For game design planning education, we researched step by step learning method from storyline setting to game content evaluation. In this process, we developed 'Content Generated Tree' educational model applying the segmentation, classification, and prediction process of decision tree theory. This model is divided into the trunk stage as a story setting, the node generation stage as a content branch, and the conformity assessment. In the node generation stage, there are 'Game Theme' stage for determining the overall direction of the game, 'Interest Element' stage for finding the unique joy of the development game, and 'Game Format' stage for setting the visualization direction. The learner creates several game content combinations through content branching, and evaluates each content combination value. The education model was applied to 19 teams, and the efficiency of the step by step learning process was confirmed.

This paper deals with the economic value analysis of meteorological forecasts for a hypothetical inventory decision-making situation in the pharmaceutical industry. The value of Asian dust (AD) forecasts is assessed in terms of the expected value of profits by using a decision tree, which is transformed from the specific payoff structure. The forecast user is assumed to determine the inventory level by considering base profit, inventory cost, and lost sales cost. We estimate the information value of AD forecasts by comparing the two cases of decision-making with or without the AD forecast. The proposed method is verified for the real data of AD forecasts and events in Seoul during the period 2004~2008. The results indicate that AD forecasts can provide the forecast users with benefits, which have various ranges of values according to the relative rate of inventory and lost sales cost.

Cost estimating is essential in decision-making for conducting project management on early design stage. The cost estimating method for each stage varies according to level of design detail. Therefore, in the cost estimating method for each stage, it must distinguish quantity items that can be directly measured from quantity items that should be predicted. The parametric estimating method is able to support cost planning for various design attributes as it is possible to set impact factors related to design features as parameters. This study suggests a prediction method for quantity information that is required to estimate the final cost during the early design stage. The case study suggests an predicting method for the steel (rebar) ratio of office buildings. The suggested parametric cost estimating model enables users to predict the steel (rebar) quantity for various design alternatives according to design features. During quantity predictions, IG(Information gain) measurements for the design attributes were analyzed, by setting the ratio of steel-rebar quantity(Ratio: ton/Concrete_㎥) as the dependent variable.

The basis of cyber trading has been sufficiently developed with innovative advancement of Internet Technology and the tendency of stock market investment has changed from long-term investment, which estimates the value of enterprises, to short-term investment, which focuses on getting short-term stock trading margin. Hence, this research shows a Short-term Stock Price Forecasting System on Learning Agent System using DTA(Decision Tree Algorithm) ; it collects real-time information of interest and favorite issues using Agent Technology through the Internet, and forms a decision tree, and creates a Rule-Base Database. Through this procedure the Short-term Stock Price Forecasting System provides customers with the prediction of the fluctuation of stock prices for each issue in near future and a point of sales and purchases. A Human being has the limitation of analytic ability and so through taking a look into and analyzing the fluctuation of stock prices, the Agent enables man to trace out the external factors of fluctuation of stock market on real-time. Therefore, we can check out the ups and downs of several issues at the same time and figure out the relationship and interrelation among many issues using the Agent. The SPFA (Stock Price Forecasting System) has such basic four phases as Data Collection, Data Processing, Learning, and Forecasting and Feedback.

While increasing demand of the service for the disabled and the elderly people, assistive technologies have been developed rapidly. The natural signal of human such as voice or gesture has been applied to the system for assisting the disabled and the elderly people. As an example of such kind of human robot interface, the Soft Remote Control System has been developed by HWRS-ERC in KAIST[1]. This system is a vision-based hand gesture recognition system for controlling home appliances such as television, lamp and curtain. One of the most important technologies of the system is the hand gesture recognition algorithm. The frequently occurred problems which lower the recognition rate of hand gesture are inter-person variation and intra-person variation. Intra-person variation can be handled by inducing fuzzy concept. In this paper, we propose multivariate fuzzy decision tree(MFDT) learning and classification algorithm for hand motion recognition. To recognize hand gesture of a new user, the most proper recognition model among several well trained models is selected using model selection algorithm and incrementally adapted to the user’s hand gesture. For the general performance of MFDT as a classifier, we show classification rate using the benchmark data of the UCI repository. For the performance of hand gesture recognition, we tested using hand gesture data which is collected from 10 people for 15 days. The experimental results show that the classification and user adaptation performance of proposed algorithm is better than general fuzzy decision tree.