Browsing by Author "Chang, LP"

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A parallelized indexing method for large-scale case-based reasoning
(2002-08-01)
Chen, WC
;
Tseng, SS
;
Chang, LP
;
Hong, TP
;
Jiang, MF
Case-based reasoning (CBR) is a problem-solving methodology commonly seen in artificial intelligence. It can correctly take advantage of the situations and methods in former cases to find out suitable solutions for new problems. CBR must accurately retrieve similar prior cases for getting a good performance. In the past, many researchers proposed useful technologies to handle this problem. However, the performance of retrieving similar cases may be greatly influenced by the number of cases. In this paper, the performance issue of large-scale CBR is discussed and a parallelized indexing architecture is then proposed for efficiently retrieving similar cases in large-scale CBR. Several algorithms for implementing the proposed architecture are also described. Some experiments are made and the results show the efficiency of the proposed method. (C) 2002 Elsevier Science Ltd. All rights reserved.
Business agent
(2002)
Meng, IH
;
Yang, WP
;
Chen, WC
;
Chang, LP
;
資訊工程學系
The Internet and World Wide Web represent an increasingly important channel for retail commerce as well as business transactions. However, there are almost 5 billion pages or sites on the Internet and WWW. There lacks an integrated mediator business agent which is around the internet to connect between suppliers and users. Therefore, an intelligent business broking agent between supply and demand is needed for using and sharing the information efficiently and effectively. In this paper we proposed a new business agent architecture. The Business Spy Agent (BSA) captures the supply and demand information from e-commence sites automatically. Supply and Demand Analysis Mechanism (SDAM) uses NLP technologies to extract product and trading information. Domain ontology is used to classify between supply and demand and to divide them into subsets. A benefit model is proposed to handle the pairing between sub-supply and sub-demand. And a divide-and-conquer algorithm is proposed to handle the whole matching between supply and demand. In the negotiation process, the architecture uses NLP and template to produce negotiation text to handle negotiation through mail. Finally, Hidden Business Mining Mechanism (HBMM) adopts data mining technology to achieve hidden business mining. The architecture covers the four process of buying behavior including support and need identification, Product brokering, Merchant brokering, Negotiation.