• Journal of Korean Library and Information Science Society
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• v.39 no.3
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• pp.299-320
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• 2008

This study examined the historical background and structural characteristics of the UDC(Universal Decimal Classification) standard edition which has been created from the entire content of the Master Reference File database. We made a comparison of the structural characteristics between UDC standard edition and Korean abridged edition. UDC is a hybrid of two kinds of documentary classification scheme, that is enumerative and analytico-synthetic, and its structure reflects this feature. It is found that UDC standard edition extended the universality and synthetic method using its auxiliary tables compare to Korean abridged edition.

• Journal of KIISE:Software and Applications
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• v.30 no.7_8
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• pp.672-680
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• 2003

A shopbot is a software agent whose goal is to maximize buyer´s satisfaction through automatically gathering the price and quality information of goods as well as the services from on-line sellers. In the response to shopbots´ activities, sellers on the Internet need the agents called pricebots that can help them maximize their own profits. In this paper we adopts Q-learning, one of the model-free reinforcement learning methods as a price-setting algorithm of pricebots. A Q-learned agent increases profitability and eliminates the cyclic price wars when compared with the agents using the myoptimal (myopically optimal) pricing strategy Q-teaming needs to select a sequence of state-action fairs for the convergence of Q-teaming. When the uniform random method in selecting state-action pairs is used, the number of accesses to the Q-tables to obtain the optimal Q-values is quite large. Therefore, it is not appropriate for universal on-line learning in a real world environment. This phenomenon occurs because the uniform random selection reflects the uncertainty of exploitation for the optimal policy. In this paper, we propose a Mixed Nonstationary Policy (MNP), which consists of both the auxiliary Markov process and the original Markov process. MNP tries to keep balance of exploration and exploitation in reinforcement learning. Our experiment results show that the Q-learning agent using MNP converges to the optimal Q-values about 2.6 time faster than the uniform random selection on the average.

• Journal of the Korean Society for information Management
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• v.35 no.4
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• pp.37-50
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• 2018

The purpose of this study is to analyze the first edition of Brown's Subject Classification and to understand the implications of today's library classification. For this purpose, the first edition of the Subject Classification published in 1906 was analyzed. The analysis results are divided into three main areas. First, SC is divided into eleven main classes and each class is subdivided into enumerated subdivisions from 000 to 999. Second, As a method of synthesizing the classification numbers, there were three methods of synthesis. There was a flexibility to insert a new classification number at the appropriate location when a new topic that does not appear in the main table appeared. Implications for classification studies can be divided into four main categories. First, SC proposed a method of classification number synthesis for composite topics, which is an innovative method that was not available in previous library classification. Second, the subject matter was explained in various aspects through the operation of auxiliary tables supporting the facets. Third, it is possible to easily insert a new topic into the classification system by using the SC that can have a flexible classification system for each library, or to use a short classification number according to the size of the library collection. Fourth, it provided a directory that can be considered as access points for digital materials.