2. Intelligent Educational Systems: Although there have been a lot of philosophical discussion and implementation of intelligent educational systems, there is little in between the two. What we usually see are very abstract discussions and ad hoc implementations. These are mainly caused by the lack of knowledge engineering of educational systems. What we need for filling up this gap are well-designed common vocabulary and frameworks for educational systems. We also need to formalize intelligent educational tasks at the right level of abstraction. We will be able to find a nice solution to this problem in knowledge engineering in which ontology, especially ontology engineering is extensively discussed. In this project, we are trying to build task ontology for a veriety types of educational systems, such as, ITS, ILE, and CSCL.
3. Model-based Problem Solving: Model-based problem solving is one of the advanced research topics aiming at overcoming the brittleness of the current expert systems. Deep knowledge is fundamental knowledge of domains which could give the expert systems high flexibility and capability of problem solving. In this research project, we concentrate on the following three issues concerning diagnostic ES's: 1) Reusability of the knowledge and ease of its description, 2) Cognitive explanation based on causality and function, and 3) Applicability to real world systems with little ambiguity.
4. Heterogeneous Reasoning: Humans use selectively different kinds of knowledge in reasoning depending on each purpose. Moreover, they synthesize those consequences to obtain a better result. One of the representative forms of knowledge we are interested in is diagrams that are utilized in reasoning. We are going to seek the role of the diagrams in our reasoning, and explore the method of heterogeneous reasoning that can make effective use of diagrams as one kind of knowledge representation. Bisides, we are developing new reasoning frameworks of modeling and diagnosis based on diversed sources of input knowledge.
5. Qualitative, Analogical Reasoning and Knowledge Discovering: Much of human understanding is due to qualitative and analogical reasoning. We are exploring effective methods to solve new problem domain by use of these flexible reasoning techniques through observation of cognitive aspect of humans' problem solving. For instance, we are trying to establish new methods to derive, understand and discover behaviors of a physical systems based on the development of human mental models of qualitative cognition and analogical reasoning. Also, we are seeking a framework of reasoning mechanism to understand a given object and discover the associated knowledge through the investigation of our cognitive process at an elemenary law level.
6. Data Driven Rule Extraction and Concept Formation: Human's information processing capability is limited by the cognitive and physiological capacity, and it is quite difficult to deduce meaningful information that is embedded in huge amount of data. We are exploring inductive reasoning methods that are expressive enough and computationally efficient, and are suitable for data mining. We are trying to establish new approaches for data reconstruction and knowledge discovery in this project.
7. Knowledge Reformulation by Abstraction and Approximation: Successful abstraction and approximation make understanding easier and contributes to effective problem solving. We are exploring knowledge representation that captures different degree of abstraction and approximation, how to convert from one representation to other and how to make effective use of them in reasoning for problem solving. Frameworks for efficient reasonig in new problem domain will be provided through these studies.
Ability to work willingly with pragmatically motivated research groups.
Language -- English
Funding guaranteed up to three years, minimum of one year stay required
Salary: About 300,000 yen/month, no subsidy and no extras
Age: Below 35 years old
For those who are interested in, send CV, Web URL, representative papers, and three letters of reference, to the following three:
Riichiro Mizoguchi, Hiroshi Motoda and Mitsuru Ikeda
Division of Intelligent Systems Science,
The Institute of Scientific and Industrial Research,
Osaka University
8-1 Mihogaoka, Ibaraki, Osaka 567
Japan
E-mail miz@ei.sanken.osaka-u.ac.jp motoda@sanken.osaka-u.ac.jp
ikeda@ei.sanken.osaka-u.ac.jp
Phone: 81-6-879-8415/8416 81-6-879-8540
Fax : 81-6-879-2123 81-6-879-8544