A problem-solving model for episodic skeletal-plan refinement
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abstract: PROTEGE is a metalevel program that generates knowledge-acquisition tools that are based on the method of skeletal-plan-refinement. In this paper, we propose a flexible and extensible architecture that allows the problem-solving method to be assembled from more basic methods. In this architecture, we emphasize (1) a uniform view of problem solving at different levels of granularities, (2) an explicit data model that allows construction of complex datatypes from predefined datatypes, (3) the inclusion of domain-dependent control information within a domain-independent problem-solving method. We show how such a model of problem solving can drive the generation of knowledge-acquisition tools.
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| Abstract | PROTEGE is a metalevel program that genera … PROTEGE is a metalevel program that generates knowledge-acquisition tools that are based on the method of skeletal-plan-refinement. In this paper, we propose a flexible and extensible architecture that allows the problem-solving method to be assembled from more basic methods. In this architecture, we emphasize (1) a uniform view of problem solving at different levels of granularities, (2) an explicit data model that allows construction of complex datatypes from predefined datatypes, (3) the inclusion of domain-dependent control information within a domain-independent problem-solving method. We show how such a model of problem solving can drive the generation of knowledge-acquisition tools. generation of knowledge-acquisition tools. |
| Author | Samson W. Tu +, Yuval Shahar +, John Dawes +, James Winkles +, Angel R. Puerta +, and Mark A. Musen + |
| Bibtype | techreport + |
| Institution | Knowledge Systems, AI Laboratory + |
| Key | KSL-91-22 + |
| Number | KSL-91-22 + |
| Tag | Computer science + |
| Title | A Problem-Solving Model for Episodic Skeletal-Plan Refinement + |
| Tr id | KSL-91-22 + |
| Year | 1991 + |
