PRODIGY: an integrated architecture for planning and learning
Abstract: "PRODIGY is a computational architecture that integrates general problem solving and multiple learning methods. The primary design objectives are: to provide an open-architecture research vehicle to gain insight into deliberative symbolic reasoning, to investigate learning in the cont...
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Pittsburgh, Pa.
1989
|
| Schriftenreihe: | Carnegie-Mellon University <Pittsburgh, Pa.> / Computer Science Department: CMU-CS
89,189 |
| Schlagworte: | |
| Zusammenfassung: | Abstract: "PRODIGY is a computational architecture that integrates general problem solving and multiple learning methods. The primary design objectives are: to provide an open-architecture research vehicle to gain insight into deliberative symbolic reasoning, to investigate learning in the context of a performance engine, to permit the evaluation of multiple learning techniques within the same architecture in multiple domains, and to provide a basis for the development of flexible, adaptive knowledge-based systems. The PRODIGY system consists of a general planning and problem-solving engine, a set of machine learning techniques, and multiple knowledge sources encoded in a uniform, logic based knowledge representation In particular, PRODIGY learns control knowledge via explanation-based learning and static domain analysis, forms abstraction hierarchies for effective planning, recycles past experience via derivational analogy, extends and refines domain knowledge through experimentation, and acquires knowledge dynamically from domain experts. PRODIGY has been tested in various domains such as basic machine-shop scheduling and high-level robotic planning. This paper focuses primarily on the general PRODIGY problem solver, the explanation-based learning method, the abstraction learning method, and an empirical evaluation of these methods on large populations of problems. |
| Beschreibung: | 38 S. |
Internformat
MARC
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| 100 | 1 | |a Carbonell, Jaime G. |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a PRODIGY |b an integrated architecture for planning and learning |c Jaime G. Carbonell, Craig A. Knoblock and Steven Minton |
| 264 | 1 | |a Pittsburgh, Pa. |c 1989 | |
| 300 | |a 38 S. | ||
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| 337 | |b n |2 rdamedia | ||
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| 490 | 1 | |a Carnegie-Mellon University <Pittsburgh, Pa.> / Computer Science Department: CMU-CS |v 89,189 | |
| 520 | 3 | |a Abstract: "PRODIGY is a computational architecture that integrates general problem solving and multiple learning methods. The primary design objectives are: to provide an open-architecture research vehicle to gain insight into deliberative symbolic reasoning, to investigate learning in the context of a performance engine, to permit the evaluation of multiple learning techniques within the same architecture in multiple domains, and to provide a basis for the development of flexible, adaptive knowledge-based systems. The PRODIGY system consists of a general planning and problem-solving engine, a set of machine learning techniques, and multiple knowledge sources encoded in a uniform, logic based knowledge representation | |
| 520 | 3 | |a In particular, PRODIGY learns control knowledge via explanation-based learning and static domain analysis, forms abstraction hierarchies for effective planning, recycles past experience via derivational analogy, extends and refines domain knowledge through experimentation, and acquires knowledge dynamically from domain experts. PRODIGY has been tested in various domains such as basic machine-shop scheduling and high-level robotic planning. This paper focuses primarily on the general PRODIGY problem solver, the explanation-based learning method, the abstraction learning method, and an empirical evaluation of these methods on large populations of problems. | |
| 650 | 4 | |a Künstliche Intelligenz | |
| 650 | 4 | |a Abstraction | |
| 650 | 4 | |a Artificial intelligence | |
| 650 | 4 | |a Learning models (Stochastic processes) | |
| 700 | 1 | |a Knoblock, Craig A. |d 1962- |e Verfasser |0 (DE-588)172191041 |4 aut | |
| 700 | 1 | |a Minton, Steven |e Verfasser |4 aut | |
| 810 | 2 | |a Computer Science Department: CMU-CS |t Carnegie-Mellon University <Pittsburgh, Pa.> |v 89,189 |w (DE-604)BV006187264 |9 89,189 | |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-005904506 | ||
Datensatz im Suchindex
| _version_ | 1804123281654022144 |
|---|---|
| any_adam_object | |
| author | Carbonell, Jaime G. Knoblock, Craig A. 1962- Minton, Steven |
| author_GND | (DE-588)172191041 |
| author_facet | Carbonell, Jaime G. Knoblock, Craig A. 1962- Minton, Steven |
| author_role | aut aut aut |
| author_sort | Carbonell, Jaime G. |
| author_variant | j g c jg jgc c a k ca cak s m sm |
| building | Verbundindex |
| bvnumber | BV008948778 |
| ctrlnum | (OCoLC)21024562 (DE-599)BVBBV008948778 |
| dewey-full | 510.7808 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 510 - Mathematics |
| dewey-raw | 510.7808 |
| dewey-search | 510.7808 |
| dewey-sort | 3510.7808 |
| dewey-tens | 510 - Mathematics |
| discipline | Mathematik |
| format | Book |
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| id | DE-604.BV008948778 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-09T17:27:17Z |
| institution | BVB |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-005904506 |
| oclc_num | 21024562 |
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| owner_facet | DE-29T |
| physical | 38 S. |
| publishDate | 1989 |
| publishDateSearch | 1989 |
| publishDateSort | 1989 |
| record_format | marc |
| series2 | Carnegie-Mellon University <Pittsburgh, Pa.> / Computer Science Department: CMU-CS |
| spelling | Carbonell, Jaime G. Verfasser aut PRODIGY an integrated architecture for planning and learning Jaime G. Carbonell, Craig A. Knoblock and Steven Minton Pittsburgh, Pa. 1989 38 S. txt rdacontent n rdamedia nc rdacarrier Carnegie-Mellon University <Pittsburgh, Pa.> / Computer Science Department: CMU-CS 89,189 Abstract: "PRODIGY is a computational architecture that integrates general problem solving and multiple learning methods. The primary design objectives are: to provide an open-architecture research vehicle to gain insight into deliberative symbolic reasoning, to investigate learning in the context of a performance engine, to permit the evaluation of multiple learning techniques within the same architecture in multiple domains, and to provide a basis for the development of flexible, adaptive knowledge-based systems. The PRODIGY system consists of a general planning and problem-solving engine, a set of machine learning techniques, and multiple knowledge sources encoded in a uniform, logic based knowledge representation In particular, PRODIGY learns control knowledge via explanation-based learning and static domain analysis, forms abstraction hierarchies for effective planning, recycles past experience via derivational analogy, extends and refines domain knowledge through experimentation, and acquires knowledge dynamically from domain experts. PRODIGY has been tested in various domains such as basic machine-shop scheduling and high-level robotic planning. This paper focuses primarily on the general PRODIGY problem solver, the explanation-based learning method, the abstraction learning method, and an empirical evaluation of these methods on large populations of problems. Künstliche Intelligenz Abstraction Artificial intelligence Learning models (Stochastic processes) Knoblock, Craig A. 1962- Verfasser (DE-588)172191041 aut Minton, Steven Verfasser aut Computer Science Department: CMU-CS Carnegie-Mellon University <Pittsburgh, Pa.> 89,189 (DE-604)BV006187264 89,189 |
| spellingShingle | Carbonell, Jaime G. Knoblock, Craig A. 1962- Minton, Steven PRODIGY an integrated architecture for planning and learning Künstliche Intelligenz Abstraction Artificial intelligence Learning models (Stochastic processes) |
| title | PRODIGY an integrated architecture for planning and learning |
| title_auth | PRODIGY an integrated architecture for planning and learning |
| title_exact_search | PRODIGY an integrated architecture for planning and learning |
| title_full | PRODIGY an integrated architecture for planning and learning Jaime G. Carbonell, Craig A. Knoblock and Steven Minton |
| title_fullStr | PRODIGY an integrated architecture for planning and learning Jaime G. Carbonell, Craig A. Knoblock and Steven Minton |
| title_full_unstemmed | PRODIGY an integrated architecture for planning and learning Jaime G. Carbonell, Craig A. Knoblock and Steven Minton |
| title_short | PRODIGY |
| title_sort | prodigy an integrated architecture for planning and learning |
| title_sub | an integrated architecture for planning and learning |
| topic | Künstliche Intelligenz Abstraction Artificial intelligence Learning models (Stochastic processes) |
| topic_facet | Künstliche Intelligenz Abstraction Artificial intelligence Learning models (Stochastic processes) |
| volume_link | (DE-604)BV006187264 |
| work_keys_str_mv | AT carbonelljaimeg prodigyanintegratedarchitectureforplanningandlearning AT knoblockcraiga prodigyanintegratedarchitectureforplanningandlearning AT mintonsteven prodigyanintegratedarchitectureforplanningandlearning |