Inductive methods for acquiring task knowledge in adaptive systems:
Abstract: "In order to achieve the adaptation of interactive systems to situation-specific task requirements, models of tasks as well as methods of deducing possible tasks from the user's input actions are required. The method of 'task-oriented parsing', which is based on an atti...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Sankt Augustin
1989
|
| Schriftenreihe: | Gesellschaft für Mathematik und Datenverarbeitung <Sankt Augustin>: Arbeitspapiere der GMD
392 |
| Schlagworte: | |
| Zusammenfassung: | Abstract: "In order to achieve the adaptation of interactive systems to situation-specific task requirements, models of tasks as well as methods of deducing possible tasks from the user's input actions are required. The method of 'task-oriented parsing', which is based on an attibute grammar representation of tasks serves these purposes, e.g. enabling further system-initiated advice in cases of suboptimal performance. This is demonstrated by a prototypical application (FINIX) which provides intelligent help for UNIX file handling operations. As for other knowledge-based systems, knowledge acquisition is crucial in order to make this approach practically useful. This paper gives a detailed description of two inductive, similarity-based methods for acquiring task knowledge form [i.e. from] the dialogue history The first approach is semi-automatic and relies on interactions with a human referee, whereas the second is completely automated based on certain heuristics. These methods are analyzed according to the underlying machine learning principles. Finally, an analysis-based approach for acquiring operational task schemata based on declarative descriptions of generic task concepts is briefly explained. The different methods have been implemented and successfully tested in the FINIX environment. They are general in that they may be used to acquire procedural task knowledge in different domains. |
| Beschreibung: | 22 S. |
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| 490 | 1 | |a Gesellschaft für Mathematik und Datenverarbeitung <Sankt Augustin>: Arbeitspapiere der GMD |v 392 | |
| 520 | 3 | |a Abstract: "In order to achieve the adaptation of interactive systems to situation-specific task requirements, models of tasks as well as methods of deducing possible tasks from the user's input actions are required. The method of 'task-oriented parsing', which is based on an attibute grammar representation of tasks serves these purposes, e.g. enabling further system-initiated advice in cases of suboptimal performance. This is demonstrated by a prototypical application (FINIX) which provides intelligent help for UNIX file handling operations. As for other knowledge-based systems, knowledge acquisition is crucial in order to make this approach practically useful. This paper gives a detailed description of two inductive, similarity-based methods for acquiring task knowledge form [i.e. from] the dialogue history | |
| 520 | 3 | |a The first approach is semi-automatic and relies on interactions with a human referee, whereas the second is completely automated based on certain heuristics. These methods are analyzed according to the underlying machine learning principles. Finally, an analysis-based approach for acquiring operational task schemata based on declarative descriptions of generic task concepts is briefly explained. The different methods have been implemented and successfully tested in the FINIX environment. They are general in that they may be used to acquire procedural task knowledge in different domains. | |
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a Task analysis | |
| 700 | 1 | |a Plötzner, Rolf |e Verfasser |4 aut | |
| 830 | 0 | |a Gesellschaft für Mathematik und Datenverarbeitung <Sankt Augustin>: Arbeitspapiere der GMD |v 392 |w (DE-604)BV000613796 |9 392 | |
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Datensatz im Suchindex
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| any_adam_object | |
| author | Hoppe, Heinz Ulrich Plötzner, Rolf |
| author_facet | Hoppe, Heinz Ulrich Plötzner, Rolf |
| author_role | aut aut |
| author_sort | Hoppe, Heinz Ulrich |
| author_variant | h u h hu huh r p rp |
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| bvnumber | BV008983573 |
| ctrlnum | (OCoLC)21896397 (DE-599)BVBBV008983573 |
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| id | DE-604.BV008983573 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-09T17:27:56Z |
| institution | BVB |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-005933686 |
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| physical | 22 S. |
| publishDate | 1989 |
| publishDateSearch | 1989 |
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| series | Gesellschaft für Mathematik und Datenverarbeitung <Sankt Augustin>: Arbeitspapiere der GMD |
| series2 | Gesellschaft für Mathematik und Datenverarbeitung <Sankt Augustin>: Arbeitspapiere der GMD |
| spelling | Hoppe, Heinz Ulrich Verfasser aut Inductive methods for acquiring task knowledge in adaptive systems Heinz Ulrich Hoppe ; Rolf Plötzner Sankt Augustin 1989 22 S. txt rdacontent n rdamedia nc rdacarrier Gesellschaft für Mathematik und Datenverarbeitung <Sankt Augustin>: Arbeitspapiere der GMD 392 Abstract: "In order to achieve the adaptation of interactive systems to situation-specific task requirements, models of tasks as well as methods of deducing possible tasks from the user's input actions are required. The method of 'task-oriented parsing', which is based on an attibute grammar representation of tasks serves these purposes, e.g. enabling further system-initiated advice in cases of suboptimal performance. This is demonstrated by a prototypical application (FINIX) which provides intelligent help for UNIX file handling operations. As for other knowledge-based systems, knowledge acquisition is crucial in order to make this approach practically useful. This paper gives a detailed description of two inductive, similarity-based methods for acquiring task knowledge form [i.e. from] the dialogue history The first approach is semi-automatic and relies on interactions with a human referee, whereas the second is completely automated based on certain heuristics. These methods are analyzed according to the underlying machine learning principles. Finally, an analysis-based approach for acquiring operational task schemata based on declarative descriptions of generic task concepts is briefly explained. The different methods have been implemented and successfully tested in the FINIX environment. They are general in that they may be used to acquire procedural task knowledge in different domains. Machine learning Task analysis Plötzner, Rolf Verfasser aut Gesellschaft für Mathematik und Datenverarbeitung <Sankt Augustin>: Arbeitspapiere der GMD 392 (DE-604)BV000613796 392 |
| spellingShingle | Hoppe, Heinz Ulrich Plötzner, Rolf Inductive methods for acquiring task knowledge in adaptive systems Gesellschaft für Mathematik und Datenverarbeitung <Sankt Augustin>: Arbeitspapiere der GMD Machine learning Task analysis |
| title | Inductive methods for acquiring task knowledge in adaptive systems |
| title_auth | Inductive methods for acquiring task knowledge in adaptive systems |
| title_exact_search | Inductive methods for acquiring task knowledge in adaptive systems |
| title_full | Inductive methods for acquiring task knowledge in adaptive systems Heinz Ulrich Hoppe ; Rolf Plötzner |
| title_fullStr | Inductive methods for acquiring task knowledge in adaptive systems Heinz Ulrich Hoppe ; Rolf Plötzner |
| title_full_unstemmed | Inductive methods for acquiring task knowledge in adaptive systems Heinz Ulrich Hoppe ; Rolf Plötzner |
| title_short | Inductive methods for acquiring task knowledge in adaptive systems |
| title_sort | inductive methods for acquiring task knowledge in adaptive systems |
| topic | Machine learning Task analysis |
| topic_facet | Machine learning Task analysis |
| volume_link | (DE-604)BV000613796 |
| work_keys_str_mv | AT hoppeheinzulrich inductivemethodsforacquiringtaskknowledgeinadaptivesystems AT plotznerrolf inductivemethodsforacquiringtaskknowledgeinadaptivesystems |