Earticle

초록

영어

Efficient evidential reasoning is an important issue in the development of advanced knowledge based systems. Efficiency is closely related to the design of problems solving methods adopted in the system. The explicit modeling of problem-solving structures is suggested for efficient and effective reasoning. It is pointed out that the problem-solving method framework is often too coarse-grained and too abstract to specify the detailed design and implementation of a reasoning system. Therefore, as a key step in developing a new reasoning scheme based on properties of the problem, the problem-solving method framework is expanded by introducing finer grained problem-solving primitives and defining an overall control structure in terms of these primitives. Once the individual components of the control structure are defined in terms of problem solving primitives, the overall control algorithm for the reasoning system can be represented in terms of a finite state diagram.

목차

Abstract
1. Introduction
2. Efficient Problem Solving
2.1 Problem Solving Methods
2.2 Task Theory
2.3 PSMs and Tasks
3. Machine Learning Approach for Efficient Problem Solving
3.1 Learning by Chunking
3.2 Macro-Operators
3.3 Explanation Based Learning
4. Problem Solving Primitives for Efficient Reasoning
5. State Diagram for Problem Solving
6. Conclusion
References

키워드

저자

• Gye Sung Lee [ Dept. of Software, Dankook University, Korea ] Corresponding author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Internet, Broadcasting and Communication

• 간기

  계간

• pISSN

  2288-4920

• eISSN

  2288-4939

• 수록기간

  2009~2025

• 십진분류

  KDC 326 DDC 380