double auctions (CDA) performs well, although is outperformed in some cases by a memory based agent based on the Gjerstad Dickhaut agent for CDA.     

The design and evaluation of an intelligent sales agent for online persuasion and negotiation

Online purchases from e-stores are getting popular among Internet users. Although many e-commerce activities, such as auctions, bargaining, and recommendation services are available, most e-stores lack clerk-like mechanisms to persuade potential buyers into buying products and to bargain with them for making a good deal. The objectives of this research are to design a lab prototype of a sales agent with persuasion and negotiation capabilities and to evaluate its effectiveness as a virtual clerk in an e-store. The prototypical intelligent sales agent (ISA) is equipped with reinforcement learning capabilities and an abstract argument framework. We conduct both laboratory and Internet experiments to assess ISA’s performance. The experimental results reveal that an e-store embedded within such a sales agent can improve a seller’s surplus and increase a buyer’s product valuation, willingness-to-pay, and satisfaction with the e-store.     

Design, development and validation of an agent-based model of electronic auctions

This paper presents the design, development and validation methodology of an agent-based computational model of the B2C electronic auction marketplace. It aims at a comprehensive understanding of the varied issues governing a B2C electronic auction, incorporating the behavior of all relevant agents such as the auctioneer, the consumer and the retailer; and the environment in which these agents operate and interact. In contrast with conventional methods, agent based modeling employs a bottom-up modeling approach where behaviors of individual agents and rules for their interaction, specified at the micro level, give rise to emergent macro level phenomenon. The development methodology should ensure that agent models are aligned with theory, current knowledge of the field and observed phenomena, and output validity of the model also needs to be ascertained. Beginning with a general introduction to agent based computational modeling, this paper formalizes this alignment and validation methodology and elaborates each step, noting the rationale and means for achieving these. The manner in which this process was used in modeling B2C auctions is then described.     

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。     

Building an E-Business from Enterprise Systems

Building their companies into successful e-businesses has become an important objective for today's enterprises. Conceptually, it embodies the enabling of the business with such capabilities as global networking, streamlining business processes, sharing information, agility in responding to the market, and intelligent decision making. But how can these concepts be implemented in actual enterprises? Why do these desirable attributes of business systems suddenly become the definition of competency of companies large and small? This paper describes the framework for building an e-business from the enterprise information systems' perspective. Increasingly enterprise systems have extended beyond the traditional business functions and include such new features as supply-chain management, customer relationship management and electronic commerce. The shifting focus is driven by the adoption of the Web as a new channel for product distribution, marketing, and interacting with customers. The integration of the traditional as well as the Web-oriented functions is the cornerstone for a successful e-business. This paper presents a framework for e-businesses that on one hand build on the enterprise systems but on the other hand encompasses the new e-business dimensions.     

Develop a negotiation framework for automating B2B processes in the RosettaNet environment using fuzzy technology

The electronic trade between enterprises becomes more popular due to the rapid development of electronic commerce. Under the electronic business environment, in order to make the trade process smoother and faster, most enterprises adopt industry standards, such as EDI and RosettaNet, for developing electronic business platforms to transact business processes. These standards and platforms can help both trading sides to control and facilitate the business processes and validate the document format. However, these processes still need human efforts to make decisions, which might cause business losses due to time delay or human error during the process. In order to solve the problem, this study takes a step further to propose a negotiation framework for automating the B2B processes. This study chooses RosettaNet purchase order management processes as an example to implement automated negotiations on Biztalk platform. In order to evaluate the negotiation behavior of the opponent, two fuzzy membership functions, preference degree and opponent’s concession degree, are proposed to develop 25 levels of fuzzy rules to determine better offers to help to reach a settlement more responsively and dynamically. Experiment results show that our approach can help B2B transaction effectively.     

Enabling assisted strategic negotiations in actual-world procurement scenarios

In the everyday business world, the sourcing process of multiple goods and services usually involves complex negotiations (via telephone, fax, etc.) that include discussion of product and service features. Nowadays, this is a high-cost process due to the scarce use of tools that streamline this negotiation process and assist purchasing managers’ decision-making. With the advent of Internet-based technologies, it has become feasible the idea of tools enabling low-cost, assisted, fluid, on-line dialogs between buyer enterprises and their providers wherever they are located. Consequently, several commercial systems to support on-line negotiations (e-sourcing tools) have been released. It is our view that there is still a need for these systems to incorporate effective decision support. This article presents the foundations of Quotes, a commercial sourcing application developed by iSOCO that, in addition to cover the whole sequence of sourcing tasks, incorporates decision support facilities based on Artificial Intelligence (AI) techniques that successfully address previous limitations within a single and coherent framework. The paper focuses on the computational realization of sourcing tasks along with the decision support facilities they require. While supported negotiation events are Request for Quotations/Proposals (RFQs/RFPs) and reverse auctions, decision support facilities include offer generation, offer comparison, and optimal bid set computation (winner determination) in combinatorial negotiations. Additionally, the paper presents a compound of experiences and lessons learned when using Quotes for real sourcing processes.     

A computing theory for collaborative and transparent decision making under time constraint

In this paper, we present a computing theory on the accelerated critical point of selection of proper strategies between collaboration and competition and its mathematical analysis for collaborative and transparent decision making under “time constraint”, i.e., cost of time pressure, which decision makers face in negotiation. Web 2.0 provides a useful digital environment to support various time-stressed human behaviors for collaborative decision making. However, little research has examined the collaborative behavior under time constraint on the Internet in its designs and implementations on electronically supported decision making. Those systems are implemented in black boxes so that we need transparent designs of decision support systems to promote collaboration by time-stressed decision makers. The essential problem on collaborative decision making under time constraint is the discovery and evaluation on the critical point of selection of proper strategies between collaboration and competition. Most of the current decision support systems accept the half of maximum acceptable time for negotiation as a critical point, a priori, though that conventional point is often late for proper decision making. The proposed theory shows that a critical point is to be accelerated at the one-third of maximum acceptable time for negotiation. We have formulated the proposed computing theory based on mathematical formulation, and checked its feasibility in its applications to a case study. The proposed theory promotes collaborative and transparent decision making in consideration of the properties of stakes under time pressure by selecting the strategy of collaboration or competition at the much earlier stage.     

Combining bundle search with buyer coalition formation in electronic markets: A distributed approach through explicit negotiation

In electronic markets, both bundle search and buyer coalition formation are profitable purchasing strategies for buyers who need to buy small amount of goods and have no or limited bargaining power. In this paper, we present a distributed mechanism that allows buyers to use both purchasing strategies. The mechanism includes a heuristic bundle search algorithm and a distributed coalition formation scheme, which is based on an explicit negotiation protocol with low communication cost. The resulting coalitions are stable in the core in terms of coalition rationality. The simulation results show that this mechanism is very efficient. The resulting cost to buyers is close to the optimal cost.     

同时参加多宗网上拍卖的智能Agent的设计

本文提出的方法使得智能Agent能够同时监视多宗网上拍卖.Agent站在消费者的立场上作各种决定,并按照使用者的偏好尽力保证拍品的获得.Agent监视并收集各种正在进行的拍卖信息,然后决定它愿意参加哪个或同时参加哪些拍卖.至于在拍卖上的出价则基于一系列的策略和方法.     

Strategic business modeling: representation and reasoning

Business intelligence (BI) offers tremendous potential for business organizations to gain insights into their day-to-day operations, as well as longer term opportunities and threats. However, most of today’s BI tools are based on models that are too much data-oriented from the point of view of business decision makers. We propose an enterprise modeling approach to bridge the business-level understanding of the enterprise with its representations in databases and data warehouses. The business intelligence model (BIM) offers concepts familiar to business decision making—such as goals, strategies, processes, situations, influences, and indicators. Unlike many enterprise models which are meant to be used to derive, manage, or align with IT system implementations, BIM aims to help business users organize and make sense of the vast amounts of data about the enterprise and its external environment. In this paper, we present core BIM concepts, focusing especially on reasoning about situations, influences, and indicators. Such reasoning supports strategic analysis of business objectives in light of current enterprise data, allowing analysts to explore scenarios and find alternative strategies. We describe how goal reasoning techniques from conceptual modeling and requirements engineering have been applied to BIM. Techniques are also provided to support reasoning with indicators linked to business metrics, including cases where specifications of indicators are incomplete. Evaluation of the proposed modeling and reasoning framework includes an on-going prototype implementation, as well as case studies.