The RACE Project

Abstract This paper reports on the aims, the approach, and the results of the European project RACE. The project aim was to enhance the behavior of an autonomous robot by having the robot learn from conceptualized experiences of previous performance, based on initial models of the domain and its own...
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Autor*in:	Hertzberg, Joachim [verfasserIn] Zhang, Jianwei Zhang, Liwei Rockel, Sebastian Neumann, Bernd Lehmann, Jos Dubba, Krishna S. R. Cohn, Anthony G. Saffiotti, Alessandro Pecora, Federico Mansouri, Masoumeh Konečný, Štefan Günther, Martin Stock, Sebastian Lopes, Luis Seabra Oliveira, Miguel Lim, Gi Hyun Kasaei, Hamidreza Mokhtari, Vahid Hotz, Lothar Bohlken, Wilfried

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2014

Schlagwörter:	Object Category Control Architecture Plan Execution Early Integration Perceptual Memory

Anmerkung:	© Springer-Verlag Berlin Heidelberg 2014

Übergeordnetes Werk:	Enthalten in: Künstliche Intelligenz - Berlin : Springer, 2010, 28(2014), 4 vom: 21. Sept., Seite 297-304
Übergeordnetes Werk:	volume:28 ; year:2014 ; number:4 ; day:21 ; month:09 ; pages:297-304

Links:	Volltext

DOI / URN:	10.1007/s13218-014-0327-y

Katalog-ID:	SPR030894921

Internformat


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520			\|a Abstract This paper reports on the aims, the approach, and the results of the European project RACE. The project aim was to enhance the behavior of an autonomous robot by having the robot learn from conceptualized experiences of previous performance, based on initial models of the domain and its own actions in it. This paper introduces the general system architecture; it then sketches some results in detail regarding hybrid reasoning and planning used in RACE, and instances of learning from the experiences of real robot task execution. Enhancement of robot competence is operationalized in terms of performance quality and description length of the robot instructions, and such enhancement is shown to result from the RACE system.
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650		4	\|a Control Architecture \|7 (dpeaa)DE-He213
650		4	\|a Plan Execution \|7 (dpeaa)DE-He213
650		4	\|a Early Integration \|7 (dpeaa)DE-He213
650		4	\|a Perceptual Memory \|7 (dpeaa)DE-He213
700	1		\|a Zhang, Jianwei \|4 aut
700	1		\|a Zhang, Liwei \|4 aut
700	1		\|a Rockel, Sebastian \|4 aut
700	1		\|a Neumann, Bernd \|4 aut
700	1		\|a Lehmann, Jos \|4 aut
700	1		\|a Dubba, Krishna S. R. \|4 aut
700	1		\|a Cohn, Anthony G. \|4 aut
700	1		\|a Saffiotti, Alessandro \|4 aut
700	1		\|a Pecora, Federico \|4 aut
700	1		\|a Mansouri, Masoumeh \|4 aut
700	1		\|a Konečný, Štefan \|4 aut
700	1		\|a Günther, Martin \|4 aut
700	1		\|a Stock, Sebastian \|4 aut
700	1		\|a Lopes, Luis Seabra \|4 aut
700	1		\|a Oliveira, Miguel \|4 aut
700	1		\|a Lim, Gi Hyun \|4 aut
700	1		\|a Kasaei, Hamidreza \|4 aut
700	1		\|a Mokhtari, Vahid \|4 aut
700	1		\|a Hotz, Lothar \|4 aut
700	1		\|a Bohlken, Wilfried \|4 aut
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912			\|a GBV_ILN_2015
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912			\|a GBV_ILN_2039
912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
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912			\|a GBV_ILN_2057
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2065
912			\|a GBV_ILN_2068
912			\|a GBV_ILN_2070
912			\|a GBV_ILN_2086
912			\|a GBV_ILN_2088
912			\|a GBV_ILN_2093
912			\|a GBV_ILN_2106
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951			\|a AR
952			\|d 28 \|j 2014 \|e 4 \|b 21 \|c 09 \|h 297-304

Indexfelder

author_variant	j h jh j z jz l z lz s r sr b n bn j l jl k s r d ksr ksrd a g c ag agc a s as f p fp m m mm š k šk m g mg s s ss l s l ls lsl m o mo g h l gh ghl h k hk v m vm l h lh w b wb
matchkey_str	article:16101987:2014----::hrcp
hierarchy_sort_str	2014
publishDate	2014
allfields	10.1007/s13218-014-0327-y doi (DE-627)SPR030894921 (SPR)s13218-014-0327-y-e DE-627 ger DE-627 rakwb eng Hertzberg, Joachim verfasserin aut The RACE Project 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2014 Abstract This paper reports on the aims, the approach, and the results of the European project RACE. The project aim was to enhance the behavior of an autonomous robot by having the robot learn from conceptualized experiences of previous performance, based on initial models of the domain and its own actions in it. This paper introduces the general system architecture; it then sketches some results in detail regarding hybrid reasoning and planning used in RACE, and instances of learning from the experiences of real robot task execution. Enhancement of robot competence is operationalized in terms of performance quality and description length of the robot instructions, and such enhancement is shown to result from the RACE system. Object Category (dpeaa)DE-He213 Control Architecture (dpeaa)DE-He213 Plan Execution (dpeaa)DE-He213 Early Integration (dpeaa)DE-He213 Perceptual Memory (dpeaa)DE-He213 Zhang, Jianwei aut Zhang, Liwei aut Rockel, Sebastian aut Neumann, Bernd aut Lehmann, Jos aut Dubba, Krishna S. R. aut Cohn, Anthony G. aut Saffiotti, Alessandro aut Pecora, Federico aut Mansouri, Masoumeh aut Konečný, Štefan aut Günther, Martin aut Stock, Sebastian aut Lopes, Luis Seabra aut Oliveira, Miguel aut Lim, Gi Hyun aut Kasaei, Hamidreza aut Mokhtari, Vahid aut Hotz, Lothar aut Bohlken, Wilfried aut Enthalten in Künstliche Intelligenz Berlin : Springer, 2010 28(2014), 4 vom: 21. Sept., Seite 297-304 (DE-627)618327665 (DE-600)2537719-X 1610-1987 nnns volume:28 year:2014 number:4 day:21 month:09 pages:297-304 https://dx.doi.org/10.1007/s13218-014-0327-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 28 2014 4 21 09 297-304
spelling	10.1007/s13218-014-0327-y doi (DE-627)SPR030894921 (SPR)s13218-014-0327-y-e DE-627 ger DE-627 rakwb eng Hertzberg, Joachim verfasserin aut The RACE Project 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2014 Abstract This paper reports on the aims, the approach, and the results of the European project RACE. The project aim was to enhance the behavior of an autonomous robot by having the robot learn from conceptualized experiences of previous performance, based on initial models of the domain and its own actions in it. This paper introduces the general system architecture; it then sketches some results in detail regarding hybrid reasoning and planning used in RACE, and instances of learning from the experiences of real robot task execution. Enhancement of robot competence is operationalized in terms of performance quality and description length of the robot instructions, and such enhancement is shown to result from the RACE system. Object Category (dpeaa)DE-He213 Control Architecture (dpeaa)DE-He213 Plan Execution (dpeaa)DE-He213 Early Integration (dpeaa)DE-He213 Perceptual Memory (dpeaa)DE-He213 Zhang, Jianwei aut Zhang, Liwei aut Rockel, Sebastian aut Neumann, Bernd aut Lehmann, Jos aut Dubba, Krishna S. R. aut Cohn, Anthony G. aut Saffiotti, Alessandro aut Pecora, Federico aut Mansouri, Masoumeh aut Konečný, Štefan aut Günther, Martin aut Stock, Sebastian aut Lopes, Luis Seabra aut Oliveira, Miguel aut Lim, Gi Hyun aut Kasaei, Hamidreza aut Mokhtari, Vahid aut Hotz, Lothar aut Bohlken, Wilfried aut Enthalten in Künstliche Intelligenz Berlin : Springer, 2010 28(2014), 4 vom: 21. Sept., Seite 297-304 (DE-627)618327665 (DE-600)2537719-X 1610-1987 nnns volume:28 year:2014 number:4 day:21 month:09 pages:297-304 https://dx.doi.org/10.1007/s13218-014-0327-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 28 2014 4 21 09 297-304
allfields_unstemmed	10.1007/s13218-014-0327-y doi (DE-627)SPR030894921 (SPR)s13218-014-0327-y-e DE-627 ger DE-627 rakwb eng Hertzberg, Joachim verfasserin aut The RACE Project 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2014 Abstract This paper reports on the aims, the approach, and the results of the European project RACE. The project aim was to enhance the behavior of an autonomous robot by having the robot learn from conceptualized experiences of previous performance, based on initial models of the domain and its own actions in it. This paper introduces the general system architecture; it then sketches some results in detail regarding hybrid reasoning and planning used in RACE, and instances of learning from the experiences of real robot task execution. Enhancement of robot competence is operationalized in terms of performance quality and description length of the robot instructions, and such enhancement is shown to result from the RACE system. Object Category (dpeaa)DE-He213 Control Architecture (dpeaa)DE-He213 Plan Execution (dpeaa)DE-He213 Early Integration (dpeaa)DE-He213 Perceptual Memory (dpeaa)DE-He213 Zhang, Jianwei aut Zhang, Liwei aut Rockel, Sebastian aut Neumann, Bernd aut Lehmann, Jos aut Dubba, Krishna S. R. aut Cohn, Anthony G. aut Saffiotti, Alessandro aut Pecora, Federico aut Mansouri, Masoumeh aut Konečný, Štefan aut Günther, Martin aut Stock, Sebastian aut Lopes, Luis Seabra aut Oliveira, Miguel aut Lim, Gi Hyun aut Kasaei, Hamidreza aut Mokhtari, Vahid aut Hotz, Lothar aut Bohlken, Wilfried aut Enthalten in Künstliche Intelligenz Berlin : Springer, 2010 28(2014), 4 vom: 21. Sept., Seite 297-304 (DE-627)618327665 (DE-600)2537719-X 1610-1987 nnns volume:28 year:2014 number:4 day:21 month:09 pages:297-304 https://dx.doi.org/10.1007/s13218-014-0327-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 28 2014 4 21 09 297-304
allfieldsGer	10.1007/s13218-014-0327-y doi (DE-627)SPR030894921 (SPR)s13218-014-0327-y-e DE-627 ger DE-627 rakwb eng Hertzberg, Joachim verfasserin aut The RACE Project 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2014 Abstract This paper reports on the aims, the approach, and the results of the European project RACE. The project aim was to enhance the behavior of an autonomous robot by having the robot learn from conceptualized experiences of previous performance, based on initial models of the domain and its own actions in it. This paper introduces the general system architecture; it then sketches some results in detail regarding hybrid reasoning and planning used in RACE, and instances of learning from the experiences of real robot task execution. Enhancement of robot competence is operationalized in terms of performance quality and description length of the robot instructions, and such enhancement is shown to result from the RACE system. Object Category (dpeaa)DE-He213 Control Architecture (dpeaa)DE-He213 Plan Execution (dpeaa)DE-He213 Early Integration (dpeaa)DE-He213 Perceptual Memory (dpeaa)DE-He213 Zhang, Jianwei aut Zhang, Liwei aut Rockel, Sebastian aut Neumann, Bernd aut Lehmann, Jos aut Dubba, Krishna S. R. aut Cohn, Anthony G. aut Saffiotti, Alessandro aut Pecora, Federico aut Mansouri, Masoumeh aut Konečný, Štefan aut Günther, Martin aut Stock, Sebastian aut Lopes, Luis Seabra aut Oliveira, Miguel aut Lim, Gi Hyun aut Kasaei, Hamidreza aut Mokhtari, Vahid aut Hotz, Lothar aut Bohlken, Wilfried aut Enthalten in Künstliche Intelligenz Berlin : Springer, 2010 28(2014), 4 vom: 21. Sept., Seite 297-304 (DE-627)618327665 (DE-600)2537719-X 1610-1987 nnns volume:28 year:2014 number:4 day:21 month:09 pages:297-304 https://dx.doi.org/10.1007/s13218-014-0327-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 28 2014 4 21 09 297-304
allfieldsSound	10.1007/s13218-014-0327-y doi (DE-627)SPR030894921 (SPR)s13218-014-0327-y-e DE-627 ger DE-627 rakwb eng Hertzberg, Joachim verfasserin aut The RACE Project 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2014 Abstract This paper reports on the aims, the approach, and the results of the European project RACE. The project aim was to enhance the behavior of an autonomous robot by having the robot learn from conceptualized experiences of previous performance, based on initial models of the domain and its own actions in it. This paper introduces the general system architecture; it then sketches some results in detail regarding hybrid reasoning and planning used in RACE, and instances of learning from the experiences of real robot task execution. Enhancement of robot competence is operationalized in terms of performance quality and description length of the robot instructions, and such enhancement is shown to result from the RACE system. Object Category (dpeaa)DE-He213 Control Architecture (dpeaa)DE-He213 Plan Execution (dpeaa)DE-He213 Early Integration (dpeaa)DE-He213 Perceptual Memory (dpeaa)DE-He213 Zhang, Jianwei aut Zhang, Liwei aut Rockel, Sebastian aut Neumann, Bernd aut Lehmann, Jos aut Dubba, Krishna S. R. aut Cohn, Anthony G. aut Saffiotti, Alessandro aut Pecora, Federico aut Mansouri, Masoumeh aut Konečný, Štefan aut Günther, Martin aut Stock, Sebastian aut Lopes, Luis Seabra aut Oliveira, Miguel aut Lim, Gi Hyun aut Kasaei, Hamidreza aut Mokhtari, Vahid aut Hotz, Lothar aut Bohlken, Wilfried aut Enthalten in Künstliche Intelligenz Berlin : Springer, 2010 28(2014), 4 vom: 21. Sept., Seite 297-304 (DE-627)618327665 (DE-600)2537719-X 1610-1987 nnns volume:28 year:2014 number:4 day:21 month:09 pages:297-304 https://dx.doi.org/10.1007/s13218-014-0327-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 28 2014 4 21 09 297-304
language	English
source	Enthalten in Künstliche Intelligenz 28(2014), 4 vom: 21. Sept., Seite 297-304 volume:28 year:2014 number:4 day:21 month:09 pages:297-304
sourceStr	Enthalten in Künstliche Intelligenz 28(2014), 4 vom: 21. Sept., Seite 297-304 volume:28 year:2014 number:4 day:21 month:09 pages:297-304
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Object Category Control Architecture Plan Execution Early Integration Perceptual Memory
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container_title	Künstliche Intelligenz
authorswithroles_txt_mv	Hertzberg, Joachim @@aut@@ Zhang, Jianwei @@aut@@ Zhang, Liwei @@aut@@ Rockel, Sebastian @@aut@@ Neumann, Bernd @@aut@@ Lehmann, Jos @@aut@@ Dubba, Krishna S. R. @@aut@@ Cohn, Anthony G. @@aut@@ Saffiotti, Alessandro @@aut@@ Pecora, Federico @@aut@@ Mansouri, Masoumeh @@aut@@ Konečný, Štefan @@aut@@ Günther, Martin @@aut@@ Stock, Sebastian @@aut@@ Lopes, Luis Seabra @@aut@@ Oliveira, Miguel @@aut@@ Lim, Gi Hyun @@aut@@ Kasaei, Hamidreza @@aut@@ Mokhtari, Vahid @@aut@@ Hotz, Lothar @@aut@@ Bohlken, Wilfried @@aut@@
publishDateDaySort_date	2014-09-21T00:00:00Z
hierarchy_top_id	618327665
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language_de	englisch
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The project aim was to enhance the behavior of an autonomous robot by having the robot learn from conceptualized experiences of previous performance, based on initial models of the domain and its own actions in it. This paper introduces the general system architecture; it then sketches some results in detail regarding hybrid reasoning and planning used in RACE, and instances of learning from the experiences of real robot task execution. 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author	Hertzberg, Joachim
spellingShingle	Hertzberg, Joachim misc Object Category misc Control Architecture misc Plan Execution misc Early Integration misc Perceptual Memory The RACE Project
authorStr	Hertzberg, Joachim
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topic_title	The RACE Project Object Category (dpeaa)DE-He213 Control Architecture (dpeaa)DE-He213 Plan Execution (dpeaa)DE-He213 Early Integration (dpeaa)DE-He213 Perceptual Memory (dpeaa)DE-He213
topic	misc Object Category misc Control Architecture misc Plan Execution misc Early Integration misc Perceptual Memory
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title	The RACE Project
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title_full	The RACE Project
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author_browse	Hertzberg, Joachim Zhang, Jianwei Zhang, Liwei Rockel, Sebastian Neumann, Bernd Lehmann, Jos Dubba, Krishna S. R. Cohn, Anthony G. Saffiotti, Alessandro Pecora, Federico Mansouri, Masoumeh Konečný, Štefan Günther, Martin Stock, Sebastian Lopes, Luis Seabra Oliveira, Miguel Lim, Gi Hyun Kasaei, Hamidreza Mokhtari, Vahid Hotz, Lothar Bohlken, Wilfried
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format_se	Elektronische Aufsätze
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abstract	Abstract This paper reports on the aims, the approach, and the results of the European project RACE. The project aim was to enhance the behavior of an autonomous robot by having the robot learn from conceptualized experiences of previous performance, based on initial models of the domain and its own actions in it. This paper introduces the general system architecture; it then sketches some results in detail regarding hybrid reasoning and planning used in RACE, and instances of learning from the experiences of real robot task execution. Enhancement of robot competence is operationalized in terms of performance quality and description length of the robot instructions, and such enhancement is shown to result from the RACE system. © Springer-Verlag Berlin Heidelberg 2014
abstractGer	Abstract This paper reports on the aims, the approach, and the results of the European project RACE. The project aim was to enhance the behavior of an autonomous robot by having the robot learn from conceptualized experiences of previous performance, based on initial models of the domain and its own actions in it. This paper introduces the general system architecture; it then sketches some results in detail regarding hybrid reasoning and planning used in RACE, and instances of learning from the experiences of real robot task execution. Enhancement of robot competence is operationalized in terms of performance quality and description length of the robot instructions, and such enhancement is shown to result from the RACE system. © Springer-Verlag Berlin Heidelberg 2014
abstract_unstemmed	Abstract This paper reports on the aims, the approach, and the results of the European project RACE. The project aim was to enhance the behavior of an autonomous robot by having the robot learn from conceptualized experiences of previous performance, based on initial models of the domain and its own actions in it. This paper introduces the general system architecture; it then sketches some results in detail regarding hybrid reasoning and planning used in RACE, and instances of learning from the experiences of real robot task execution. Enhancement of robot competence is operationalized in terms of performance quality and description length of the robot instructions, and such enhancement is shown to result from the RACE system. © Springer-Verlag Berlin Heidelberg 2014
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title_short	The RACE Project
url	https://dx.doi.org/10.1007/s13218-014-0327-y
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author2	Zhang, Jianwei Zhang, Liwei Rockel, Sebastian Neumann, Bernd Lehmann, Jos Dubba, Krishna S. R. Cohn, Anthony G. Saffiotti, Alessandro Pecora, Federico Mansouri, Masoumeh Konečný, Štefan Günther, Martin Stock, Sebastian Lopes, Luis Seabra Oliveira, Miguel Lim, Gi Hyun Kasaei, Hamidreza Mokhtari, Vahid Hotz, Lothar Bohlken, Wilfried
author2Str	Zhang, Jianwei Zhang, Liwei Rockel, Sebastian Neumann, Bernd Lehmann, Jos Dubba, Krishna S. R. Cohn, Anthony G. Saffiotti, Alessandro Pecora, Federico Mansouri, Masoumeh Konečný, Štefan Günther, Martin Stock, Sebastian Lopes, Luis Seabra Oliveira, Miguel Lim, Gi Hyun Kasaei, Hamidreza Mokhtari, Vahid Hotz, Lothar Bohlken, Wilfried
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up_date	2024-07-03T20:46:00.979Z
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score	7.399585

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