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FUNCTIONAL REPRESENTATION AND REASONING FOR REFLECTIVE SYSTEMS

 

作者: ELENI STROULIA,   ASHOKK. GOEL,  

 

期刊: Applied Artificial Intelligence  (Taylor Available online 1995)
卷期: Volume 9, issue 1  

页码: 101-124

 

ISSN:0883-9514

 

年代: 1995

 

DOI:10.1080/08839519508945470

 

出版商: Taylor & Francis Group

 

数据来源: Taylor

 

摘要:

Functional models have been extensively investigated in the context of several problemsolving tasks such as device diagnosis and design. In this paper, we view problem solvers themselves as devices, and use structure-behavior-function models to represent how they work. The model representing the functioning of a problem solver explicitly specifies how the knowledge and reasoning of the problem solver result in the achievement of its goals. Then, we employ these models for performance-driven reflective learning. We view performance-driven learning as the task of redesigning the knowledge and reasoning of the problem solver to improve its performance. We use the model of the problem solver to monitor its reasoning. Assign blame when it fails, and appropriately redesign its knowledge and reasoning. This paper focuses on the model-based redesign of a path planner's task structure. It illustrates the modelbased reflection using examples from an operational system called the Autognostic system.

 

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