Journal of Intelligence and Information Systems,
Vol. 9, No. 3, December 2003
Multi-Agent based Negotiation Support Systems for Order based Manufacturer
Hyung Rim Choi, Hyun soo Kim, Young Jae Park, Byung Joo Park, and Yong Sung Park
Vol. 9, No. 3, Page: 1 ~ 21
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 problems 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.
Customer Relationship Management Techniques Based on Dynamic Customer Analysis Utilizing Data Mining
Sung Ho Ha, and Jae Shin Yi
Vol. 9, No. 3, Page: 23 ~ 47
Keywords : Electronic Commerce, Business-to-Consumer, Customer Relationship Management, Marketing Strategy, Agent Model
Traditional studies for customer relationship management (CRM) generally focus on static CRM in a specific time frame. The static CRM and customer behavior knowledge derived could help marketers to redirect marketing resources fur profit gain at that given point in time. However, as time goes, the static knowledge becomes obsolete. Therefore, application of CRM to an online retailer should be done dynamically in time. Customer-based analysis should observe the past purchase behavior of customers to understand their current and likely future purchase patterns in consumer markets, and to divide a market into distinct subsets of customers, any of which may conceivably be selected as a market target to be reached with a distinct marketing mix. Though the concept of buying-behavior-based CRM was advanced several decades ago, virtually little application of the dynamic CRM has been reported to date. In this paper, we propose a dynamic CRM model utilizing data mining and a Monitoring Agent System (MAS) to extract longitudinal knowledge from the customer data and to analyze customer behavior patterns over time for the Internet retailer. The proposed model includes an extensive analysis about a customer career path that observes behaviors of segment shifts of each customer: prediction of customer careers, identification of dominant career paths that most customers show and their managerial implications, and about the evolution of customer segments over time. furthermore, we show that dynamic CRM could be useful for solving several managerial problems which any retailers may face.
A Tool for Implementation of Expert System with Knowledge Management System
Euy-hyun Suh
Vol. 9, No. 3, Page: 49 ~ 63
This paper proposes and implements a tool for the development of efficient and reliable expert system. In the expert system the inference is executed, based on the knowledges stored in the knowledge base of specific domain. To acquire the reliable results of inference, the expert system requires the facilities which can access the various kinds of knowledge and maintain the consistency and accuracy of knowledge. In this context this paper implemented the knowledge management system which maintains the consistency and accuracy of knowledge, adding selectively the knowledges without error to the knowledge base by verifying their error before the knowledges are added to the knowledge base. At the same time this paper made the expert system call and use the procedural knowledge and the declarative knowledge in the data base so that it might use the various kinds of knowledge in the process of inference.
The Identifier Recognition from Shipping Container Image by Using Contour Tracking and Self-Generation Supervised Learning Algorithm Based on Enhanced ART1
Kwang-baek Kim
Vol. 9, No. 3, Page: 65 ~ 79
Keywords : Canny Edge Mask, Contour Tracking Algorithm, Self-Generation Supervised Loaming Algorithm
In general, the extraction and recognition of identifier is very hard work, because the scale or location of identifier is not fixed-form. And, because the provided image is contained by camera, it has some noises. In this paper, we propose methods for automatic detecting edge using canny edge mask. After detecting edges, we extract regions of identifier by detected edge information's. In regions of identifier, we extract each identifier using contour tracking algorithm. The self-generation supervised learning algorithm is proposed for recognizing them, which has the algorithm of combining the enhanced ART1 and the supervised teaming method. The proposed method has applied to the container images. The extraction rate of identifier obtained by using contour tracking algorithm showed better results than that from the histogram method. Furthermore, the recognition rate of the self-generation supervised teaming method based on enhanced ART1 was improved much more than that of the self-generation supervised learning method based conventional ART1.
Digital Watermarking using ART2 Algorithm
Cheol-Ki Kim, and Kwang-baek Kim
Vol. 9, No. 3, Page: 81 ~ 97
Keywords : wavelet, signal processing, neural network, data hiding
In this paper, we suggest a method of robust watermarking for protection of multimedia data using the wavelet transform and artificial neural network. for the purpose of implementation, we decompose a original image using wavelet transform at level 3. After we classify transformed coefficients of other subbands using neural network except fur the lowest subband LL, we apply a calculated threshold about chosen cluster as the biggest. We used binary logo watermarks to make sure that it is true or not on behalf of the Gaussian Random Vector. Besides, we tested a method of dual watermark insertion and extraction. For the purpose of implementation, we decompose a original image using wavelet transform at level 3. After we classify transformed coefficients of other subbands using neural network except for the lowest subband LL, we apply a above mentioned watermark insert method. In the experimental results, we found that it has a good quality and robust about many attacks.
Knowledge-Based Approach for an Object-Oriented Spatial Database System
Yang Hee Kim
Vol. 9, No. 3, Page: 99 ~ 115
Keywords : object-oriented spatial databases, knowledge-based approach, object-oriented data model, spatial object-oriented query language
In this paper, we present a knowledge-based object-oriented spatial database system called KOBOS. A knowledge-based approach is introduced to the object-oriented spatial database system for data modeling and approximate query answering. For handling the structure of spatial objects and the approximate spatial operators, we propose three levels of object-oriented data model: (1) a spatial shape model; (2) a spatial object model; (3) an internal description model. We use spatial type abstraction hierarchies(STAHs) to provide the range of the approximate spatial operators. We then propose SOQL, a spatial object-oriented query language. SOQL provides an integrated mechanism for the graphical display of spatial objects and the retrieval of spatial and aspatial objects. To support an efficient hybrid query evaluation, we use the top-down spatial query processing method.
Collaborative Filtering System using Self-Organizing Map for Web Personalization
Boo-Sik Kang
Vol. 9, No. 3, Page: 117 ~ 135
Keywords : Collaborative Filtering, Self-Organizing Map, Web Personalization
This study is to propose a procedure solving scale problem of traditional collaborative filtering (CF) approach. The CF approach generally uses some similarity measures like correlation coefficient. So, as the user of the Website increases, the complexity of computation increases exponentially. To solve the scale problem, this study suggests a clustering model-based approach using Self-Organizing Map (SOM) and RFM (Recency, Frequency, Momentary) method. SOM clusters users into some user groups. The preference score of each item in a group is computed using RFM method. The items are sorted and stored in their preference score order. If an active user logins in the system, SOM determines a user group according to the user's characteristics. And the system recommends items to the user using the stored information for the group. If the user evaluates the recommended items, the system determines whether it will be updated or not. Experimental results applied to MovieLens dataset show that the proposed method outperforms than the traditional CF method comparatively in the recommendation performance and the computation complexity,
Ad Planning Model by Comparison Challenge Approach in the e-Marketplace
Jae-Kyu Lee, and Jae-Won Lee
Vol. 9, No. 3, Page: 137 ~ 153
Keywords : Comparison Challenge, Comparison Shopping, Internet Advertising, Ad Planning, Optimization
Comparison shopping is the most popular functionality in the e-Marketplace. Most of their revenue has been generated kent the Internet advertisement, but the ad earning was declined as the ad costing per action method widespread. Seller less familiar to the customer shrinks from chances for advertising and exposing their products. So, we need an efficient methodology subject to the seller's ad budget and other constraints, and it also has to increase comparison broker's earning in the e-Marketplace. Our research proposed and developed an ad planning methodology using comparison challenge approach which can be applied by 3?party comparison brokers. Comparison challenge planning is organized with challenge policy of competitor level, product level and specification level. With that policies and basic challenge propositions, we measure the quantified value of functional distance between the specifications of my product and competitor's product. My product challenges the comparison using the comparative ad format to the similar but inferior competitor's product based on quantified valuation. Comparison challenge planning system has two phases of comparative value generation and optimization. We developed a prototype system and applied it to the desktop PC market of five major manufacturers. Our performance was emphasized by comparing to other comparative ad methods such as random display method and minimum distance method..
The Negotiation Model of Negotiation Agents for m-Commerce
JinGuk Jung, SoonGeun Lee, and GeunSik Jo
Vol. 9, No. 3, Page: 155 ~ 175
Keywords : CSP, m-Commerce, Negotiation Agent
In context of e-commerce, negotiation is a procedure to help negotiate between buyer and seller by adjusting their negotiation issues such as price and in terms of payment. We used intelligent agent and mobile device to promote new framework of e-commerce. Moreover, this framework can help buyers and sellers to carry their commercial transactions effectively. In regard to that issue, we need to carry out the research of negotiation agent that can be used in e-commerce fields. In this paper, we modeled the negotiation using CSP for the performance of agent in m-commerce environment. Furthermore we implemented interface for mobile device to extract buyer's requirement and preference easily Besides that we used utility function to make a decision for various evaluation functions and suggestions that are used for evaluation of negotiation issues. A difficulty of generating offer is dependent on the number of negotiation issues and the range of the values. Therefore, if any offer has a number of negotiation issues and the range of values are wide, the search space will be exponentially expanded. There have been many studies fur solving this problem, we applied those techniques to improve the agent's ability of negotiation. For example, a contract can be accomplished by exchanging seller and buyer's offer that is generated by agent to adjust the requisite profit for each party. Finally, we show the improvement of satisfaction as the negotiation is processed.
Development of a Personalized Recommendation Procedure Based on Data Mining Techniques for Internet Shopping Malls
Jae-Kyeong Kim, Do-Hyun Ahn, and Yoon-Ho Cho
Vol. 9, No. 3, Page: 177 ~ 191
Keywords : Product recommendation, Web usage mining, Clustering, Personalization
Recommender systems are a personalized information filtering technology to help customers find the products they would like to purchase. Collaborative filtering is the most successful recommendation technology. Web usage mining and clustering analysis are widely used in the recommendation field. In this paper, we propose several hybrid collaborative filtering-based recommender procedures to address the effect of web usage mining and cluster analysis. Through the experiment with real e-commerce data, it is found that collaborative filtering using web log data can perform recommendation tasks effectively, but using cluster analysis can perform efficiently.

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