Flexible Latching: A Biologically-Inspired Mechanism for Improving the Management of Homeostatic Goals

Abstract Controlling cognitive systems like domestic robots or intelligent assistive environments requires striking an appropriate balance between responsiveness and persistence. Basic goal arbitration is an essential element of low level action selection for cognitive systems, necessarily preceding...
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Gespeichert in:

Autor*in:	Rohlfshagen, Philipp [verfasserIn] Bryson, Joanna J.

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2010

Schlagwörter:	Action selection Drives Modularity Cognitive architectures

Anmerkung:	© Springer Science+Business Media, LLC 2010

Übergeordnetes Werk:	Enthalten in: Cognitive Computation - New York, NY : Springer, 2009, 2(2010), 3 vom: 17. Juli, Seite 230-241
Übergeordnetes Werk:	volume:2 ; year:2010 ; number:3 ; day:17 ; month:07 ; pages:230-241

Links:	Volltext

DOI / URN:	10.1007/s12559-010-9057-0

Katalog-ID:	SPR026472449

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520			\|a Abstract Controlling cognitive systems like domestic robots or intelligent assistive environments requires striking an appropriate balance between responsiveness and persistence. Basic goal arbitration is an essential element of low level action selection for cognitive systems, necessarily preceding even deliberate control in the direction of attention. In natural intelligence, chemically regulated motivation systems focus an agent’s behavioural attention on one problem at a time. Such simple durative decision state can improve the efficiency of artificial action selection by avoiding dithering, but taken to extremes such systems can be inefficient and produce cognitively implausible results. This article describes and demonstrates an easy-to-implement, general-purpose latching method that allows for a balance between persistence and flexibility in the presence of interruptions. This appraisal-based system facilitates automatic reassessment of the current focus of attention by existing action-selection mechanisms. The proposed mechanism, flexible latching, drastically improves efficiency in handling multiple competing goals at the cost of a surprisingly small amount of additional code (or cognitive) complexity. We discuss implications of these results for understanding natural cognitive systems.
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allfields	10.1007/s12559-010-9057-0 doi (DE-627)SPR026472449 (SPR)s12559-010-9057-0-e DE-627 ger DE-627 rakwb eng Rohlfshagen, Philipp verfasserin aut Flexible Latching: A Biologically-Inspired Mechanism for Improving the Management of Homeostatic Goals 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media, LLC 2010 Abstract Controlling cognitive systems like domestic robots or intelligent assistive environments requires striking an appropriate balance between responsiveness and persistence. Basic goal arbitration is an essential element of low level action selection for cognitive systems, necessarily preceding even deliberate control in the direction of attention. In natural intelligence, chemically regulated motivation systems focus an agent’s behavioural attention on one problem at a time. Such simple durative decision state can improve the efficiency of artificial action selection by avoiding dithering, but taken to extremes such systems can be inefficient and produce cognitively implausible results. This article describes and demonstrates an easy-to-implement, general-purpose latching method that allows for a balance between persistence and flexibility in the presence of interruptions. This appraisal-based system facilitates automatic reassessment of the current focus of attention by existing action-selection mechanisms. The proposed mechanism, flexible latching, drastically improves efficiency in handling multiple competing goals at the cost of a surprisingly small amount of additional code (or cognitive) complexity. We discuss implications of these results for understanding natural cognitive systems. Action selection (dpeaa)DE-He213 Drives (dpeaa)DE-He213 Modularity (dpeaa)DE-He213 Cognitive architectures (dpeaa)DE-He213 Bryson, Joanna J. aut Enthalten in Cognitive Computation New York, NY : Springer, 2009 2(2010), 3 vom: 17. Juli, Seite 230-241 (DE-627)594779472 (DE-600)2486574-6 1866-9964 nnns volume:2 year:2010 number:3 day:17 month:07 pages:230-241 https://dx.doi.org/10.1007/s12559-010-9057-0 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_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_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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 2 2010 3 17 07 230-241
spelling	10.1007/s12559-010-9057-0 doi (DE-627)SPR026472449 (SPR)s12559-010-9057-0-e DE-627 ger DE-627 rakwb eng Rohlfshagen, Philipp verfasserin aut Flexible Latching: A Biologically-Inspired Mechanism for Improving the Management of Homeostatic Goals 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media, LLC 2010 Abstract Controlling cognitive systems like domestic robots or intelligent assistive environments requires striking an appropriate balance between responsiveness and persistence. Basic goal arbitration is an essential element of low level action selection for cognitive systems, necessarily preceding even deliberate control in the direction of attention. In natural intelligence, chemically regulated motivation systems focus an agent’s behavioural attention on one problem at a time. Such simple durative decision state can improve the efficiency of artificial action selection by avoiding dithering, but taken to extremes such systems can be inefficient and produce cognitively implausible results. This article describes and demonstrates an easy-to-implement, general-purpose latching method that allows for a balance between persistence and flexibility in the presence of interruptions. This appraisal-based system facilitates automatic reassessment of the current focus of attention by existing action-selection mechanisms. The proposed mechanism, flexible latching, drastically improves efficiency in handling multiple competing goals at the cost of a surprisingly small amount of additional code (or cognitive) complexity. We discuss implications of these results for understanding natural cognitive systems. Action selection (dpeaa)DE-He213 Drives (dpeaa)DE-He213 Modularity (dpeaa)DE-He213 Cognitive architectures (dpeaa)DE-He213 Bryson, Joanna J. aut Enthalten in Cognitive Computation New York, NY : Springer, 2009 2(2010), 3 vom: 17. Juli, Seite 230-241 (DE-627)594779472 (DE-600)2486574-6 1866-9964 nnns volume:2 year:2010 number:3 day:17 month:07 pages:230-241 https://dx.doi.org/10.1007/s12559-010-9057-0 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_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_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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 2 2010 3 17 07 230-241
allfields_unstemmed	10.1007/s12559-010-9057-0 doi (DE-627)SPR026472449 (SPR)s12559-010-9057-0-e DE-627 ger DE-627 rakwb eng Rohlfshagen, Philipp verfasserin aut Flexible Latching: A Biologically-Inspired Mechanism for Improving the Management of Homeostatic Goals 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media, LLC 2010 Abstract Controlling cognitive systems like domestic robots or intelligent assistive environments requires striking an appropriate balance between responsiveness and persistence. Basic goal arbitration is an essential element of low level action selection for cognitive systems, necessarily preceding even deliberate control in the direction of attention. In natural intelligence, chemically regulated motivation systems focus an agent’s behavioural attention on one problem at a time. Such simple durative decision state can improve the efficiency of artificial action selection by avoiding dithering, but taken to extremes such systems can be inefficient and produce cognitively implausible results. This article describes and demonstrates an easy-to-implement, general-purpose latching method that allows for a balance between persistence and flexibility in the presence of interruptions. This appraisal-based system facilitates automatic reassessment of the current focus of attention by existing action-selection mechanisms. The proposed mechanism, flexible latching, drastically improves efficiency in handling multiple competing goals at the cost of a surprisingly small amount of additional code (or cognitive) complexity. We discuss implications of these results for understanding natural cognitive systems. Action selection (dpeaa)DE-He213 Drives (dpeaa)DE-He213 Modularity (dpeaa)DE-He213 Cognitive architectures (dpeaa)DE-He213 Bryson, Joanna J. aut Enthalten in Cognitive Computation New York, NY : Springer, 2009 2(2010), 3 vom: 17. Juli, Seite 230-241 (DE-627)594779472 (DE-600)2486574-6 1866-9964 nnns volume:2 year:2010 number:3 day:17 month:07 pages:230-241 https://dx.doi.org/10.1007/s12559-010-9057-0 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_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_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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 2 2010 3 17 07 230-241
allfieldsGer	10.1007/s12559-010-9057-0 doi (DE-627)SPR026472449 (SPR)s12559-010-9057-0-e DE-627 ger DE-627 rakwb eng Rohlfshagen, Philipp verfasserin aut Flexible Latching: A Biologically-Inspired Mechanism for Improving the Management of Homeostatic Goals 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media, LLC 2010 Abstract Controlling cognitive systems like domestic robots or intelligent assistive environments requires striking an appropriate balance between responsiveness and persistence. Basic goal arbitration is an essential element of low level action selection for cognitive systems, necessarily preceding even deliberate control in the direction of attention. In natural intelligence, chemically regulated motivation systems focus an agent’s behavioural attention on one problem at a time. Such simple durative decision state can improve the efficiency of artificial action selection by avoiding dithering, but taken to extremes such systems can be inefficient and produce cognitively implausible results. This article describes and demonstrates an easy-to-implement, general-purpose latching method that allows for a balance between persistence and flexibility in the presence of interruptions. This appraisal-based system facilitates automatic reassessment of the current focus of attention by existing action-selection mechanisms. The proposed mechanism, flexible latching, drastically improves efficiency in handling multiple competing goals at the cost of a surprisingly small amount of additional code (or cognitive) complexity. We discuss implications of these results for understanding natural cognitive systems. Action selection (dpeaa)DE-He213 Drives (dpeaa)DE-He213 Modularity (dpeaa)DE-He213 Cognitive architectures (dpeaa)DE-He213 Bryson, Joanna J. aut Enthalten in Cognitive Computation New York, NY : Springer, 2009 2(2010), 3 vom: 17. Juli, Seite 230-241 (DE-627)594779472 (DE-600)2486574-6 1866-9964 nnns volume:2 year:2010 number:3 day:17 month:07 pages:230-241 https://dx.doi.org/10.1007/s12559-010-9057-0 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_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_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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 2 2010 3 17 07 230-241
allfieldsSound	10.1007/s12559-010-9057-0 doi (DE-627)SPR026472449 (SPR)s12559-010-9057-0-e DE-627 ger DE-627 rakwb eng Rohlfshagen, Philipp verfasserin aut Flexible Latching: A Biologically-Inspired Mechanism for Improving the Management of Homeostatic Goals 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media, LLC 2010 Abstract Controlling cognitive systems like domestic robots or intelligent assistive environments requires striking an appropriate balance between responsiveness and persistence. Basic goal arbitration is an essential element of low level action selection for cognitive systems, necessarily preceding even deliberate control in the direction of attention. In natural intelligence, chemically regulated motivation systems focus an agent’s behavioural attention on one problem at a time. Such simple durative decision state can improve the efficiency of artificial action selection by avoiding dithering, but taken to extremes such systems can be inefficient and produce cognitively implausible results. This article describes and demonstrates an easy-to-implement, general-purpose latching method that allows for a balance between persistence and flexibility in the presence of interruptions. This appraisal-based system facilitates automatic reassessment of the current focus of attention by existing action-selection mechanisms. The proposed mechanism, flexible latching, drastically improves efficiency in handling multiple competing goals at the cost of a surprisingly small amount of additional code (or cognitive) complexity. We discuss implications of these results for understanding natural cognitive systems. Action selection (dpeaa)DE-He213 Drives (dpeaa)DE-He213 Modularity (dpeaa)DE-He213 Cognitive architectures (dpeaa)DE-He213 Bryson, Joanna J. aut Enthalten in Cognitive Computation New York, NY : Springer, 2009 2(2010), 3 vom: 17. Juli, Seite 230-241 (DE-627)594779472 (DE-600)2486574-6 1866-9964 nnns volume:2 year:2010 number:3 day:17 month:07 pages:230-241 https://dx.doi.org/10.1007/s12559-010-9057-0 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_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_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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 2 2010 3 17 07 230-241
language	English
source	Enthalten in Cognitive Computation 2(2010), 3 vom: 17. Juli, Seite 230-241 volume:2 year:2010 number:3 day:17 month:07 pages:230-241
sourceStr	Enthalten in Cognitive Computation 2(2010), 3 vom: 17. Juli, Seite 230-241 volume:2 year:2010 number:3 day:17 month:07 pages:230-241
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Action selection Drives Modularity Cognitive architectures
isfreeaccess_bool	false
container_title	Cognitive Computation
authorswithroles_txt_mv	Rohlfshagen, Philipp @@aut@@ Bryson, Joanna J. @@aut@@
publishDateDaySort_date	2010-07-17T00:00:00Z
hierarchy_top_id	594779472
id	SPR026472449
language_de	englisch
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author	Rohlfshagen, Philipp
spellingShingle	Rohlfshagen, Philipp misc Action selection misc Drives misc Modularity misc Cognitive architectures Flexible Latching: A Biologically-Inspired Mechanism for Improving the Management of Homeostatic Goals
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topic_title	Flexible Latching: A Biologically-Inspired Mechanism for Improving the Management of Homeostatic Goals Action selection (dpeaa)DE-He213 Drives (dpeaa)DE-He213 Modularity (dpeaa)DE-He213 Cognitive architectures (dpeaa)DE-He213
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title	Flexible Latching: A Biologically-Inspired Mechanism for Improving the Management of Homeostatic Goals
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title_full	Flexible Latching: A Biologically-Inspired Mechanism for Improving the Management of Homeostatic Goals
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title_sort	flexible latching: a biologically-inspired mechanism for improving the management of homeostatic goals
title_auth	Flexible Latching: A Biologically-Inspired Mechanism for Improving the Management of Homeostatic Goals
abstract	Abstract Controlling cognitive systems like domestic robots or intelligent assistive environments requires striking an appropriate balance between responsiveness and persistence. Basic goal arbitration is an essential element of low level action selection for cognitive systems, necessarily preceding even deliberate control in the direction of attention. In natural intelligence, chemically regulated motivation systems focus an agent’s behavioural attention on one problem at a time. Such simple durative decision state can improve the efficiency of artificial action selection by avoiding dithering, but taken to extremes such systems can be inefficient and produce cognitively implausible results. This article describes and demonstrates an easy-to-implement, general-purpose latching method that allows for a balance between persistence and flexibility in the presence of interruptions. This appraisal-based system facilitates automatic reassessment of the current focus of attention by existing action-selection mechanisms. The proposed mechanism, flexible latching, drastically improves efficiency in handling multiple competing goals at the cost of a surprisingly small amount of additional code (or cognitive) complexity. We discuss implications of these results for understanding natural cognitive systems. © Springer Science+Business Media, LLC 2010
abstractGer	Abstract Controlling cognitive systems like domestic robots or intelligent assistive environments requires striking an appropriate balance between responsiveness and persistence. Basic goal arbitration is an essential element of low level action selection for cognitive systems, necessarily preceding even deliberate control in the direction of attention. In natural intelligence, chemically regulated motivation systems focus an agent’s behavioural attention on one problem at a time. Such simple durative decision state can improve the efficiency of artificial action selection by avoiding dithering, but taken to extremes such systems can be inefficient and produce cognitively implausible results. This article describes and demonstrates an easy-to-implement, general-purpose latching method that allows for a balance between persistence and flexibility in the presence of interruptions. This appraisal-based system facilitates automatic reassessment of the current focus of attention by existing action-selection mechanisms. The proposed mechanism, flexible latching, drastically improves efficiency in handling multiple competing goals at the cost of a surprisingly small amount of additional code (or cognitive) complexity. We discuss implications of these results for understanding natural cognitive systems. © Springer Science+Business Media, LLC 2010
abstract_unstemmed	Abstract Controlling cognitive systems like domestic robots or intelligent assistive environments requires striking an appropriate balance between responsiveness and persistence. Basic goal arbitration is an essential element of low level action selection for cognitive systems, necessarily preceding even deliberate control in the direction of attention. In natural intelligence, chemically regulated motivation systems focus an agent’s behavioural attention on one problem at a time. Such simple durative decision state can improve the efficiency of artificial action selection by avoiding dithering, but taken to extremes such systems can be inefficient and produce cognitively implausible results. This article describes and demonstrates an easy-to-implement, general-purpose latching method that allows for a balance between persistence and flexibility in the presence of interruptions. This appraisal-based system facilitates automatic reassessment of the current focus of attention by existing action-selection mechanisms. The proposed mechanism, flexible latching, drastically improves efficiency in handling multiple competing goals at the cost of a surprisingly small amount of additional code (or cognitive) complexity. We discuss implications of these results for understanding natural cognitive systems. © Springer Science+Business Media, LLC 2010
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title_short	Flexible Latching: A Biologically-Inspired Mechanism for Improving the Management of Homeostatic Goals
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Basic goal arbitration is an essential element of low level action selection for cognitive systems, necessarily preceding even deliberate control in the direction of attention. In natural intelligence, chemically regulated motivation systems focus an agent’s behavioural attention on one problem at a time. Such simple durative decision state can improve the efficiency of artificial action selection by avoiding dithering, but taken to extremes such systems can be inefficient and produce cognitively implausible results. This article describes and demonstrates an easy-to-implement, general-purpose latching method that allows for a balance between persistence and flexibility in the presence of interruptions. This appraisal-based system facilitates automatic reassessment of the current focus of attention by existing action-selection mechanisms. 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