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.

Intelligent Tutoring Systems (ITS) represents a wide class of computer based tutoring systems, designed with an extensive use of the technology of modern Artificial Intelligence. Successful applications of various expert systems and other knowledge based systems of AI gave rise to a new wave of interests to ITS. Yet, many authors conclude that practically valuable achievements of ITS are rather modest despite the relatively long history of attempts to use knowledge based systems for tutoring. It is advocated in this paper that some basic obstacles for designing really successful ITS are due to the lack of well understood and sound models of the education process. The paper proposes to overcome these problems by borrowing the required models from AI and adjacent fields. In particular, the concept of Learning Levels from AI might be very useful both for giving a valuable retrospective analysis of computer based tutoring and for suggestion of some perspective directions in the field of ITS.

This study is intended to investigate a method to diagnose the student model in the domain of procedural problem solving. In this domain, the goal of an instruction should be to understand the processes of solving given problems, and to understand the reasons why problems can be solved by using sertain knowledge; the acquisition of problem solving skills might not be the intrinsic instructional goals. The tutoring systems in this domain must understand the effect of each problem solving operators, as well as when to implement these operators in order to effectively solve given problems. We have been studying and developing a system which deals with student modelling in the domain of procedural problem solving. We believe that the two types of knowledge should be clearly defined for the diagnosing tasks; effective knowledge (EK) and principle knowledge (PK). The former is the knowledge which is explicitly applied by students throughout problem solving processes, and the latter is the one which gives the justifications of the EK. We have developed a student model diagnosing system which infers students' knowledge structure pertaining to PK, based on the precedently manipulated student model about EK. This student model diagnosing method requires knowledge which argues the relationship between the PK and the EK. This knowledge plays the very important role in our system, and it's hard to describe such knowledge properly by hand. In this paper, we provide a student model diagnosing system which has the knowledge acquiring function to learn the relationship between EK and PK. The system acquires this knowledge through its own problem solving experience. Based on the student model and the acquired relational knowledge, the system can give students proper instructions about construction of EK with explanations in terms of PK. The system has been partly implemented with CESP language on a UNIX workstation.

This paper describes the concepts and methodologies of the INTELLITUTOR system which is an integrated intelligent programming environment for learning programming. INTELLITUTOR attempts to work as a human programming tutor to guide a user, i.e., a student, in writing a computer program, to detect logical errors within it, and to make advices not only for fixing them but also for letting him notice his misunderstandings. The system consists of three major modules, i.e., GUIDE, ALPUS and TUTOR. GUIDE is a guided editor for easy coding, ALPUS is an algorithm-based program understander, and TUTOR is an embedded-intelligent tutoring system for programming education. The ALPUS system can infer user's intentions from buggy codes in addition to detecting logical errors by means of knowledge-based reasoning. ALPUS uses four kinds of programming knowledge: 1) knowledge on algorithms, 2) Knowledge on programming techniques, 3) Knowledge on a programming language, and 4) Knowledge on logical errors. These knowledge are organized in a hierarchical procedure graph (HPG) as a multi-use knowledge base. The knowledge on logical errors was obtained by means of cognitive experiments. The student model is built by means of the results of ALPUS and interactions between a student and the system. Teaching is done based on the student model. Because the ITS subsystem, i.e., TUTOR, is embedded within the intelligent programming environment interactions for creating the student model could be minimized. Although the current system deals with the PASCAL language, most of the knowledge is applicable to those of procedure-oriented programming languages. The INTELLITUTOR system was implemented in the frame-based knowledge engineering environment ZERO and working on a UNIX workstation for system evaluation.

A simulation-based ITS (Intelligent tutoring system), SRIM, has been developed for the purpose of providing individualized learning to students of PID control. We first indicate that the following two steps will be a burden to the student during personal use of simulators: 1) Selection of operational goals and 2) Interpretation of the simulation results. In order to reduce the burden of students in learning with a simulator, SRIM guides the learning process by providing local goals for PID controller tuning and by giving messages. Two tutoring strategies: i.e. the exercise style strategy and the illustrating style strategy, are employed in SRIM. In the exercise style strategy, a local goal for tuning a PID controller is first given to the student. A local goal is defined as one which can be satisfied by a single operation step such as Decrease the off-set." The student selects his operation and executes the simulation. By observing the simulation, the student understands whether his operation was a success or a failure. The illustrating style strategy is invoked to repair the student's erroneous knowledge when a contradiction is detected in the student model or a wrong operation is selected repeatedly. The architecture of ITS is employed to perform the local goal selection and the tutoring strategy switching, in a natural, well timed manner. The performance of SRIM was evaluated for the purpose of demonstrating the effectiveness of the teaching strategy. The evaluation experiment was carried out in the following steps: 1) Pre-test, 2) Learning and 3) Post-test. The teaching effect of SRIM was compared with other learning methods such as simple use of simulators or a textbook from the results of the pre-test and the post-test. The results showed that SRIM is effective in providing individualized learning with simulators.

This paper presents an experimental environment of an intelligent tutoring system called EXPITS. In this environment, users learn functions and the structure of the intelligent tutoring system and characteristics of knowledge processing. EXPITS provides facilities for investigating internal processes and internal states of the intelligent tutoring system. These facilities include visualization tools and controllers of internal processes. Because the internal states and behavior of ITS depend on student's understanding states, one cannot get total understanding of ITS without information about student's knowledge states. To solve this problem, we introduce a pseudo student which simulates a human student in order to visualize explicitly all information which affects ITS behavior. Target users of EXPITS are school teachers, who are users of intelligent tutoring systems, university students who are studying artificial intelligence and postgraduate students who are specially studying intelligent tutoring systems. We have designed EXPITS to achieve different learning objectives for these three kinds of users. The learning objective for school teachers is to understand the differnce between intelligent tutoring systems and traditional CAI systems. University students are expected to understand characteristics of knowledge processing and rule based systems. Lastly, EXPITS provides postgraduate students who are studying intelligent tutoring systems with a test bed for examining ability and efficiency of the system in different configurations by changing parameters and by replacing constituents of the system. To achieve these purposes, EXPITS has experimental facilities for the following four themes; relationship between the domain knowledge representation method and teaching activities, the selection method of teaching paradigms, relationship between problem solving processes and teaching activities, and student modeling.