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Intelligent agents for automated one-to-many e-commerce negotiation

TLDR
This paper presents a framework for one- to-many negotiation by means of conducting a number of concurrent coordinated one-to-one negotiations, and outlines two levels of strategies that can be exercised on two levels, the individual negotiation level, and the coordination level.
Abstract
Negotiation is a process in which two or more parties with different criteria, constraints, and preferences, jointly reach an agreement on the terms of a transaction. Many current automated negotiation systems support one-to-one negotiation. One-to-many negotiation has been mostly automated using various kinds of auction mechanisms, which have a number of limitations such as the lack of the ability to perform two-way communication of offers and counteroffers. Moreover, in auctions, there is no way of exercising different negotiation strategies with different opponents. Even though auction-based online trading is suitable for many applications, there are some in which there is a need for such greater flexibility. There has been a significant body of work towards sophisticated one-to-one automated negotiation. In this paper, we present a framework for one-to-many negotiation by means of conducting a number of concurrent coordinated one-to-one negotiations. In our framework, a number of agents, all working on behalf of one party, negotiate individually with other parties. After each negotiation cycle, these agents report back to a coordinating agent that evaluates how well each agent has done, and issues new instructions accordingly. Each individual agent conducts reasoning by using constraint-based techniques. We outline two levels of strategies that can be exercised on two levels, the individual negotiation level, and the coordination level. We also show that our one-to-many negotiation architecture can be directly used to support many-to-many negotiations. In our prototype Intelligent Trading Agency (ITA), agents autonomously negotiate multi- attribute terms of transactions in an e-commerce environment tested with a personal computer trading scenario.

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Journal ArticleDOI

Argumentation-based negotiation

TL;DR: This article provides a conceptual framework through which the core elements and features required by agents engaged in argumentation-based negotiation, as well as the environment that hosts these agents are outlined, and surveys and evaluates existing proposed techniques in the literature.
Journal ArticleDOI

Agent-Based Cloud Computing

TL;DR: This work devised a complex cloud negotiation mechanism that supports parallel negotiation activities in interrelated markets: a cloud service market between consumer agents and broker agents, and multiple cloud resource markets between broker agents and provider agents.
Journal ArticleDOI

Learning about the opponent in automated bilateral negotiation: a comprehensive survey of opponent modeling techniques

TL;DR: All possible ways opponent modeling has been used to benefit agents so far are discussed, and a taxonomy of currently existing opponent models based on their underlying learning techniques is introduced, which provides guidelines for deciding on the appropriate performance measures for every opponent model type in their taxonomy.
Journal ArticleDOI

Fuzzy trust evaluation and credibility development in multi-agent systems

TL;DR: This paper proposes not only a customisable trust evaluation model based on fuzzy logic but also demonstrates the integration of post-interaction processes like business interaction reviews and credibility adjustment.
Proceedings ArticleDOI

Coordinating Multiple Concurrent Negotiations

TL;DR: This paper presents a novel heuristic model for coordinating multiple bilateral negotiations that is empirically evaluated and shown to be effective and robust in a range of negotiation scenarios.
References
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Lecture Notes in Artificial Intelligence

P. Brezillon, +1 more
TL;DR: The topics in LNAI include automated reasoning, automated programming, algorithms, knowledge representation, agent-based systems, intelligent systems, expert systems, machine learning, natural-language processing, machine vision, robotics, search systems, knowledge discovery, data mining, and related programming languages.
Book

Rules of encounter: designing conventions for automated negotiation among computers

TL;DR: This chapter discusses the negotiation problem in different domains attributes of negotiation mechanisms assumptions incentive compatibility, and the hierarchy of deal types - summary unbounded worth of a goal - tidy agents.
Journal ArticleDOI

Algorithms for constraint-satisfaction problems: a survey

Vipin Kumar
- 01 Apr 1992 - 
TL;DR: A large number of problems in AI and other areas of computer science can be viewed as special cases of the constraint-satisfaction problem, and a number of different approaches have been developed for solving them.
Proceedings Article

Kasbah: An Agent Marketplace for Buying and Selling Goods

TL;DR: How Kasbah works is described, a system where users create autonomous agents to buy and sell goods on their behalf and the implementation of a simple proof-of-concept prototype is discussed.
Journal ArticleDOI

Agent-mediated electronic commerce: a survey

TL;DR: The CBB model the authors present augments traditional marketing models with concepts from Software Agents research to accommodate electronic markets and discusses the variety of Artificial Intelligence techniques that support agent mediation.
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Trending Questions (1)
How many negotiation are held online?

The paper does not provide information on the exact number of negotiations held online.