Generation of aggregated knowledge in qualitative reasoning:
Abstract: "Because knowledge acquisition is a very difficult process, some qualitative reasoning systems use deep knowledge representing principles. But using deep knowledge increases the complexity of reasoning because the grain size of reasoning that uses only deep knowledge is sometimes too...
Gespeichert in:
| Format: | Buch |
|---|---|
| Sprache: | English |
| Veröffentlicht: |
Tokyo, Japan
1992
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| Schriftenreihe: | Shin-Sedai-Konpyūta-Gijutsu-Kaihatsu-Kikō <Tōkyō>: ICOT technical report
820 |
| Schlagworte: | |
| Zusammenfassung: | Abstract: "Because knowledge acquisition is a very difficult process, some qualitative reasoning systems use deep knowledge representing principles. But using deep knowledge increases the complexity of reasoning because the grain size of reasoning that uses only deep knowledge is sometimes too small. We therefore propose a method for generating knowledge that has a larger grain size. This method generates 'aggregated knowledge,' representing the behavior of large components, from deep knowledge representing the behavior of small components. The generation process consists of three analysis steps. The first is qualitative simulation to find all possible bahaviors of the target large component The second is to find all the possible states from these behaviors. And the last is to find the transitional order of those states. These steps generate aggregated knowledge that has existential conditions, relations, and transitional orders for each possible state. Such aggregated knowledge can represent all kinds of components and is useful in applying qualitative reasoning to large and complex systems. |
| Beschreibung: | 22 S. |
Internformat
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| 490 | 1 | |a Shin-Sedai-Konpyūta-Gijutsu-Kaihatsu-Kikō <Tōkyō>: ICOT technical report |v 820 | |
| 520 | 3 | |a Abstract: "Because knowledge acquisition is a very difficult process, some qualitative reasoning systems use deep knowledge representing principles. But using deep knowledge increases the complexity of reasoning because the grain size of reasoning that uses only deep knowledge is sometimes too small. We therefore propose a method for generating knowledge that has a larger grain size. This method generates 'aggregated knowledge,' representing the behavior of large components, from deep knowledge representing the behavior of small components. The generation process consists of three analysis steps. The first is qualitative simulation to find all possible bahaviors of the target large component | |
| 520 | 3 | |a The second is to find all the possible states from these behaviors. And the last is to find the transitional order of those states. These steps generate aggregated knowledge that has existential conditions, relations, and transitional orders for each possible state. Such aggregated knowledge can represent all kinds of components and is useful in applying qualitative reasoning to large and complex systems. | |
| 650 | 4 | |a Knowledge acquisition (Expert systems) | |
| 700 | 1 | |a Shinjo, Hiroshi |e Sonstige |4 oth | |
| 830 | 0 | |a Shin-Sedai-Konpyūta-Gijutsu-Kaihatsu-Kikō <Tōkyō>: ICOT technical report |v 820 |w (DE-604)BV010923438 |9 820 | |
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| id | DE-604.BV010957508 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-09T18:01:40Z |
| institution | BVB |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-007329531 |
| oclc_num | 29209045 |
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| owner_facet | DE-91G DE-BY-TUM |
| physical | 22 S. |
| publishDate | 1992 |
| publishDateSearch | 1992 |
| publishDateSort | 1992 |
| record_format | marc |
| series | Shin-Sedai-Konpyūta-Gijutsu-Kaihatsu-Kikō <Tōkyō>: ICOT technical report |
| series2 | Shin-Sedai-Konpyūta-Gijutsu-Kaihatsu-Kikō <Tōkyō>: ICOT technical report |
| spelling | Generation of aggregated knowledge in qualitative reasoning by H. Shinjo ... Tokyo, Japan 1992 22 S. txt rdacontent n rdamedia nc rdacarrier Shin-Sedai-Konpyūta-Gijutsu-Kaihatsu-Kikō <Tōkyō>: ICOT technical report 820 Abstract: "Because knowledge acquisition is a very difficult process, some qualitative reasoning systems use deep knowledge representing principles. But using deep knowledge increases the complexity of reasoning because the grain size of reasoning that uses only deep knowledge is sometimes too small. We therefore propose a method for generating knowledge that has a larger grain size. This method generates 'aggregated knowledge,' representing the behavior of large components, from deep knowledge representing the behavior of small components. The generation process consists of three analysis steps. The first is qualitative simulation to find all possible bahaviors of the target large component The second is to find all the possible states from these behaviors. And the last is to find the transitional order of those states. These steps generate aggregated knowledge that has existential conditions, relations, and transitional orders for each possible state. Such aggregated knowledge can represent all kinds of components and is useful in applying qualitative reasoning to large and complex systems. Knowledge acquisition (Expert systems) Shinjo, Hiroshi Sonstige oth Shin-Sedai-Konpyūta-Gijutsu-Kaihatsu-Kikō <Tōkyō>: ICOT technical report 820 (DE-604)BV010923438 820 |
| spellingShingle | Generation of aggregated knowledge in qualitative reasoning Shin-Sedai-Konpyūta-Gijutsu-Kaihatsu-Kikō <Tōkyō>: ICOT technical report Knowledge acquisition (Expert systems) |
| title | Generation of aggregated knowledge in qualitative reasoning |
| title_auth | Generation of aggregated knowledge in qualitative reasoning |
| title_exact_search | Generation of aggregated knowledge in qualitative reasoning |
| title_full | Generation of aggregated knowledge in qualitative reasoning by H. Shinjo ... |
| title_fullStr | Generation of aggregated knowledge in qualitative reasoning by H. Shinjo ... |
| title_full_unstemmed | Generation of aggregated knowledge in qualitative reasoning by H. Shinjo ... |
| title_short | Generation of aggregated knowledge in qualitative reasoning |
| title_sort | generation of aggregated knowledge in qualitative reasoning |
| topic | Knowledge acquisition (Expert systems) |
| topic_facet | Knowledge acquisition (Expert systems) |
| volume_link | (DE-604)BV010923438 |
| work_keys_str_mv | AT shinjohiroshi generationofaggregatedknowledgeinqualitativereasoning |