Credit Assignment in Rule Discovery Systems Based on Genetic Algorithms
Abstract In rule discovery systems, learning often proceeds by first assessing the quality of the system's current rules and then modifying rules based on that assessment. This paper addresses the credit assignment problem that arises when long sequences of rules fire between successive externa...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Grefenstette, John J. [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
1988 |
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| Anmerkung: |
© Kluwer Academic Publishers 1988 |
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| Übergeordnetes Werk: |
Enthalten in: Machine learning - Kluwer Academic Publishers-Plenum Publishers, 1986, 3(1988), 2-3 vom: Okt., Seite 225-245 |
|---|---|
| Übergeordnetes Werk: |
volume:3 ; year:1988 ; number:2-3 ; month:10 ; pages:225-245 |
| Links: |
|---|
| DOI / URN: |
10.1023/A:1022614421909 |
|---|
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OLC2026510296 |
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| 520 | |a Abstract In rule discovery systems, learning often proceeds by first assessing the quality of the system's current rules and then modifying rules based on that assessment. This paper addresses the credit assignment problem that arises when long sequences of rules fire between successive external rewards. The focus is on the kinds of rule assessment schemes which have been proposed for rule discovery systems that use genetic algorithms as the primary rule modification strategy. Two distinct approaches to rule learning with genetic algorithms have been previously reported, each approach offering a useful solution to a different level of the credit assignment problem. We describe a system, called RUDI, that exploits both approaches. We present analytic and experimental results that support the hypothesis that multiple levels of credit assignment can improve the performance of rule learning systems based on genetic algorithms. | ||
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10.1023/A:1022614421909 doi (DE-627)OLC2026510296 (DE-He213)A:1022614421909-p DE-627 ger DE-627 rakwb eng 150 004 VZ Grefenstette, John J. verfasserin aut Credit Assignment in Rule Discovery Systems Based on Genetic Algorithms 1988 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Kluwer Academic Publishers 1988 Abstract In rule discovery systems, learning often proceeds by first assessing the quality of the system's current rules and then modifying rules based on that assessment. This paper addresses the credit assignment problem that arises when long sequences of rules fire between successive external rewards. The focus is on the kinds of rule assessment schemes which have been proposed for rule discovery systems that use genetic algorithms as the primary rule modification strategy. Two distinct approaches to rule learning with genetic algorithms have been previously reported, each approach offering a useful solution to a different level of the credit assignment problem. We describe a system, called RUDI, that exploits both approaches. We present analytic and experimental results that support the hypothesis that multiple levels of credit assignment can improve the performance of rule learning systems based on genetic algorithms. Enthalten in Machine learning Kluwer Academic Publishers-Plenum Publishers, 1986 3(1988), 2-3 vom: Okt., Seite 225-245 (DE-627)12920403X (DE-600)54638-0 (DE-576)014457377 0885-6125 nnns volume:3 year:1988 number:2-3 month:10 pages:225-245 https://doi.org/10.1023/A:1022614421909 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_2093 GBV_ILN_2244 GBV_ILN_4012 GBV_ILN_4046 GBV_ILN_4266 GBV_ILN_4307 GBV_ILN_4318 AR 3 1988 2-3 10 225-245 |
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10.1023/A:1022614421909 doi (DE-627)OLC2026510296 (DE-He213)A:1022614421909-p DE-627 ger DE-627 rakwb eng 150 004 VZ Grefenstette, John J. verfasserin aut Credit Assignment in Rule Discovery Systems Based on Genetic Algorithms 1988 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Kluwer Academic Publishers 1988 Abstract In rule discovery systems, learning often proceeds by first assessing the quality of the system's current rules and then modifying rules based on that assessment. This paper addresses the credit assignment problem that arises when long sequences of rules fire between successive external rewards. The focus is on the kinds of rule assessment schemes which have been proposed for rule discovery systems that use genetic algorithms as the primary rule modification strategy. Two distinct approaches to rule learning with genetic algorithms have been previously reported, each approach offering a useful solution to a different level of the credit assignment problem. We describe a system, called RUDI, that exploits both approaches. We present analytic and experimental results that support the hypothesis that multiple levels of credit assignment can improve the performance of rule learning systems based on genetic algorithms. Enthalten in Machine learning Kluwer Academic Publishers-Plenum Publishers, 1986 3(1988), 2-3 vom: Okt., Seite 225-245 (DE-627)12920403X (DE-600)54638-0 (DE-576)014457377 0885-6125 nnns volume:3 year:1988 number:2-3 month:10 pages:225-245 https://doi.org/10.1023/A:1022614421909 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_2093 GBV_ILN_2244 GBV_ILN_4012 GBV_ILN_4046 GBV_ILN_4266 GBV_ILN_4307 GBV_ILN_4318 AR 3 1988 2-3 10 225-245 |
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10.1023/A:1022614421909 doi (DE-627)OLC2026510296 (DE-He213)A:1022614421909-p DE-627 ger DE-627 rakwb eng 150 004 VZ Grefenstette, John J. verfasserin aut Credit Assignment in Rule Discovery Systems Based on Genetic Algorithms 1988 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Kluwer Academic Publishers 1988 Abstract In rule discovery systems, learning often proceeds by first assessing the quality of the system's current rules and then modifying rules based on that assessment. This paper addresses the credit assignment problem that arises when long sequences of rules fire between successive external rewards. The focus is on the kinds of rule assessment schemes which have been proposed for rule discovery systems that use genetic algorithms as the primary rule modification strategy. Two distinct approaches to rule learning with genetic algorithms have been previously reported, each approach offering a useful solution to a different level of the credit assignment problem. We describe a system, called RUDI, that exploits both approaches. We present analytic and experimental results that support the hypothesis that multiple levels of credit assignment can improve the performance of rule learning systems based on genetic algorithms. Enthalten in Machine learning Kluwer Academic Publishers-Plenum Publishers, 1986 3(1988), 2-3 vom: Okt., Seite 225-245 (DE-627)12920403X (DE-600)54638-0 (DE-576)014457377 0885-6125 nnns volume:3 year:1988 number:2-3 month:10 pages:225-245 https://doi.org/10.1023/A:1022614421909 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_2093 GBV_ILN_2244 GBV_ILN_4012 GBV_ILN_4046 GBV_ILN_4266 GBV_ILN_4307 GBV_ILN_4318 AR 3 1988 2-3 10 225-245 |
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10.1023/A:1022614421909 doi (DE-627)OLC2026510296 (DE-He213)A:1022614421909-p DE-627 ger DE-627 rakwb eng 150 004 VZ Grefenstette, John J. verfasserin aut Credit Assignment in Rule Discovery Systems Based on Genetic Algorithms 1988 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Kluwer Academic Publishers 1988 Abstract In rule discovery systems, learning often proceeds by first assessing the quality of the system's current rules and then modifying rules based on that assessment. This paper addresses the credit assignment problem that arises when long sequences of rules fire between successive external rewards. The focus is on the kinds of rule assessment schemes which have been proposed for rule discovery systems that use genetic algorithms as the primary rule modification strategy. Two distinct approaches to rule learning with genetic algorithms have been previously reported, each approach offering a useful solution to a different level of the credit assignment problem. We describe a system, called RUDI, that exploits both approaches. We present analytic and experimental results that support the hypothesis that multiple levels of credit assignment can improve the performance of rule learning systems based on genetic algorithms. Enthalten in Machine learning Kluwer Academic Publishers-Plenum Publishers, 1986 3(1988), 2-3 vom: Okt., Seite 225-245 (DE-627)12920403X (DE-600)54638-0 (DE-576)014457377 0885-6125 nnns volume:3 year:1988 number:2-3 month:10 pages:225-245 https://doi.org/10.1023/A:1022614421909 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_2093 GBV_ILN_2244 GBV_ILN_4012 GBV_ILN_4046 GBV_ILN_4266 GBV_ILN_4307 GBV_ILN_4318 AR 3 1988 2-3 10 225-245 |
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10.1023/A:1022614421909 doi (DE-627)OLC2026510296 (DE-He213)A:1022614421909-p DE-627 ger DE-627 rakwb eng 150 004 VZ Grefenstette, John J. verfasserin aut Credit Assignment in Rule Discovery Systems Based on Genetic Algorithms 1988 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Kluwer Academic Publishers 1988 Abstract In rule discovery systems, learning often proceeds by first assessing the quality of the system's current rules and then modifying rules based on that assessment. This paper addresses the credit assignment problem that arises when long sequences of rules fire between successive external rewards. The focus is on the kinds of rule assessment schemes which have been proposed for rule discovery systems that use genetic algorithms as the primary rule modification strategy. Two distinct approaches to rule learning with genetic algorithms have been previously reported, each approach offering a useful solution to a different level of the credit assignment problem. We describe a system, called RUDI, that exploits both approaches. We present analytic and experimental results that support the hypothesis that multiple levels of credit assignment can improve the performance of rule learning systems based on genetic algorithms. Enthalten in Machine learning Kluwer Academic Publishers-Plenum Publishers, 1986 3(1988), 2-3 vom: Okt., Seite 225-245 (DE-627)12920403X (DE-600)54638-0 (DE-576)014457377 0885-6125 nnns volume:3 year:1988 number:2-3 month:10 pages:225-245 https://doi.org/10.1023/A:1022614421909 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_2093 GBV_ILN_2244 GBV_ILN_4012 GBV_ILN_4046 GBV_ILN_4266 GBV_ILN_4307 GBV_ILN_4318 AR 3 1988 2-3 10 225-245 |
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Abstract In rule discovery systems, learning often proceeds by first assessing the quality of the system's current rules and then modifying rules based on that assessment. This paper addresses the credit assignment problem that arises when long sequences of rules fire between successive external rewards. The focus is on the kinds of rule assessment schemes which have been proposed for rule discovery systems that use genetic algorithms as the primary rule modification strategy. Two distinct approaches to rule learning with genetic algorithms have been previously reported, each approach offering a useful solution to a different level of the credit assignment problem. We describe a system, called RUDI, that exploits both approaches. We present analytic and experimental results that support the hypothesis that multiple levels of credit assignment can improve the performance of rule learning systems based on genetic algorithms. © Kluwer Academic Publishers 1988 |
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Abstract In rule discovery systems, learning often proceeds by first assessing the quality of the system's current rules and then modifying rules based on that assessment. This paper addresses the credit assignment problem that arises when long sequences of rules fire between successive external rewards. The focus is on the kinds of rule assessment schemes which have been proposed for rule discovery systems that use genetic algorithms as the primary rule modification strategy. Two distinct approaches to rule learning with genetic algorithms have been previously reported, each approach offering a useful solution to a different level of the credit assignment problem. We describe a system, called RUDI, that exploits both approaches. We present analytic and experimental results that support the hypothesis that multiple levels of credit assignment can improve the performance of rule learning systems based on genetic algorithms. © Kluwer Academic Publishers 1988 |
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Abstract In rule discovery systems, learning often proceeds by first assessing the quality of the system's current rules and then modifying rules based on that assessment. This paper addresses the credit assignment problem that arises when long sequences of rules fire between successive external rewards. The focus is on the kinds of rule assessment schemes which have been proposed for rule discovery systems that use genetic algorithms as the primary rule modification strategy. Two distinct approaches to rule learning with genetic algorithms have been previously reported, each approach offering a useful solution to a different level of the credit assignment problem. We describe a system, called RUDI, that exploits both approaches. We present analytic and experimental results that support the hypothesis that multiple levels of credit assignment can improve the performance of rule learning systems based on genetic algorithms. © Kluwer Academic Publishers 1988 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2026510296</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230503172141.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200820s1988 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1023/A:1022614421909</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2026510296</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)A:1022614421909-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">150</subfield><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Grefenstette, John J.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Credit Assignment in Rule Discovery Systems Based on Genetic Algorithms</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">1988</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Kluwer Academic Publishers 1988</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract In rule discovery systems, learning often proceeds by first assessing the quality of the system's current rules and then modifying rules based on that assessment. 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