• Proceedings of the Korea Inteligent Information System Society Conference
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• 2000.04a
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• pp.207-216
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• 2000

Due to recent growing interest in autonomous software agents and their potential application in areas such as electronic commerce, the autonomous negotiation become more important. Evidence from both theoretical analysis and observations of human interactions suggests that if decision makers have prior information on opponents and furthermore learn the behaviors of other agents from interaction, the overall payoff would increase. We propose a new methodology for a strategy finding process using data mining in autonomous negotiation system ; ANSIA (Autonomous Negotiation System using Intelligent Agent). ANSIA is a strategy based negotiation system. The framework of ANSIA is composed of following component layers : 1) search agent layer, 2) data mining agent layer and 3) negotiation agent layer. In the data mining agent layer, that plays a key role as a system engine, extracts strategy from the historic negotiation is extracted by competitive learning in neural network. In negotiation agent layer, we propose the autonomous negotiation process model that enables to estimate the strategy of opponent and achieve interactive settlement of negotiation. ANISIA is motivated by providing a computational framework for negotiation and by defining a strategy finding model with an autonomous negotiation process.

• Industrial Engineering and Management Systems
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• v.7 no.3
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• pp.266-287
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• 2008

To manage collaborative negotiation in software system design, we have built a socio-technical argument-based negotiation management approach by integrating a Socio-technical Co-construction Process (STCP) with an Argument-based Negotiation Process (ABNP). This paper reviews relevant research work and presents each step of this approach. The STCP provides rich contextual information of technical decisions and social interactions in a system design process. The ABNP provides STCP with a negotiation management and conflict resolution strategy by guiding software engineers to generate, exchange and evaluate their argument claims in negotiation activities. In addition, this paper describes a prototype system which implements this new approach using the advanced Web-based software technologies with the goal of demonstrating how to systematically enhance the negotiation management capabilities in a dynamic socio-technical framework.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.29 no.2
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• pp.20-30
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• 2006

Due to recent growing interest in autonomous software agents and their potential application in areas such as electronic commerce, the autonomous negotiation become more important. Evidence from both theoretical analysis and observations of human interactions suggests that if decision makers have prior information on opponents and furthermore learn the behaviors of other agents from interaction, the overall payoff would increase. We propose a new methodology for a strategy finding process using data mining in autonomous negotiation system; ANSIA(Autonomous Negotiation System using Intelligent Agent). ANSIA is a strategy based negotiation system. The framework of ANSIA consists of three component layers; 1) search agent layer, 2) data mining agent layer and 3) negotiation agent layer. ANSIA is motivated by providing a computational framework for negotiation and by defining a strategy finding model with an autonomous negotiation process.

• IE interfaces
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• v.18 no.1
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• pp.44-51
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• 2005

The rapid expansion of the Internet-related technology has changed the current commercial transaction process. In the physical commercial transaction, most deals are accomplished through the negotiation process except a fixed price system. Thus, the negotiation is essential to conclude the business transactions. Especially, under the e-commerce environment, an automated negotiation system is necessary to respond quickly and flexibly to the diverse environmental changes and also to perform negotiations consistently and effectively. To this end, we develop an automated negotiation system using multi-agent technology. This new system includes such functions as creating negotiation proposals automatically, evaluating the counterpart’ proposals, and preparing counter offers.

• Journal of Information Technology Applications and Management
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• v.16 no.1
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• pp.1-20
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• 2009

Negotiation is a process of reaching an agreement on the terms of a transaction. such as price, quantity, for two or more parties. Negotiation tries to maximize the benefits for all parties concerned. instead of using human-based negotiation. the e-commerce environment provides such an environment as adopting automated negotiation. Thus. choosing agent technology is appropriate for an automatic electronic negotiation platform. since autonomous software agents strive for the best deal on behalf of the human participants. Negotiation agents need a clear-cut definition of negotiation models or strategies. In reality, most bargaining systems embody nearly one negotiation model. In this article. we present a mobile agent negotiation system with reusable negotiation strategies that allows agents to dynamically embody a user's favorite negotiation strategy which can be preinstalled as a component in the system. We develop a prototype system, which is fully implemented in compliance with FIPA specifications, and then. describe the benefits of using the system.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.3
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• pp.342-348
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• 2004

As the advent of the Internet, B2B negotiation process on the Internet has been given attention from both researchers and practitioners. However, literature still shows that only structured conditions have been explicitly considered, despite the fact that unstructured conditions should be rendered as well. In this sense, this paper proposes a new negotiation support mechanism to incorporate causal relationships between structured and unstructured conditions in the process of B2B negotiation. Fuzzy cognitive map was used as a main source of causal knowledge as well causal inference engine. A prototype named CAKES-NEGO was developed to perform experiments with an illustrative example. Results revealed the robustness of our proposed negotiation support mechanism.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.05a
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• pp.467-470
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• 2004

MANPro-based control is a novel control paradigm aimed at intelligent manufacturing systems on the basis of mobile agent-based negotiation process (MANPro). MANPro is a negotiation mechanism based on the agent-based control architecture and, especially, it adapts a mobile agent system called N-agent for the negotiation process. N-agent travels around the network of distributed manufacturing systems to acquire information, and it makes a decision for system control according to the obtained information. MANPro includes communication architecture and information architecture for intelligent shop floor control. MANPro also considers the following issues: (1) negotiation mechanism, (2) single-agent internal strategic policies, and (3) information model. Communication architecture concerns the first issue of the negotiation mechanism. It provides information exchanging mechanism with functional modules. In specific, N-agent is equipped with an intelligent reasoning engine with a built-in knowledge base. This reasoning engine is closely related to the single-agent internal strategic policies of the second issue. Finally, ontology-based information architecture addresses information models and provides a framework for information modeling on negotiation. In this paper, these three issues are addressed in detail and a framework of MANPro-based control is also proposed.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2000.04a
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• pp.197-206
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• 2000

We proposed a negotiation model for the n:n negotiation process in the deal. The proposed model was based on the geometrical metaphor concept. We suggested also a negotiation state space using the metaphors where deals can be done conveniently and effectively. We analyzed and showed the usefulness of the model for the both parties concerned in the deal.

• Proceedings of the Korea Association of Information Systems Conference
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• 2003.05a
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• pp.1-20
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• 2003

In this research, we developed a Multi-Agent based Negotiation Support System to be able to increase the competitive power of a company in dynamic environment and correspond to various orders of customers by diffusion of electronic commerce. The system uses the agent technology that is being embossed as new paradigm in dynamic environment and flexible system framework. The multi-agent technology is used to solve these problem through cooperation of agent. The system consists of six sub agents: mediator, manufacturability analysis agent, process planning agent, scheduling agent, selection agent, negotiation-strategy building agent. In this paper, the proposed Multi-Agent based Negotiation Support System takes aim at the automation of transaction process from ordering to manufacturing plan through the automation of negotiation that is the most important in order-taking transaction.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.6
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• pp.2050-2077
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• 2015

One of the major issues in emerging cloud management system needs the efficient service level agreement negotiation framework, with an optimal negotiation strategy. Most researchers focus mainly on the atomic service negotiation model, with the assistance of the Agent Controller in the broker part to reduce the total negotiation time, and communication overhead to some extent. This research focuses mainly on composite service negotiation, to further minimize both the total negotiation time and communication overhead through the pre-request optimization of broker strategy. The main objective of this research work is to introduce an Automated Dynamic Service Level Agreement Negotiation Framework (ADSLANF), which consists of an Intelligent Third-party Broker for composite service negotiation between the consumer and the service provider. A broker consists of an Intelligent Third-party Broker Agent, Agent Controller and Additional Agent Controller for managing and controlling its negotiation strategy. The Intelligent third-party broker agent manages the composite service by assigning its atomic services to multiple Agent Controllers. Using the Additional Agent Controllers, the Agent Controllers manage the concurrent negotiation with multiple service providers. In this process, the total negotiation time value is reduced partially. Further, the negotiation strategy is optimized in two stages, viz., Classified Similarity Matching (CSM) approach, and the Truncated Negotiation Group Gale Shapely Stable Matching (TNGGSSM) approach, to minimize the communication overhead.