Automatic parameter learning method for agent activation spreading network by evolutionary computation

Abstract A variety of planning research is being actively conducted in multiple research fields. The focus of these studies is to flexibly utilize both immediate and deliberative planning in response to the environment and to adaptively prioritize multiple goals and actions in a human-like manner. T...
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Autor*in:	Shimokawa, Daiki [verfasserIn] Yoshida, Naoto Koyama, Shuzo Kurihara, Satoshi

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Artificial intelligence Multi-agent systems Evolutionary computations Swarm intelligence

Anmerkung:	© The Author(s) 2023

Übergeordnetes Werk:	Enthalten in: Artificial life and robotics - Berlin [u.a.] : Springer, 1997, 28(2023), 3 vom: 17. Apr., Seite 571-582
Übergeordnetes Werk:	volume:28 ; year:2023 ; number:3 ; day:17 ; month:04 ; pages:571-582

Links:	Volltext

DOI / URN:	10.1007/s10015-023-00873-z

Katalog-ID:	SPR052315665

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allfields	10.1007/s10015-023-00873-z doi (DE-627)SPR052315665 (SPR)s10015-023-00873-z-e DE-627 ger DE-627 rakwb eng Shimokawa, Daiki verfasserin aut Automatic parameter learning method for agent activation spreading network by evolutionary computation 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract A variety of planning research is being actively conducted in multiple research fields. The focus of these studies is to flexibly utilize both immediate and deliberative planning in response to the environment and to adaptively prioritize multiple goals and actions in a human-like manner. To achieve this, a method that applies active propagation to multi-agent planning (agent activation spreading network) has been proposed and is being utilized in various research fields. Furthermore, with the recent development of large-scale artificial intelligence models, we should soon be able to incorporate tacit human knowledge into this architecture. However, there is not yet a method for adjusting the parameters in this architecture which creates a barrier to future extension. In response, we have developed a method for automatically adjusting the parameters using evolutionary computation. Our experimental results showed that (1) the proposed method enables a higher degree of adaptation, thanks to taking the agent’s semantics into account, and (2) it is possible to obtain parameters that are appropriate to the environment even when the experimental environment is changed. Artificial intelligence (dpeaa)DE-He213 Multi-agent systems (dpeaa)DE-He213 Evolutionary computations (dpeaa)DE-He213 Swarm intelligence (dpeaa)DE-He213 Yoshida, Naoto aut Koyama, Shuzo aut Kurihara, Satoshi aut Enthalten in Artificial life and robotics Berlin [u.a.] : Springer, 1997 28(2023), 3 vom: 17. Apr., Seite 571-582 (DE-627)271596678 (DE-600)1480655-1 1614-7456 nnns volume:28 year:2023 number:3 day:17 month:04 pages:571-582 https://dx.doi.org/10.1007/s10015-023-00873-z kostenfrei 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_4126 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 28 2023 3 17 04 571-582
spelling	10.1007/s10015-023-00873-z doi (DE-627)SPR052315665 (SPR)s10015-023-00873-z-e DE-627 ger DE-627 rakwb eng Shimokawa, Daiki verfasserin aut Automatic parameter learning method for agent activation spreading network by evolutionary computation 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract A variety of planning research is being actively conducted in multiple research fields. The focus of these studies is to flexibly utilize both immediate and deliberative planning in response to the environment and to adaptively prioritize multiple goals and actions in a human-like manner. To achieve this, a method that applies active propagation to multi-agent planning (agent activation spreading network) has been proposed and is being utilized in various research fields. Furthermore, with the recent development of large-scale artificial intelligence models, we should soon be able to incorporate tacit human knowledge into this architecture. However, there is not yet a method for adjusting the parameters in this architecture which creates a barrier to future extension. In response, we have developed a method for automatically adjusting the parameters using evolutionary computation. Our experimental results showed that (1) the proposed method enables a higher degree of adaptation, thanks to taking the agent’s semantics into account, and (2) it is possible to obtain parameters that are appropriate to the environment even when the experimental environment is changed. Artificial intelligence (dpeaa)DE-He213 Multi-agent systems (dpeaa)DE-He213 Evolutionary computations (dpeaa)DE-He213 Swarm intelligence (dpeaa)DE-He213 Yoshida, Naoto aut Koyama, Shuzo aut Kurihara, Satoshi aut Enthalten in Artificial life and robotics Berlin [u.a.] : Springer, 1997 28(2023), 3 vom: 17. Apr., Seite 571-582 (DE-627)271596678 (DE-600)1480655-1 1614-7456 nnns volume:28 year:2023 number:3 day:17 month:04 pages:571-582 https://dx.doi.org/10.1007/s10015-023-00873-z kostenfrei 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_4126 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 28 2023 3 17 04 571-582
allfields_unstemmed	10.1007/s10015-023-00873-z doi (DE-627)SPR052315665 (SPR)s10015-023-00873-z-e DE-627 ger DE-627 rakwb eng Shimokawa, Daiki verfasserin aut Automatic parameter learning method for agent activation spreading network by evolutionary computation 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract A variety of planning research is being actively conducted in multiple research fields. The focus of these studies is to flexibly utilize both immediate and deliberative planning in response to the environment and to adaptively prioritize multiple goals and actions in a human-like manner. To achieve this, a method that applies active propagation to multi-agent planning (agent activation spreading network) has been proposed and is being utilized in various research fields. Furthermore, with the recent development of large-scale artificial intelligence models, we should soon be able to incorporate tacit human knowledge into this architecture. However, there is not yet a method for adjusting the parameters in this architecture which creates a barrier to future extension. In response, we have developed a method for automatically adjusting the parameters using evolutionary computation. Our experimental results showed that (1) the proposed method enables a higher degree of adaptation, thanks to taking the agent’s semantics into account, and (2) it is possible to obtain parameters that are appropriate to the environment even when the experimental environment is changed. Artificial intelligence (dpeaa)DE-He213 Multi-agent systems (dpeaa)DE-He213 Evolutionary computations (dpeaa)DE-He213 Swarm intelligence (dpeaa)DE-He213 Yoshida, Naoto aut Koyama, Shuzo aut Kurihara, Satoshi aut Enthalten in Artificial life and robotics Berlin [u.a.] : Springer, 1997 28(2023), 3 vom: 17. Apr., Seite 571-582 (DE-627)271596678 (DE-600)1480655-1 1614-7456 nnns volume:28 year:2023 number:3 day:17 month:04 pages:571-582 https://dx.doi.org/10.1007/s10015-023-00873-z kostenfrei 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_4126 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 28 2023 3 17 04 571-582
allfieldsGer	10.1007/s10015-023-00873-z doi (DE-627)SPR052315665 (SPR)s10015-023-00873-z-e DE-627 ger DE-627 rakwb eng Shimokawa, Daiki verfasserin aut Automatic parameter learning method for agent activation spreading network by evolutionary computation 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract A variety of planning research is being actively conducted in multiple research fields. The focus of these studies is to flexibly utilize both immediate and deliberative planning in response to the environment and to adaptively prioritize multiple goals and actions in a human-like manner. To achieve this, a method that applies active propagation to multi-agent planning (agent activation spreading network) has been proposed and is being utilized in various research fields. Furthermore, with the recent development of large-scale artificial intelligence models, we should soon be able to incorporate tacit human knowledge into this architecture. However, there is not yet a method for adjusting the parameters in this architecture which creates a barrier to future extension. In response, we have developed a method for automatically adjusting the parameters using evolutionary computation. Our experimental results showed that (1) the proposed method enables a higher degree of adaptation, thanks to taking the agent’s semantics into account, and (2) it is possible to obtain parameters that are appropriate to the environment even when the experimental environment is changed. Artificial intelligence (dpeaa)DE-He213 Multi-agent systems (dpeaa)DE-He213 Evolutionary computations (dpeaa)DE-He213 Swarm intelligence (dpeaa)DE-He213 Yoshida, Naoto aut Koyama, Shuzo aut Kurihara, Satoshi aut Enthalten in Artificial life and robotics Berlin [u.a.] : Springer, 1997 28(2023), 3 vom: 17. Apr., Seite 571-582 (DE-627)271596678 (DE-600)1480655-1 1614-7456 nnns volume:28 year:2023 number:3 day:17 month:04 pages:571-582 https://dx.doi.org/10.1007/s10015-023-00873-z kostenfrei 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_4126 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 28 2023 3 17 04 571-582
allfieldsSound	10.1007/s10015-023-00873-z doi (DE-627)SPR052315665 (SPR)s10015-023-00873-z-e DE-627 ger DE-627 rakwb eng Shimokawa, Daiki verfasserin aut Automatic parameter learning method for agent activation spreading network by evolutionary computation 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract A variety of planning research is being actively conducted in multiple research fields. The focus of these studies is to flexibly utilize both immediate and deliberative planning in response to the environment and to adaptively prioritize multiple goals and actions in a human-like manner. To achieve this, a method that applies active propagation to multi-agent planning (agent activation spreading network) has been proposed and is being utilized in various research fields. Furthermore, with the recent development of large-scale artificial intelligence models, we should soon be able to incorporate tacit human knowledge into this architecture. However, there is not yet a method for adjusting the parameters in this architecture which creates a barrier to future extension. In response, we have developed a method for automatically adjusting the parameters using evolutionary computation. Our experimental results showed that (1) the proposed method enables a higher degree of adaptation, thanks to taking the agent’s semantics into account, and (2) it is possible to obtain parameters that are appropriate to the environment even when the experimental environment is changed. Artificial intelligence (dpeaa)DE-He213 Multi-agent systems (dpeaa)DE-He213 Evolutionary computations (dpeaa)DE-He213 Swarm intelligence (dpeaa)DE-He213 Yoshida, Naoto aut Koyama, Shuzo aut Kurihara, Satoshi aut Enthalten in Artificial life and robotics Berlin [u.a.] : Springer, 1997 28(2023), 3 vom: 17. Apr., Seite 571-582 (DE-627)271596678 (DE-600)1480655-1 1614-7456 nnns volume:28 year:2023 number:3 day:17 month:04 pages:571-582 https://dx.doi.org/10.1007/s10015-023-00873-z kostenfrei 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_4126 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 28 2023 3 17 04 571-582
language	English
source	Enthalten in Artificial life and robotics 28(2023), 3 vom: 17. Apr., Seite 571-582 volume:28 year:2023 number:3 day:17 month:04 pages:571-582
sourceStr	Enthalten in Artificial life and robotics 28(2023), 3 vom: 17. Apr., Seite 571-582 volume:28 year:2023 number:3 day:17 month:04 pages:571-582
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Artificial intelligence Multi-agent systems Evolutionary computations Swarm intelligence
isfreeaccess_bool	true
container_title	Artificial life and robotics
authorswithroles_txt_mv	Shimokawa, Daiki @@aut@@ Yoshida, Naoto @@aut@@ Koyama, Shuzo @@aut@@ Kurihara, Satoshi @@aut@@
publishDateDaySort_date	2023-04-17T00:00:00Z
hierarchy_top_id	271596678
id	SPR052315665
language_de	englisch
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author	Shimokawa, Daiki
spellingShingle	Shimokawa, Daiki misc Artificial intelligence misc Multi-agent systems misc Evolutionary computations misc Swarm intelligence Automatic parameter learning method for agent activation spreading network by evolutionary computation
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topic_title	Automatic parameter learning method for agent activation spreading network by evolutionary computation Artificial intelligence (dpeaa)DE-He213 Multi-agent systems (dpeaa)DE-He213 Evolutionary computations (dpeaa)DE-He213 Swarm intelligence (dpeaa)DE-He213
topic	misc Artificial intelligence misc Multi-agent systems misc Evolutionary computations misc Swarm intelligence
topic_unstemmed	misc Artificial intelligence misc Multi-agent systems misc Evolutionary computations misc Swarm intelligence
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title	Automatic parameter learning method for agent activation spreading network by evolutionary computation
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title_full	Automatic parameter learning method for agent activation spreading network by evolutionary computation
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author_browse	Shimokawa, Daiki Yoshida, Naoto Koyama, Shuzo Kurihara, Satoshi
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author-letter	Shimokawa, Daiki
doi_str_mv	10.1007/s10015-023-00873-z
title_sort	automatic parameter learning method for agent activation spreading network by evolutionary computation
title_auth	Automatic parameter learning method for agent activation spreading network by evolutionary computation
abstract	Abstract A variety of planning research is being actively conducted in multiple research fields. The focus of these studies is to flexibly utilize both immediate and deliberative planning in response to the environment and to adaptively prioritize multiple goals and actions in a human-like manner. To achieve this, a method that applies active propagation to multi-agent planning (agent activation spreading network) has been proposed and is being utilized in various research fields. Furthermore, with the recent development of large-scale artificial intelligence models, we should soon be able to incorporate tacit human knowledge into this architecture. However, there is not yet a method for adjusting the parameters in this architecture which creates a barrier to future extension. In response, we have developed a method for automatically adjusting the parameters using evolutionary computation. Our experimental results showed that (1) the proposed method enables a higher degree of adaptation, thanks to taking the agent’s semantics into account, and (2) it is possible to obtain parameters that are appropriate to the environment even when the experimental environment is changed. © The Author(s) 2023
abstractGer	Abstract A variety of planning research is being actively conducted in multiple research fields. The focus of these studies is to flexibly utilize both immediate and deliberative planning in response to the environment and to adaptively prioritize multiple goals and actions in a human-like manner. To achieve this, a method that applies active propagation to multi-agent planning (agent activation spreading network) has been proposed and is being utilized in various research fields. Furthermore, with the recent development of large-scale artificial intelligence models, we should soon be able to incorporate tacit human knowledge into this architecture. However, there is not yet a method for adjusting the parameters in this architecture which creates a barrier to future extension. In response, we have developed a method for automatically adjusting the parameters using evolutionary computation. Our experimental results showed that (1) the proposed method enables a higher degree of adaptation, thanks to taking the agent’s semantics into account, and (2) it is possible to obtain parameters that are appropriate to the environment even when the experimental environment is changed. © The Author(s) 2023
abstract_unstemmed	Abstract A variety of planning research is being actively conducted in multiple research fields. The focus of these studies is to flexibly utilize both immediate and deliberative planning in response to the environment and to adaptively prioritize multiple goals and actions in a human-like manner. To achieve this, a method that applies active propagation to multi-agent planning (agent activation spreading network) has been proposed and is being utilized in various research fields. Furthermore, with the recent development of large-scale artificial intelligence models, we should soon be able to incorporate tacit human knowledge into this architecture. However, there is not yet a method for adjusting the parameters in this architecture which creates a barrier to future extension. In response, we have developed a method for automatically adjusting the parameters using evolutionary computation. Our experimental results showed that (1) the proposed method enables a higher degree of adaptation, thanks to taking the agent’s semantics into account, and (2) it is possible to obtain parameters that are appropriate to the environment even when the experimental environment is changed. © The Author(s) 2023
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container_issue	3
title_short	Automatic parameter learning method for agent activation spreading network by evolutionary computation
url	https://dx.doi.org/10.1007/s10015-023-00873-z
remote_bool	true
author2	Yoshida, Naoto Koyama, Shuzo Kurihara, Satoshi
author2Str	Yoshida, Naoto Koyama, Shuzo Kurihara, Satoshi
ppnlink	271596678
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isOA_txt	true
hochschulschrift_bool	false
doi_str	10.1007/s10015-023-00873-z
up_date	2024-07-04T02:19:40.881Z
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Automatic parameter learning method for agent activation spreading network by evolutionary computation

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