We introduce an ontology for transportation systems demand forecasting and its implementation into a decision support system (DSS) generator. The term ontology, as we use it here, means a collection of building blocks necessary and sufficient to construct a skeleton of a specific DSS, that is a task ontology. The ontology is specified in constraint logic, which also ensures a good support for modularity.

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 current situations and future directions of intelligent CAI researches/development in Japan. Then necessity of intelligence in CAIs/Educational systems are thought over corresponding to the model of teaching and the cognitive model of human learning like the situated learning, knowledge construction and so on. Originally, the main aims of ITSs/ICAIs are to tealize the high level environment of individual teaching/learning. So it is the most important to incorporate the intellectual function of teaching into the system. Whatever kinds of teaching purposes ITSs have, they have the quite complex structure which consists of the domain knowledge base (Expert system), student model, the tutoring knowledge base, the powerful human interface, and sophisticated inference engine with plural functions by artificial intelligence technology. In this paper, the technological and educational points of view are discussed, surveyed and summarized based on intelligent teaching functions of ITSs/ICAIs. Moreover, the meaning of new paradigm from ITSs to ILE are mentioned under the new technology of networking and multi-media.

This paper is about describing the architecture of an Intelligent Tutoring System which can diagnose and give individual advice for the purpose of teaching the operational procedures and theorem-understanding in set-operations. From the technological point of artificial intelligence, a method of processing of mathematical equation for set-operational procedures and a dynamic construction of a student model based on condition induction are proposed. That is the architecture of ITS on teaching the algorithm for the operational procedures and modelling the structure of student's understanding in set-operations. The fundamental concepts consist of two kinds within the student model which are the state-memory of target achieving (MTGA) and the state-memory of problem-application (MPA). The system works cooperatively and adaptively monitoring MPA and MTGA, in order to realize more individualized tutoring corresponding with the state of the student's understanding. As the global teaching strategy, there is the selection-function of the problem matched with the level of a student's understanding pased on the method of condition-induction.

Recently, various systems based on agent model architecture have been developed. In these systems, 'agents' with their own goals and functions are embedded, and perform their own tasks through collaboration among them by communication to achieve a goal as the system requires. Using this agent model for the construction of educational systems, adaptive configuration of the system is achieved. The purpose of this study is to propose a methodology for the design of an educational system based on agent model architecture. This paper describes the configuration of the agent model and the communication language and protocol used to represent collaboration among the agents necessary for performing a cooperative task. Moreover, we explain how to organize these agents as an educational system. As a case to show the organization of agents, we discuss the configuration of an intelligent learning environment to support C shell programming in UNIX and explain the collaborative behavior of embedded agents.

Many studies and systems that incorporate elements such as “pleasure” and “fun” in the game to improve a learner's motivation have been developed in the field of learning environments. However, few are the studies of situations where many learners gather at a single computer and participate in a game-based learning environment (GBLE), and where the GBLE designs the learning process by controlling the interactions between learners such as competition, collaboration, and learning by teaching. Therefore, the purpose of this study is to propose a framework of educational control that induces and activates interaction between learners intentionally to create a learning opportunity that is based on the knowledge understanding model of each learner. In this paper, we explain the design philosophy and the framework of our GBLE called “Who becomes the king in the country of mathematics?” from a game viewpoint and describe the method of learning support control in the learning environment. In addition, we report the results of the learning experiment with our GBLE, which we carried out in a junior high school, and include some comments by a principal and a teacher. From the results of the experiment and some comments, we noticed that a game may play a significant role in weakening the learning relationship among students and creating new relationships in the world of the game. Furthermore, we discovered that learning support control of the GBLE has led to activation of the interaction between learners to some extent.