| Project/Area Number |
02452157
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| Research Category |
Grant-in-Aid for General Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Research Field |
情報工学
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| Research Institution | Tokyo Institute of Technology |
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Principal Investigator |
KOBAYASHI Shigenobu Tokyo Institute of Technology, Department of Intelligence Science, Professor, 大学院総合理工学研究科, 教授 (40016697)
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| Co-Investigator(Kenkyū-buntansha) |
YAMAMURA Masayuki Tokyo Institute of Technology Department of Intelligence Science, research Assoc, 大学院総合理工学研究科, 助手 (00220442)
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| Project Period (FY) |
1990 – 1991
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| Project Status |
Completed (Fiscal Year 1991)
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| Budget Amount *help |
¥5,600,000 (Direct Cost: ¥5,600,000)
Fiscal Year 1991: ¥2,400,000 (Direct Cost: ¥2,400,000)
Fiscal Year 1990: ¥3,200,000 (Direct Cost: ¥3,200,000)
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| Keywords | Explanation-Based Learning / Extended EBL / Integrated Learning / Imperfect Domain Theory Problem / Operationalization Problem / Inconsistent Problem / Intractable Problem / Incomplete Problem / EBL / SBL / 効用問題 |
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| Research Abstract |
The objective of this research was to establish a methodology for acquiring valid and useful macro rules under the imperfect domain theory.
The following results were obtained
1)proposition of an extended EBL
The augmented EBL is a framework for knowledge refinement based on generalization from prural examples. As operationality criteria, the maximization of the usage and the minimization of backtracking number were introduced. The least EBG is useful to find operational generalizations incrementally. A Learning system with an incremental least EBG generator has been realized.
2)an approach to the inconsistent problem
A method for solving the inconsistent problem has been proposed by introducing a concept of maximal covering. By top down search over generalization hierarchy of common explanation structures, the best set of macro rules which includes all positive examples and excludes all negative ones can be found.
3)an approach to the intractable problem
Focusing an a class of problem solving that have serially desomposable subgoals, an augmented EBL learner that acquires a problem solving macrotable from examples. The usefulness of the learner has been shown by applying to the eight puzzle problem.
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