Thanks to recent improvements in road heating technology, traffic problems due to icy roads are decreasing. However, there has always been concern about the high operational and maintenance cost associated with road heating. One way to reduce the cost is to reduce the time when power is applied for preheating because it is often applied even when a road is not likely to be icy. The authors believe that, if it is possible to forecast accurately whether a road will become icy, unnecessary preheating can be greatly reduced. This paper presents an algorithm for forecasting physical road conditions. The algorithm divides the weather conditions that people perceive daily into 11 patterns. The comparison between the changes in road conditions as determined by our method and known changes in road conditions has shown a 12% increase over previous methods in forecasting accuracy.

This paper presents a method for tuning the structure of a causal network (CN) to evaluate a learner's profile for a learning assistance system that employs hierarchically structured learning material. The method uses as an initial CN structure causally related inter-node paths that explicitly define the learning material structure. Then, based on this initial structure other inter-node paths (sideway paths) not present in the initial CN structure are inferred by referring to the learner's database generated through the use of a learning assistance system. An evaluation using simulation indicates that the method has an inference probability of about 63% and an inference accuracy of about 30%.

One of the most important factors indicating the effectiveness of a word clustering method is how commonly it can be applied to different languages. This paper evaluates the applicability of a new word clustering method to the English and Japanese languages using word sets edited from technical summaries. The method employs an iterative clustering routine which increases the number of clustered words. Thus, evaluations are achieved as a function of the number of iterations of the clustering routine from the aspects (a) clustering characteristics determined from the number of clustered words, the number of clusters formed, etc., and (b) performance determined from the average clustering ratio and the average cluster uniformity. Consequently, the applicability of the method to English and Japanese is obtained through evaluations indicating similarities between them for both clustering characteristics and performance. It is also clarified that about fifty percent of the target words can be clustered in less than five iterations of the clustering routine.

In a conventional client-server system using the satellite communications, the responsibility of the system to the client user is considerably degraded by the long transmission time between the satellite and the ground terminal as well as the relatively low data transmission rate in comparison with the ground transmission line as the Ethernet. In this paper, a new client-server control, VEEC, is proposed to solve the problem. As a result of the experimental performance studies, it is clarified that the responsibility in the client is remarkably improved when the pre-fetching mechanism of VEEC works efficiently.

Although production systems are widely used in artificial intelligence (AI) applications, they are seen to have certain disadvantages in terms of their need for special purpose assistance software to build and execute their knowledge-bases (KB), and in the fact that they will not run on any operating system (platform dependency). Furthermore, for AI applications such as learning assistance systems, there is a strong requirement for a self-adaptive function enabling a flexible change in the service contents provided, according to the user. Against such a background, a Java based production system (JPS) featuring no requirement for special purpose assistance software and no platform dependency, is proposed. Furthermore, a new self-adaptive Java production system (A-JPS) is proposed to realize the "user adaptation" requirement mentioned above. Its key characteristic is the combination of JPS with a Causal-network (CN) for obtaining a "user profile". In addition, the execution time of the JPS was studied using several benchmark problems with the aim of comparing the effectiveness of different matching algorithms in their recognize-act cycles as well as comparing their performance to that of traditional procedural programs for different problem types. Moreover, the effectiveness of the user adaptation function of the A-JPS was studied for the case of a CN with a general DAG structure, using the experimental KB of a learning assistance system.