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%.

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.