Toward market-driven agents for electronic auction

While there are several existing agent-based systems addressing the crucial and difficult issues of automated negotiation and auction, this research has designed and engineered a society of trading agents with two distinguishing features: 1) a market-driven negotiation strategy and 2) a deal optimizing auction protocol. Unlike some of the existing systems where users manually select predefined trading strategies, in the market-driven approach, trading agents automatically select the appropriate strategies by examining the changing market situations. Results from a series of experiments suggest that the market-driven approach generally achieved more favorable outcomes as compared to the fixed strategy approach. Furthermore, it provides a more intuitive simulation of trading because trading agents are able to respond to different market situations with appropriate strategies. By augmenting the auction protocol with a deal optimization stage, trading agents can be programmed to optimize transaction deals by delaying the finalization of deals in search of better deals. Experimental results showed that by having a deal optimization stage, the auction protocol produced generally optimistic outcomes     

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.     

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.     

A Dynamic Multiagent System Based on a Negotiation Mechanism for Product Family Design

Many organizations are expending enormous efforts to minimize cost and time when developing new products by sharing and reusing distributed design knowledge and information. Multiagent systems provide an ideal mechanism to develop various products efficiently and cost-effectively by integrating distributed design knowledge and information. Product family design entails developing a set of differentiated products around a platform, while targeting individual products to distinct market segments. This paper presents a mathematical model reflecting the tradeoffs inherent in having a platform and introduces a dynamic multiagent system (DMAS) based on negotiation mechanisms to address the model effectively. The negotiation protocol to design a platform is developed using market-based auction mechanisms. In the proposed system, specific design tasks are assigned to agents by decomposing tasks for product family design, and an optimal platform is determined by negotiations between agents. We apply the DMAS to design a platform for a family of power tools.     

Continuous-Time Negotiation Mechanism for Software Agents

While there are several existing mechanisms and systems addressing the crucial and difficult issues of automated one-to-many negotiation, this paper develops a flexible one-to-many negotiation mechanism for software agents. Unlike the existing general one-to-many negotiation mechanism, in which an agent should wait until it has received proposals from all its trading partners before generating counterproposals, in the flexible one-to-many negotiation mechanism, an agent can make a proposal in a flexible way during negotiation, i.e., negotiation is conducted in continuous time. To decide when to make a proposal, two strategies based on fixed waiting time and a fixed waiting ratio is proposed. Results from a series of experiments suggest that, guided by the two strategies for deciding when to make a proposal, the flexible negotiation mechanism achieved more favorable trading outcomes as compared with the general one-to-many negotiation mechanism. To determine the amount of concession, negotiation agents are guided by four mathematical functions based on factors such as time, trading partners' strategies, negotiation situations of other threads, and competition. Experimental results show that agents guided by the four functions react to changing market situations by making prudent and appropriate rates of concession and achieve generally favorable negotiation outcomes     

Multi-item auctions for automatic negotiation

Available resources can often be limited with regard to the number of demands. In this paper we propose an approach for solving this problem, which consists of using the mechanisms of multi-item auctions for allocating the resources to a set of software agents. We consider the resource problem as a market in which there are vendor agents and buyer agents trading on items representing the resources. These agents use multi-item auctions, which are viewed here as a process of automatic negotiation, and implemented as a network of intelligent software agents. In this negotiation, agents exhibit different acquisition capabilities that let them act differently depending on the current context or situation of the market. For example, the ‘richer’ an agent is, the more items it can buy, i.e. the more resources it can acquire. We present a model for this approach based on the English auction, then we discuss experimental evidence of such a model.     

Explorations in evolutionary design of online auction market mechanisms

This paper describes the use of a genetic algorithm (GA) to find optimal parameter-values for trading agents that operate in virtual online auction ‘e-marketplaces’, where the rules of those marketplaces are also under simultaneous control of the GA. The aim is to use the GA to automatically design new mechanisms for agent-based e-marketplaces that are more efficient than online markets designed by (or populated by) humans. The space of possible auction-types explored by the GA includes the continuous double auction (CDA) mechanism (as used in most of the world’s financial exchanges), and also two purely one-sided mechanisms. Surprisingly, the GA did not always settle on the CDA as an optimum. Instead, novel hybrid auction mechanisms were evolved, which are unlike any existing market mechanisms. In this paper we show that, when the market supply and demand schedules undergo sudden ‘shock’ changes partway through the evaluation process, two-sided hybrid market mechanisms can evolve which may be unlike any human-designed auction and yet may also be significantly more efficient than any human designed market mechanism.     

A Secure Agent-based Framework for Internet Trading in Mobile Computing Environments

Most of the current Internet trading frameworks, in particular their negotiation and payment phases, are intended for customers frequently connected to the Internet during an entire transaction. This requirement cannot be easily met in the high communication cost and/or low bandwidth settings, typically found in mobile computing environments. Based on the software agent paradigm, a new secure agent-based framework for Internet trading in mobile computing environments is proposed in this paper. The framework is composed of two new protocols. One is the agent-based auction-like negotiation protocol, another is the agent-based payment protocol. Both of them are dedicated to solve the trade problems of Internet trading in mobile computing environments and ensured to be safe by cryptographic technologies. The combination of the two secure protocols constitutes an integrative solution for Internet trading in mobile computing environments.     

Market-Based Task Allocation Mechanisms for Limited-Capacity Suppliers

This paper reports on the design and comparison of two economically inspired mechanisms for task allocation in environments where sellers have finite production capacities and a cost structure composed of a fixed overhead cost and a constant marginal cost. Such mechanisms are required when a system consists of multiple self-interested stakeholders that each possess private information that is relevant to solving a systemwide problem. Against this background, we first develop a computationally tractable centralized mechanism that finds the set of producers that have the lowest total cost in providing a certain demand (i.e., it is efficient). We achieve this by extending the standard Vickrey-Clarke-Groves mechanism to allow for multiattribute bids and by introducing a novel penalty scheme such that producers are incentivized to truthfully report their capacities and their costs. Furthermore, our extended mechanism is able to handle sellers' uncertainty about their production capacity and ensures that individual agents find it profitable to participate in the mechanism. However, since this first mechanism is centralized, we also develop a complementary decentralized mechanism based around the continuous double auction. Again, because of the characteristics of our domain, we need to extend the standard form of this protocol by introducing a novel clearing rule based around an order book. With this modified protocol, we empirically demonstrate (with simple trading strategies) that the mechanism achieves high efficiency. In particular, despite this simplicity, the traders can still derive a profit from the market which makes our mechanism attractive since these results are a likely lower bound on their expected returns     

Tasks for agent-based negotiation teams: Analysis,review, and challenges

An agent-based negotiation team is a group of interdependent agents that join together as a single negotiation party due to their shared interests in the negotiation at hand. The reasons to employ an agent-based negotiation team may vary: (i) more computation and parallelization capabilities; (ii) unite agents with different expertise and skills whose joint work makes it possible to tackle complex negotiation domains; (iii) the necessity to represent different stakeholders or different preferences in the same party (e.g., organizations, countries, and married couple). The topic of agent-based negotiation teams has been recently introduced in multi-agent research. Therefore, it is necessary to identify good practices, challenges, and related research that may help in advancing the state-of-the-art in agent-based negotiation teams. For that reason, in this article we review the tasks to be carried out by agent-based negotiation teams. Each task is analyzed and related with current advances in different research areas. The analysis aims to identify special challenges that may arise due to the particularities of agent-based negotiation teams.     

基于移动Agent的安全电子拍卖系统的构建

随着电子商务技术的日益发展，移动Agent技术可以用来进行市场调查，商品买卖及离线拍卖等。事实证明只有当用移动Agent构建的系统的安全性得到确切的保证后，才能被大众用户所接受。该文提出了一个基于移动Agent的电子拍卖协议，并以此协议构建了一个实际的系统。系统中采用了主从结构，在认证过程中要求Agent向拍卖服务器出示许可证等方法来保证系统的安全性。最后，对协议设计的正确性和效率进行了详尽的分析。     

An adaptive attitude bidding strategy for agents in continuous double auctions

A continuous double auction (CDA) is an efficient market institution for real-world trading of commodities and electronic marketplaces. In this paper, we present the design and analysis of a new bidding strategy for buyer and seller agents participating in agent-based CDAs. The strategy employs heuristic rules and a reasoning mechanism based on a two-level adaptive bid-determination method, including short-term and long-term attitudes. Agents adopting the strategy dynamically adjust their behaviors in response to the changes of the supply and demand relationships in the market. Experimental results show that agents adopting the strategy outperform agents using other strategies reported in the literature.     

An adaptive attitude bidding strategy for agents in continuous double auctions

A continuous double auction (CDA) is an efficient market institution for real-world trading of commodities and electronic marketplaces. In this paper, we present the design and analysis of a new bidding strategy for buyer and seller agents participating in agent-based CDAs. The strategy employs heuristic rules and a reasoning mechanism based on a two-level adaptive bid-determination method, including short-term and long-term attitudes. Agents adopting the strategy dynamically adjust their behaviors in response to the changes of the supply and demand relationships in the market. Experimental results show that agents adopting the strategy outperform agents using other strategies reported in the literature.     

MoRVAM: A reverse Vickrey auction system for mobile commerce

Internet auctions bring buyers and sellers together for the purpose of trading goods and services online. In order to get the goods, a buyer must search for items through several auction sites. When the auction starts, the buyer needs to connect to these auction sites frequently so that he/she can monitor the bid states and re-bid. In this paper, we propose an automated negotiation model between two participants, for mobile commerce, using collaborative mobile agents called MoRVAM, which mediates between the buyer and the sellers, and executes bidding asynchronously and autonomously. A new RVT protocol is also implemented to achieve unconditional bid privacy. Advantages of the RVT protocol are addressed as well. All the bidding process can be implemented without revealing losing bid and unnecessary information.     

一种基于市场驱动的Multi-agent自动谈判策略

针对当前电子商务中基于Agent的谈判系统的谈判策略的静态性问题,提出基于市场驱动的谈判策略。Agent在谈判中能根据变化的市场情况做出可以调整比率的让步,帮助用户做出最优的交易决策,且自动地选择合适的策略。实验结果表明,采用基于市场驱动的策略比采用固定的策略的谈判结果更让用户感到满意。     

Expert systems and economic policy analysis: An application to OCS auction leasing

In this paper we argue that expert systems can be powerful tools for modelling microeconomic systems, including both individual decision making and the coordination of individual agents in a resource allocation mechanism. Using the fact that expert systems are essentially computerized versions of decision processes, we illustrate how they can be viewed as generalized process models of decision-making. We argue that the expert system approach is beneficial because it allows a policy analyst to explore the implication of policy alternatives without having to incur the generally prohibitive cost of field implementation studies. Further, enables the incorporation and updating of decision strategies and qualitative information, which human experts typically use but which is not amenable to pure mathematical modelling.One particular microeconomic system we suggest could be modelled as an expert system is the OCS offshore oil lease auction process. Moreover, we argue that constructing such an expert system model would require the development of two integrated expert systems: one for the auction process and subsequent resource allocation and the other to model the individual bidding behavior of the auction participants. We set out the structure of the auction expert system in some detail and discuss rules of thumb used by bidders inferred from our empirical research on past OCS auctions.Such an expert system of an auction leasing process could provide benefits to both bidders (e.g., oil companies) and the auctioneer (e.g., the Department of the Interior) as well. Bidders, by trying different strategies against different hypothesized strategies by their opponents could use such an integrated expert system to improve their bidding performances. The auctioneer, on the other hand, could test the efficiency of various proposed auction institutions under different assumptions about bidding behavior. In some circumstances, it might be desirable to even automate the auction process with a network coordinating the expert systems used by the individual firms and a computerized auctioneer.     

Analysis of B2B e-marketplaces: an operations perspective

The phenomenon of business-to-business (B2B) e-marketplaces has triggered a lot of interest among researchers in recent years. Most of these studies focus on price competition or cost reduction. Few studies, however, have examined the impact of e-marketplaces on the participants’ supply chain operations, including inventory management and order fulfillment. This study aims to fill such a gap. Employing agent-based simulations, we find that supply network agents tend to keep more inventories and backlog/lose fewer orders in the e-marketplace than in traditional supply chains. And this effect is profounder for the upstream agents, i.e., distributors and manufacturers than for downstream agents, i.e., retailers. Managerial implications of these findings are discussed.     

Cloud-enabled real-time platform for adaptive planning and control in auction logistics center

An auction logistics center (ALC) is the facility that is dedicated to all logistics and physical distribution, and provides auction functions for goods trading. Adaptive planning and control has been a hot research topic and discussed a lot in the field of manufacturing. Adaptive auction logistics planning and control (ALPC) is urgently required at the ALC to support large trading volumes and shorten processing time. To solve real-life industrial challenges, this paper presents a generic system architecture and its implementation along with the following dimensions. Firstly, a cloud-enabled platform for auction logistics center (CALC) is presented. It is proposed to implement efficient and effective ALPC, and to increase the flexibility in terms of execution of logistics operations and auction processes. Secondly, through the integration of IoT (Internet of Things) and cloud computing technologies, the proposed CALC creates a ubiquitous environment at the ALC, and establishes auction logistics services for different key stakeholders. The adaptive ALPC can be achieved with real-time visibility and traceability. Finally, this study presents a prototype of CALC to verify the proposed methodology. The case study in this paper also shows the potential of CALC to streamline operating processes in auction logistics environment.     

Software agents for knowledge management: coordination in multi-agent supply chains and auctions

A fundamental question that must be addressed in software agents for knowledge management is coordination in multi-agent systems. The coordination problem is ubiquitous in knowledge management, such as in manufacturing, supply chains, negotiation, and agent-mediated auctions. This paper summarizes several multi-agent systems for knowledge management that have been developed recently by the author and his collaborators to highlight new research directions for multi-agent knowledge management systems. In particular, the paper focuses on three areas of research:

• •Coordination mechanisms in agent-based supply chains. How do we design mechanisms for coordination, information and knowledge sharing in supply chains with self-interested agents? What would be a good coordination mechanism when we have a non-linear structure of the supply chain, such as a pyramid structure? What are the desirable properties for the optimal structure of efficient supply chains in terms of information and knowledge sharing? Will DNA computing be a viable tool for the analysis of agent-based supply chains?
• •Coordination mechanisms in agent-mediated auctions. How do we induce cooperation and coordination among various self-interested agents in agent-mediated auctions? What are the fundamental principles to promote agent cooperation behavior? How do we train agents to learn to cooperate rather than program agents to cooperate? What are the principles of trust building in agent systems?
• •Multi-agent enterprise knowledge management, performance impact and human aspects. Will people use agent-based systems? If so, how do we coordinate agent-based systems with human beings? What would be the impact of agent systems in knowledge management in an information economy?

     

Design and development of agent-based procurement system to enhance business intelligence

The purpose of this research is to propose a procurement system across other disciplines and retrieved information with relevant parties so as to have a better co-ordination between supply and demand sides. This paper demonstrates how to analyze the data with an agent-based procurement system (APS) to re-engineer and improve the existing procurement process. The intelligence agents take the responsibility of searching the potential suppliers, negotiation with the short-listed suppliers and evaluating the performance of suppliers based on the selection criteria with mathematical model. Manufacturing firms and trading companies spend more than half of their sales dollar in the purchase of raw material and components. Efficient data collection with high accuracy is one of the key success factors to generate quality procurement which is to purchasing right material at right quality from right suppliers. In general, the enterprises spend a significant amount of resources on data collection and storage, but too little on facilitating data analysis and sharing. To validate the feasibility of the approach, a case study on a manufacturing small and medium-sized enterprise (SME) has been conducted. APS supports the data and information analyzing technique to facilitate the decision making such that the agent can enhance the negotiation and suppler evaluation efficiency by saving time and cost.