opening for post-doctoral firstname.lastname@example.org (Mitsuru IKEDA)
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Date: Mon, 13 May 1996 17:09:06 +0900
To: email@example.com, firstname.lastname@example.org
From: email@example.com (Mitsuru IKEDA)
Subject: opening for post-doctoral fellow
The Institute of Sicentific and Industrial Research
The Institute of Sicentific and Industrial Research of Osaka
University is looking for a post-doctoral fellow to work on one
of the following research subjects. This is an opportunity for a
suitably qualified and ambitious scientist to work closely with
Professors Riichiro Mizoguchi
(http://www.ei.sanken.osaka-u.ac.jp/~miz) and Hiroshi Motoda
(http://www.sanken.osaka-u.ac.jp/labs/hr/motopreg.html) in the
division of Intelligent System Science.
1. Ontology and Knowledge Reuse: Ontology plays an important role in
knowledge sharing and reuse. While ontology is originally defined as a
"systematic theory of existence" in philosophy, we define ontology as "a
system of concepts/vocabulary used as primitives for building artificial
systems" from knowledge base perspective. In contrast with the majority's
line of ontology research, we argue that designing ontologies for
knowledge reuse requires commitments to the problem solving context, in
general, and the task, in particular. As a consequence, we are
designing different types of interacting ontologies.
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.
PhD in Computer Science or related discipline.
Ability to work willingly with pragmatically motivated
Language -- English
Start from July, 1996
Funding guaranteed up to three years, minimum of one year stay
Salary: About 300,000 yen/month, no subsidy and no extras
Age: Below 35 years old
Occupation: Currently under no regular employment
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,
8-1 Mihogaoka, Ibaraki, Osaka 567
E-mail firstname.lastname@example.org email@example.com
Phone: 81-6-879-8415/8416 81-6-879-8540
Fax : 81-6-879-2123 81-6-879-8544
$B")(J567 $BBg:eI\0qLZ;TH~Jf%v5V(J 8-1
Tel: (06) 879 8416 Fax: (06) 879 2123