• The KIPS Transactions:PartD
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• v.11D no.5
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• pp.1205-1212
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• 2004

As electronic commerce systems have been widely used, the necessity of adaptive e-commerce agent systems has been increased. These kinds of adaptive e-commerce agents can monitor customer's behaviors and cluster thou in similar categories, and include user's preference from each category. In order to implement our adaptive e-commerce agent system, in this paper, we propose an adaptive e-commerce agent systems consider customer's information of interest and goodwill ratio about preference goods. Proposed system build user's profile more accurately to get adaptability for user's behavior of buying and provide useful product information without inefficient searching based on such user's profile. The proposed system composed with three parts , Monitor Agent which grasps user's intension using monitoring, similarity reference Agent which refers to similar group of behavior pattern after teamed behavior pattern of user, Interest Analyzing Agent which personalized behavior DB as a change of user's behavior.

• The KIPS Transactions:PartB
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• v.9B no.6
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• pp.775-782
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• 2002

As electronic commerce systems have been widely used, the necessity of adaptive e-commerce agent systems has been increased. These kinds of agents can monitor customer's purchasing behaviors, clutter them in similar categories, and induce customer's preference from each category. In order to implement our adaptive e-commerce agent system, we focus on following 3 components-the monitor agent which can monitor customer's browsing/purchasing data and abstract them, the conceptual cluster agent which cluster customer's abstract data, and the customer profile agent which generate profile from cluster, In order to infer more accurate customer's preference, we propose a 2 layered structure consisting of conceptual cluster and inductive profile generator. Many systems have been suffered from errors in deriving user profiles by using a single structure. However, our proposed 2 layered structure enables us to improve the qualify of user profile by clustering user purchasing behavior in advance. This approach enables us to build more user adaptive e-commerce system according to user purchasing behavior.

• Journal of KIISE:Software and Applications
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• v.32 no.11
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• pp.1099-1110
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• 2005

As auctions become popular, developing good agent bidding strategies has been an important focus in agent-based electronic commerce research. Especially for the continuous double auctions where no single dominant strategy is known, the agent bidding strategy has practical significance. This paper introduces an adaptive agent strategy for the countinuous double auction. The central idea is to let the agent figure out at run time when the sophisticated strategy (called the p-strategy) is beneficial and when a simpler strategy is better. Balance between exploration and exploitation is achieved by using a heuristic exploration function that trades off the expected profits and the number of tries of each strategy. We have experimentally evaluated the performance of the adaptive strategy in a wide variety of environments. The experiment results indicate that the adaptive strategy outperforms the plain p-strategy when the p-strategy performs poorly, while it performs similar to the p-strategy when the p-strategy dominates the other simple strategies.

• Proceedings of the Korean Information Science Society Conference
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• 2001.10b
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• pp.121-123
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• 2001

오늘날의 1세대 구매 에이전트는 상품의 전체 특징 변수(attribute)에 대해서보다는 주로 판매자들이 제시한 가격만 비교해서 구매 행위를 대행해 주고 있으며, 간혹 가격 이외의 변수에 대해 비교를 해주는 에이전트의 경우에도 협상(negotiation) 과정에서 전체 변수를 적절히 고려해주는 협상 모델은 찾아보기 힘들다. 따라서, 전자 상거래의 협상 모델(negotiation model)을 가격 변수뿐만 아니라 상품의 전체 변수로 확장시켜 주는 것이 절실히 요구되고 있다. 또한 수많은 전자상거래 업체가 나타남에 여러 도메인간 에이전트 이동 기술과 도메인의 상품 특성에 따른 협상을 진행하는 적응적 에이전트의 필요성이 요구된다. 본 논문에서는 유틸리티(utility)이론과 간결한 휴리스틱스(simple heuristics)에 바탕을 두어서 가격, 상품의 특성, 보장 기간, 서비스 정책 등에 대해서 협상을 벌이는 다중변수 에이전트 협상 프레임워크인 Pmart를 제시하고 이를 확장하여 다중 도메인에서 에이전트의 이동성을 보장하고, 각 도메인의 협상 특수 지식을 XML로 제공받아 적응성을 가지는 적응적 구매 에이전트를 제안한다.