Abductive explanation-based learning: A solution to the multiple inconsistent explanation problem
Abstract One problem which frequently surfaces when applying explanation-based learning (EBL) to imperfect theories is themultiple inconsistent explanation problem. The multiple inconsistent explanation problem occurs when a domain theory produces multiple explanations for a training instance, only...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Cohen, William W. [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
1992 |
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| Schlagwörter: |
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| Anmerkung: |
© Kluwer Academic Publishers 1992 |
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| Übergeordnetes Werk: |
Enthalten in: Machine learning - Kluwer Academic Publishers, 1986, 8(1992), 2 vom: März, Seite 167-219 |
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| Übergeordnetes Werk: |
volume:8 ; year:1992 ; number:2 ; month:03 ; pages:167-219 |
| Links: |
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| DOI / URN: |
10.1007/BF00992863 |
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OLC2026512019 |
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| 520 | |a Abstract One problem which frequently surfaces when applying explanation-based learning (EBL) to imperfect theories is themultiple inconsistent explanation problem. The multiple inconsistent explanation problem occurs when a domain theory produces multiple explanations for a training instance, only some of which are correct. Domain theories which suffer from the multiple inconsistent explanation problem can occur in many different contexts, such as when some information is missing and must be assumed: since such assumptions can be incorrect, incorrect explanations can be constructed. This paper proposes an extension of explanation-based learning, calledabductive explanation-based learning (A-EBL) which solves the multiple inconsistent explanation problem by using set covering techniques and negative examples to choose among the possible explanations of a training example. It is shown by formal analysis that A-EBL has convergence properties that are only logarithmically worse than EBL/TS, a formalization of a certain type of knowledge-level EBL; A-EBL is also proven to be computationally efficient, assuming that the domain theory is tractable. Finally, experimental results are reported on an application of A-EBL to learning correct rules for opening bids in the game of contract bridge given examples and an imperfect domain theory. | ||
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10.1007/BF00992863 doi (DE-627)OLC2026512019 (DE-He213)BF00992863-p DE-627 ger DE-627 rakwb eng 150 004 VZ Cohen, William W. verfasserin aut Abductive explanation-based learning: A solution to the multiple inconsistent explanation problem 1992 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Kluwer Academic Publishers 1992 Abstract One problem which frequently surfaces when applying explanation-based learning (EBL) to imperfect theories is themultiple inconsistent explanation problem. The multiple inconsistent explanation problem occurs when a domain theory produces multiple explanations for a training instance, only some of which are correct. Domain theories which suffer from the multiple inconsistent explanation problem can occur in many different contexts, such as when some information is missing and must be assumed: since such assumptions can be incorrect, incorrect explanations can be constructed. This paper proposes an extension of explanation-based learning, calledabductive explanation-based learning (A-EBL) which solves the multiple inconsistent explanation problem by using set covering techniques and negative examples to choose among the possible explanations of a training example. It is shown by formal analysis that A-EBL has convergence properties that are only logarithmically worse than EBL/TS, a formalization of a certain type of knowledge-level EBL; A-EBL is also proven to be computationally efficient, assuming that the domain theory is tractable. Finally, experimental results are reported on an application of A-EBL to learning correct rules for opening bids in the game of contract bridge given examples and an imperfect domain theory. Explanation-based learning theory revision theory specialization probably approximately correct learning Enthalten in Machine learning Kluwer Academic Publishers, 1986 8(1992), 2 vom: März, Seite 167-219 (DE-627)12920403X (DE-600)54638-0 (DE-576)014457377 0885-6125 nnns volume:8 year:1992 number:2 month:03 pages:167-219 https://doi.org/10.1007/BF00992863 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_21 GBV_ILN_22 GBV_ILN_31 GBV_ILN_70 GBV_ILN_130 GBV_ILN_2006 GBV_ILN_2010 GBV_ILN_2020 GBV_ILN_2093 GBV_ILN_2244 GBV_ILN_4012 GBV_ILN_4046 GBV_ILN_4266 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4318 AR 8 1992 2 03 167-219 |
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10.1007/BF00992863 doi (DE-627)OLC2026512019 (DE-He213)BF00992863-p DE-627 ger DE-627 rakwb eng 150 004 VZ Cohen, William W. verfasserin aut Abductive explanation-based learning: A solution to the multiple inconsistent explanation problem 1992 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Kluwer Academic Publishers 1992 Abstract One problem which frequently surfaces when applying explanation-based learning (EBL) to imperfect theories is themultiple inconsistent explanation problem. The multiple inconsistent explanation problem occurs when a domain theory produces multiple explanations for a training instance, only some of which are correct. Domain theories which suffer from the multiple inconsistent explanation problem can occur in many different contexts, such as when some information is missing and must be assumed: since such assumptions can be incorrect, incorrect explanations can be constructed. This paper proposes an extension of explanation-based learning, calledabductive explanation-based learning (A-EBL) which solves the multiple inconsistent explanation problem by using set covering techniques and negative examples to choose among the possible explanations of a training example. It is shown by formal analysis that A-EBL has convergence properties that are only logarithmically worse than EBL/TS, a formalization of a certain type of knowledge-level EBL; A-EBL is also proven to be computationally efficient, assuming that the domain theory is tractable. Finally, experimental results are reported on an application of A-EBL to learning correct rules for opening bids in the game of contract bridge given examples and an imperfect domain theory. Explanation-based learning theory revision theory specialization probably approximately correct learning Enthalten in Machine learning Kluwer Academic Publishers, 1986 8(1992), 2 vom: März, Seite 167-219 (DE-627)12920403X (DE-600)54638-0 (DE-576)014457377 0885-6125 nnns volume:8 year:1992 number:2 month:03 pages:167-219 https://doi.org/10.1007/BF00992863 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_21 GBV_ILN_22 GBV_ILN_31 GBV_ILN_70 GBV_ILN_130 GBV_ILN_2006 GBV_ILN_2010 GBV_ILN_2020 GBV_ILN_2093 GBV_ILN_2244 GBV_ILN_4012 GBV_ILN_4046 GBV_ILN_4266 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4318 AR 8 1992 2 03 167-219 |
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10.1007/BF00992863 doi (DE-627)OLC2026512019 (DE-He213)BF00992863-p DE-627 ger DE-627 rakwb eng 150 004 VZ Cohen, William W. verfasserin aut Abductive explanation-based learning: A solution to the multiple inconsistent explanation problem 1992 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Kluwer Academic Publishers 1992 Abstract One problem which frequently surfaces when applying explanation-based learning (EBL) to imperfect theories is themultiple inconsistent explanation problem. The multiple inconsistent explanation problem occurs when a domain theory produces multiple explanations for a training instance, only some of which are correct. Domain theories which suffer from the multiple inconsistent explanation problem can occur in many different contexts, such as when some information is missing and must be assumed: since such assumptions can be incorrect, incorrect explanations can be constructed. This paper proposes an extension of explanation-based learning, calledabductive explanation-based learning (A-EBL) which solves the multiple inconsistent explanation problem by using set covering techniques and negative examples to choose among the possible explanations of a training example. It is shown by formal analysis that A-EBL has convergence properties that are only logarithmically worse than EBL/TS, a formalization of a certain type of knowledge-level EBL; A-EBL is also proven to be computationally efficient, assuming that the domain theory is tractable. Finally, experimental results are reported on an application of A-EBL to learning correct rules for opening bids in the game of contract bridge given examples and an imperfect domain theory. Explanation-based learning theory revision theory specialization probably approximately correct learning Enthalten in Machine learning Kluwer Academic Publishers, 1986 8(1992), 2 vom: März, Seite 167-219 (DE-627)12920403X (DE-600)54638-0 (DE-576)014457377 0885-6125 nnns volume:8 year:1992 number:2 month:03 pages:167-219 https://doi.org/10.1007/BF00992863 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_21 GBV_ILN_22 GBV_ILN_31 GBV_ILN_70 GBV_ILN_130 GBV_ILN_2006 GBV_ILN_2010 GBV_ILN_2020 GBV_ILN_2093 GBV_ILN_2244 GBV_ILN_4012 GBV_ILN_4046 GBV_ILN_4266 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4318 AR 8 1992 2 03 167-219 |
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10.1007/BF00992863 doi (DE-627)OLC2026512019 (DE-He213)BF00992863-p DE-627 ger DE-627 rakwb eng 150 004 VZ Cohen, William W. verfasserin aut Abductive explanation-based learning: A solution to the multiple inconsistent explanation problem 1992 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Kluwer Academic Publishers 1992 Abstract One problem which frequently surfaces when applying explanation-based learning (EBL) to imperfect theories is themultiple inconsistent explanation problem. The multiple inconsistent explanation problem occurs when a domain theory produces multiple explanations for a training instance, only some of which are correct. Domain theories which suffer from the multiple inconsistent explanation problem can occur in many different contexts, such as when some information is missing and must be assumed: since such assumptions can be incorrect, incorrect explanations can be constructed. This paper proposes an extension of explanation-based learning, calledabductive explanation-based learning (A-EBL) which solves the multiple inconsistent explanation problem by using set covering techniques and negative examples to choose among the possible explanations of a training example. It is shown by formal analysis that A-EBL has convergence properties that are only logarithmically worse than EBL/TS, a formalization of a certain type of knowledge-level EBL; A-EBL is also proven to be computationally efficient, assuming that the domain theory is tractable. Finally, experimental results are reported on an application of A-EBL to learning correct rules for opening bids in the game of contract bridge given examples and an imperfect domain theory. Explanation-based learning theory revision theory specialization probably approximately correct learning Enthalten in Machine learning Kluwer Academic Publishers, 1986 8(1992), 2 vom: März, Seite 167-219 (DE-627)12920403X (DE-600)54638-0 (DE-576)014457377 0885-6125 nnns volume:8 year:1992 number:2 month:03 pages:167-219 https://doi.org/10.1007/BF00992863 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_21 GBV_ILN_22 GBV_ILN_31 GBV_ILN_70 GBV_ILN_130 GBV_ILN_2006 GBV_ILN_2010 GBV_ILN_2020 GBV_ILN_2093 GBV_ILN_2244 GBV_ILN_4012 GBV_ILN_4046 GBV_ILN_4266 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4318 AR 8 1992 2 03 167-219 |
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10.1007/BF00992863 doi (DE-627)OLC2026512019 (DE-He213)BF00992863-p DE-627 ger DE-627 rakwb eng 150 004 VZ Cohen, William W. verfasserin aut Abductive explanation-based learning: A solution to the multiple inconsistent explanation problem 1992 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Kluwer Academic Publishers 1992 Abstract One problem which frequently surfaces when applying explanation-based learning (EBL) to imperfect theories is themultiple inconsistent explanation problem. The multiple inconsistent explanation problem occurs when a domain theory produces multiple explanations for a training instance, only some of which are correct. Domain theories which suffer from the multiple inconsistent explanation problem can occur in many different contexts, such as when some information is missing and must be assumed: since such assumptions can be incorrect, incorrect explanations can be constructed. This paper proposes an extension of explanation-based learning, calledabductive explanation-based learning (A-EBL) which solves the multiple inconsistent explanation problem by using set covering techniques and negative examples to choose among the possible explanations of a training example. It is shown by formal analysis that A-EBL has convergence properties that are only logarithmically worse than EBL/TS, a formalization of a certain type of knowledge-level EBL; A-EBL is also proven to be computationally efficient, assuming that the domain theory is tractable. Finally, experimental results are reported on an application of A-EBL to learning correct rules for opening bids in the game of contract bridge given examples and an imperfect domain theory. Explanation-based learning theory revision theory specialization probably approximately correct learning Enthalten in Machine learning Kluwer Academic Publishers, 1986 8(1992), 2 vom: März, Seite 167-219 (DE-627)12920403X (DE-600)54638-0 (DE-576)014457377 0885-6125 nnns volume:8 year:1992 number:2 month:03 pages:167-219 https://doi.org/10.1007/BF00992863 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_21 GBV_ILN_22 GBV_ILN_31 GBV_ILN_70 GBV_ILN_130 GBV_ILN_2006 GBV_ILN_2010 GBV_ILN_2020 GBV_ILN_2093 GBV_ILN_2244 GBV_ILN_4012 GBV_ILN_4046 GBV_ILN_4266 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4318 AR 8 1992 2 03 167-219 |
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Aufsätze |
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Cohen, William W. |
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10.1007/BF00992863 |
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150 004 |
| title_sort |
abductive explanation-based learning: a solution to the multiple inconsistent explanation problem |
| title_auth |
Abductive explanation-based learning: A solution to the multiple inconsistent explanation problem |
| abstract |
Abstract One problem which frequently surfaces when applying explanation-based learning (EBL) to imperfect theories is themultiple inconsistent explanation problem. The multiple inconsistent explanation problem occurs when a domain theory produces multiple explanations for a training instance, only some of which are correct. Domain theories which suffer from the multiple inconsistent explanation problem can occur in many different contexts, such as when some information is missing and must be assumed: since such assumptions can be incorrect, incorrect explanations can be constructed. This paper proposes an extension of explanation-based learning, calledabductive explanation-based learning (A-EBL) which solves the multiple inconsistent explanation problem by using set covering techniques and negative examples to choose among the possible explanations of a training example. It is shown by formal analysis that A-EBL has convergence properties that are only logarithmically worse than EBL/TS, a formalization of a certain type of knowledge-level EBL; A-EBL is also proven to be computationally efficient, assuming that the domain theory is tractable. Finally, experimental results are reported on an application of A-EBL to learning correct rules for opening bids in the game of contract bridge given examples and an imperfect domain theory. © Kluwer Academic Publishers 1992 |
| abstractGer |
Abstract One problem which frequently surfaces when applying explanation-based learning (EBL) to imperfect theories is themultiple inconsistent explanation problem. The multiple inconsistent explanation problem occurs when a domain theory produces multiple explanations for a training instance, only some of which are correct. Domain theories which suffer from the multiple inconsistent explanation problem can occur in many different contexts, such as when some information is missing and must be assumed: since such assumptions can be incorrect, incorrect explanations can be constructed. This paper proposes an extension of explanation-based learning, calledabductive explanation-based learning (A-EBL) which solves the multiple inconsistent explanation problem by using set covering techniques and negative examples to choose among the possible explanations of a training example. It is shown by formal analysis that A-EBL has convergence properties that are only logarithmically worse than EBL/TS, a formalization of a certain type of knowledge-level EBL; A-EBL is also proven to be computationally efficient, assuming that the domain theory is tractable. Finally, experimental results are reported on an application of A-EBL to learning correct rules for opening bids in the game of contract bridge given examples and an imperfect domain theory. © Kluwer Academic Publishers 1992 |
| abstract_unstemmed |
Abstract One problem which frequently surfaces when applying explanation-based learning (EBL) to imperfect theories is themultiple inconsistent explanation problem. The multiple inconsistent explanation problem occurs when a domain theory produces multiple explanations for a training instance, only some of which are correct. Domain theories which suffer from the multiple inconsistent explanation problem can occur in many different contexts, such as when some information is missing and must be assumed: since such assumptions can be incorrect, incorrect explanations can be constructed. This paper proposes an extension of explanation-based learning, calledabductive explanation-based learning (A-EBL) which solves the multiple inconsistent explanation problem by using set covering techniques and negative examples to choose among the possible explanations of a training example. It is shown by formal analysis that A-EBL has convergence properties that are only logarithmically worse than EBL/TS, a formalization of a certain type of knowledge-level EBL; A-EBL is also proven to be computationally efficient, assuming that the domain theory is tractable. Finally, experimental results are reported on an application of A-EBL to learning correct rules for opening bids in the game of contract bridge given examples and an imperfect domain theory. © Kluwer Academic Publishers 1992 |
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Abductive explanation-based learning: A solution to the multiple inconsistent explanation problem |
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