3,038 research outputs found

    Agent Based E-Market: Framework, Design, and Implementation

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    Attempt has been made to design and develop a complete adoptive Multi Agent System pertaining to merchant brokering stage of Customer Buying Behaviour Model with the intent of appropriate framework. Intelligent agents are autonomous entity which observe and act upon an environment. In general, they are software robots and vitally used in variety of e-Business applications. This paper focuses on the discussions on electronic markets and the adoptive role, which agents can play in information transformation for automating e-market transactions. It is proposed to develop a framework for agent-based electronic markets for buyers and sellers totally with the assistance of software agents.Agent Oriented e-Business, Agent Oriented e-Markets, Buyer/Seller Agents, Java, Multi Agent Systems

    Ontology acquisition and exchange of evolutionary product-brokering agents

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    Agent-based electronic commerce (e-commerce) has been booming with the development of the Internet and agent technologies. However, little effort has been devoted to exploring the learning and evolving capabilities of software agents. This paper addresses issues of evolving software agents in e-commerce applications. An agent structure with evolution features is proposed with a focus on internal hierarchical knowledge. We argue that knowledge base of agents should be the cornerstone for their evolution capabilities, and agents can enhance their knowledge bases by exchanging knowledge with other agents. In this paper, product ontology is chosen as an instance of knowledge base. We propose a new approach to facilitate ontology exchange among e-commerce agents. The ontology exchange model and its formalities are elaborated. Product-brokering agents have been designed and implemented, which accomplish the ontology exchange process from request to integration

    [[alternative]]An Interactive Negotiation Agent Based on Multi-Attribute Utility Theory for Internet Commerce

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    計畫編號:NSC91-2213-E032-020研究期間:200208~200307研究經費:612,000[[sponsorship]]行政院國家科學委員

    Multiple Issue Action and Market Algorithms for the World Wide Web

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    The Internet is quickly changing the way business-to-consumer and business-to-business commerce is conducted in the world. The Electronic Revolution has also spawned a trend of price wars and, in some instances, chaos because of the zero-sum nature of the electronic channel. The technology has created an opportunity to get beyond the lose-lose nature of single issue price wars by determining sellers' and buyers' preferences across multiple issues and encouraging negotiations, thereby creating possible joint gains for all parties. We develop simple multiple issue algorithms and heuristics that could be used in electronic auctions and electronic markets, to match business to business and consumers based on dovetailing underlying interests and preferences. We provide arguments that such dovetailed matches should help stabilize markets and make them more efficient

    Through the Telescope: UCITA and the Future of E-Commerce

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    Symposium: The New World of Intellectual Propert

    Congrats: a Configurable Granular Trust Scheme for Effective Seller Selection in an E-marketplace

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    Problem. The e-marketplace of today, with millions of buyers and sellers who never get to meet face to face, is susceptible to the presence of dishonest and fraudulent participants, prowling on unsuspecting trading partners to cheat in transactions, thereby increasing their profit to the detriment of their victims. There is also the multiplicity of goods and services with varying prices and quality, offered by a mix of honest and dishonest vendors. In order to participate in trade without incurring substantial loss, participants rely on intelligent agents using a trust evaluation scheme for partner selection. Making good deals thus depends on the ability of the intelligent agents to evaluate trading partners and picking only trustworthy ones. However, the existing trust evaluation schemes do not adequately protect buyers in the e-marketplace; hence, this study focused on designing a new trust evaluation scheme for buyer agents to use to effectively select sellers. -- Method. To increase the overall performance of intelligent agents and to limit loss for buyers in an e-marketplace, I propose CONGRATS—a configurable granular trust estimation scheme for effective seller selection. The proposed model used historical feedback ratings from multiple sources to estimate trust along multiple dimensions. I simulated a mini e-marketplace to generate the data needed for performance evaluation of the proposed model alongside two existing trust estimation schemes—FIRE and MDT. -- Results. At the peak of performance of CONGRATS, T1 sellers with the highest trust level accounted for about 45% of the total sales as against less than 10% recorded by the least trustworthy (T5) sellers. Compared to FIRE and MDT, CONGRATS had a performance gain of 15% and 30%, respectively, as well as an average earning of 0.89 (out of 1.0) per transaction in contrast to 0.70 and 0.62 per transaction respectively. Cumulative utility gain among buyer groups stood at 612.35 as contrasted to 518.96 and 421.28 for the FIRE and MDT models respectively. -- Conclusions. Modeling trust along multiple dimensions and gathering trust information from many different sources can significantly enhance the trust estimation scheme used by intelligent agents in an e-marketplace. This means that more transactions will occur between buyers and sellers that are more trustworthy. Inarguably, this will reduce loss to an infinitesimal level and consequently boost buyer confidenc
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