Katsuhiko Tsujino

Supporting mutual understanding in collaborative design project

In collaborative design, participants usually have different backgrounds and standpoints. Due to these differences, it is hard for participants to mutually understand intentions, and they must make a lot of effort to reach a mutual agreement on the design. Thus, the efficiency of the design processes is decreased. We propose a framework to support participants in mutual understanding by grasping and showing the differences among their intentions in a collaboration. A system based on the proposed framework has the following characteristics: it facilitates mutual understanding among participants by presuming their intentions and by communicating them in an understandable form, it visualizes the differences in concepts among the participants, and it progresses the collaboration in an interactive way.

A knowledge acquisition inductive system driven by empirical interpretation of derived results

This paper describes the design philosophy of and current issues concerning a knowledge acquisition system namedkaiser. This system is an intelligent workbench for construction of knowledge bases for classification tasks by domain experts themselves. It first learns classification knowledge inductively from the examples given by a human expert, then analyzes the result and process based on abstract domain knowledge which is also given by the expert. Based on this analysis, it asks sophisticated questions for acquiring new knowledge. The queries stimulate the human expert and help him to revise the learned results, control the learning process and prepare new examples and domain knowledge. Viewed from an AI aspect, it aims at integrating similarity-based inductive learning and explanation-based deductive reasoning by guiding inductive inference with theoretical and/or heuristic knowledge about the domain. This interactive induce-evaluate-ask cycle produces a rational interview which promotes incremental acquisition of domain knowledge as well as efficient induction of operational and reasonable knowledge proved by the domain knowledge.