Creating herd behavior by virtual agents using neural networks
The paper focuses on simulating an artificial life in which neural networks (recurrent (RNN) and Long Short Term Memory (LSTM) networks) control prey and predator agents. The research goal was to check whether a simple genetic algorithm evolves the LSTM based controller that competes with the classi...
Gespeichert in:
| Veröffentlicht in: | Procedia computer science Jg. 192; S. 437 - 446 |
|---|---|
| Hauptverfasser: | , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Elsevier B.V
2021
|
| Schlagworte: | |
| ISSN: | 1877-0509, 1877-0509 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | The paper focuses on simulating an artificial life in which neural networks (recurrent (RNN) and Long Short Term Memory (LSTM) networks) control prey and predator agents. The research goal was to check whether a simple genetic algorithm evolves the LSTM based controller that competes with the classic RNN controller in the real world. We also examined the impact of audio communication within a given species on the survival of agents. Our experiments evidenced the LSTM network results were slightly worse than the RNN controller. We also showed that prey agents developed herd behavior in response to predator pressure. They learned to form herds that allowed them to resist predator attacks. It was also possible to observe the prey agent’s cooperation in searching for food when the plants formed clusters. |
|---|---|
| AbstractList | The paper focuses on simulating an artificial life in which neural networks (recurrent (RNN) and Long Short Term Memory (LSTM) networks) control prey and predator agents. The research goal was to check whether a simple genetic algorithm evolves the LSTM based controller that competes with the classic RNN controller in the real world. We also examined the impact of audio communication within a given species on the survival of agents. Our experiments evidenced the LSTM network results were slightly worse than the RNN controller. We also showed that prey agents developed herd behavior in response to predator pressure. They learned to form herds that allowed them to resist predator attacks. It was also possible to observe the prey agent’s cooperation in searching for food when the plants formed clusters. |
| Author | Slimak, Adrian Markowska-Kaczmar, Urszula |
| Author_xml | – sequence: 1 givenname: Urszula surname: Markowska-Kaczmar fullname: Markowska-Kaczmar, Urszula email: urszula.markowska-kaczmar@pwr.edu.pl organization: Department of Computational Intelligence, Wroclaw University of Science and Technology, Wyb. Wyspianskiego 27, 50-370 Wroclaw, Poland – sequence: 2 givenname: Adrian surname: Slimak fullname: Slimak, Adrian organization: Department of Computational Intelligence, Wroclaw University of Science and Technology, Wyb. Wyspianskiego 27, 50-370 Wroclaw, Poland |
| BookMark | eNp9kM1OwzAMgCM0JMbYE3DpC7TESbsmB5DQxJ80iQuco7Rxt4yRTk5XtLenZRw44Yt_5M-yvks2CW1Axq6BZ8BhcbPN9tTWMRNcQMZVxvPijE1BlWXKC64nf-oLNo9xy4eQSmkop-xuSWg7H9bJBsklFW5s71tKqmPSe-oOdpfYNYYuJoc4bgU80DAL2H219BGv2HljdxHnv3nG3h8f3pbP6er16WV5v0proVWXNjqHBSBiVYCTTjc5ClGMDSrtrAbgJUrMK-EQpZOFhEY0gLIQMudOyRmTp7s1tTESNmZP_tPS0QA3owWzNT8WzGjBcGUGCwN1e6JweK33SCbWHkONzhPWnXGt_5f_BudAaS0 |
| Cites_doi | 10.1109/TEVC.2010.2059031 10.1093/beheco/arm109 10.1162/ARTL_a_00206 10.1371/journal.pone.0235750 10.1177/105971239200100105 10.1098/rsif.2013.0305 10.1016/j.asoc.2020.106177 10.1145/37402.37406 10.1016/j.physd.2019.132306 10.1016/j.ecolmodel.2015.02.018 |
| ContentType | Journal Article |
| Copyright | 2021 |
| Copyright_xml | – notice: 2021 |
| DBID | 6I. AAFTH AAYXX CITATION |
| DOI | 10.1016/j.procs.2021.08.045 |
| DatabaseName | ScienceDirect Open Access Titles Elsevier:ScienceDirect:Open Access CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 1877-0509 |
| EndPage | 446 |
| ExternalDocumentID | 10_1016_j_procs_2021_08_045 S1877050921015325 |
| GroupedDBID | --K 0R~ 0SF 1B1 457 5VS 6I. 71M AACTN AAEDT AAEDW AAFTH AAIKJ AALRI AAQFI AAXUO ABMAC ACGFS ADBBV ADEZE AEXQZ AFTJW AGHFR AITUG ALMA_UNASSIGNED_HOLDINGS AMRAJ E3Z EBS EJD EP3 FDB FNPLU HZ~ IXB KQ8 M41 M~E NCXOZ O-L O9- OK1 P2P RIG ROL SES SSZ 9DU AAYWO AAYXX ABWVN ACRPL ACVFH ADCNI ADNMO ADVLN AEUPX AFPUW AIGII AKBMS AKRWK AKYEP CITATION ~HD |
| ID | FETCH-LOGICAL-c298t-f94161eeeb51d3d9f4e225b51de89da91107e3e4b2dee3d3531f2f1e352340d83 |
| ISICitedReferencesCount | 0 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000720289000044&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1877-0509 |
| IngestDate | Sat Nov 29 07:01:38 EST 2025 Wed May 17 00:07:51 EDT 2023 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | 2010 MSC: : 92B20 62M45 68T05 Neural networks related approaches learning neural networks artificial life and related topics adaptive systems |
| Language | English |
| License | This is an open access article under the CC BY-NC-ND license. |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c298t-f94161eeeb51d3d9f4e225b51de89da91107e3e4b2dee3d3531f2f1e352340d83 |
| OpenAccessLink | https://dx.doi.org/10.1016/j.procs.2021.08.045 |
| PageCount | 10 |
| ParticipantIDs | crossref_primary_10_1016_j_procs_2021_08_045 elsevier_sciencedirect_doi_10_1016_j_procs_2021_08_045 |
| PublicationCentury | 2000 |
| PublicationDate | 2021 2021-00-00 |
| PublicationDateYYYYMMDD | 2021-01-01 |
| PublicationDate_xml | – year: 2021 text: 2021 |
| PublicationDecade | 2020 |
| PublicationTitle | Procedia computer science |
| PublicationYear | 2021 |
| Publisher | Elsevier B.V |
| Publisher_xml | – name: Elsevier B.V |
| References | Wood, Ackland (bib0001) 2007; 274 Olson, Hintze, Dyer, Knoester, Adami (bib0002) 2013 Beer, Gallagher (bib00010) 1992 Ch.Adami, J. Moore, F. Dyer, A. Hintze, R.Olson. (2016) “Exploring the coevolution of predator and prey morphology and behavior,” Artificial Life Conference Proceedings Das, Suganthan (bib00011) 2011; 15 Ward, Gobet, Kendall (bib0009) 2001; 7 . Demšar, Hemelrijk, Hildenbrandt (bib0005) 2015 Wijeyakulasuriya, Eisenhauer, Shaby, Hanks (bib00013) 2020; 15 Ioannou, Tosh, Neville, Krause (bib0008) 2008; 19 Reynolds (bib0007) 1987; 21 Olson, Knoester, Adami (bib0004) 2016; 22 Sherstinsky (bib00012) 2020; 404 Markowska-Kaczmar, Marcinkowski (bib0006) 2020; 90 Markowska-Kaczmar (10.1016/j.procs.2021.08.045_bib0006) 2020; 90 Ward (10.1016/j.procs.2021.08.045_bib0009) 2001; 7 Wood (10.1016/j.procs.2021.08.045_bib0001) 2007; 274 Wijeyakulasuriya (10.1016/j.procs.2021.08.045_bib00013) 2020; 15 Demšar (10.1016/j.procs.2021.08.045_bib0005) 2015 Beer (10.1016/j.procs.2021.08.045_bib00010) 1992 Sherstinsky (10.1016/j.procs.2021.08.045_bib00012) 2020; 404 Olson (10.1016/j.procs.2021.08.045_bib0002) 2013 Das (10.1016/j.procs.2021.08.045_bib00011) 2011; 15 Olson (10.1016/j.procs.2021.08.045_bib0004) 2016; 22 Reynolds (10.1016/j.procs.2021.08.045_bib0007) 1987; 21 10.1016/j.procs.2021.08.045_bib0003 Ioannou (10.1016/j.procs.2021.08.045_bib0008) 2008; 19 |
| References_xml | – volume: 274 start-page: 1637 year: 2007 end-page: 1642 ident: bib0001 article-title: “Evolving the selfish herd: Emergence of distinct aggregating strategies in an individual-based model,” publication-title: Proc Biol Sci. – volume: 7 start-page: 191 year: 2001 end-page: 209 ident: bib0009 article-title: “Evolving Collective Behavior in an Artificial Ecology,” publication-title: Spring – volume: 15 start-page: 4 year: 2011 end-page: 31 ident: bib00011 article-title: ”Differential Evolution: A Survey of the State-of-the-Art” publication-title: IEEE Trans. Evolutionary Computation – volume: 15 start-page: e0235750 year: 2020 ident: bib00013 article-title: ”Machine learning for modeling animal movement,” publication-title: PloS one – volume: 19 start-page: 126 year: 2008 end-page: 130 ident: bib0008 article-title: “The confusion effect - From neural networks to reduced predation risk,” publication-title: Behavioral Ecology – reference: . – volume: 404 year: 2020 ident: bib00012 article-title: ”Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network,” publication-title: Physica D: Nonlinear Phenomena – volume: 21 year: 1987 ident: bib0007 article-title: “Flocks, Herds, and Schools: A Distributed Behavioral Model,” publication-title: Computer Graphics – year: 2013 ident: bib0002 article-title: “Predator confusion is sufficient to evolve swarming behaviour,” publication-title: Journal of The Royal Society Interface – year: 2015 ident: bib0005 article-title: “Simulating predator attacks on schools: Evolving composite tactics,” publication-title: Ecological Modelling – year: 1992 ident: bib00010 article-title: “Evolving Dynamical Neural Networks for Adaptive Behavior,” publication-title: Adaptive Behavior – volume: 22 start-page: 299 year: 2016 end-page: 318 ident: bib0004 article-title: “Evolution of Swarming Behavior Is Shaped by How Predators Attack,” publication-title: Art. Life – volume: 90 start-page: 106 year: 2020 end-page: 177 ident: bib0006 article-title: “Markov network versus recurrent neural network in forming herd behavior based on sight and simple sound communication,” publication-title: Applied Soft Computing – reference: Ch.Adami, J. Moore, F. Dyer, A. Hintze, R.Olson. (2016) “Exploring the coevolution of predator and prey morphology and behavior,” Artificial Life Conference Proceedings, – volume: 15 start-page: 4 issue: 1 year: 2011 ident: 10.1016/j.procs.2021.08.045_bib00011 article-title: ”Differential Evolution: A Survey of the State-of-the-Art” publication-title: IEEE Trans. Evolutionary Computation doi: 10.1109/TEVC.2010.2059031 – volume: 19 start-page: 126 issue: 1 year: 2008 ident: 10.1016/j.procs.2021.08.045_bib0008 article-title: “The confusion effect - From neural networks to reduced predation risk,” publication-title: Behavioral Ecology doi: 10.1093/beheco/arm109 – volume: 274 start-page: 1637 issue: 1618 year: 2007 ident: 10.1016/j.procs.2021.08.045_bib0001 article-title: “Evolving the selfish herd: Emergence of distinct aggregating strategies in an individual-based model,” publication-title: Proc Biol Sci. – volume: 22 start-page: 299 issue: 3 year: 2016 ident: 10.1016/j.procs.2021.08.045_bib0004 article-title: “Evolution of Swarming Behavior Is Shaped by How Predators Attack,” publication-title: Art. Life doi: 10.1162/ARTL_a_00206 – volume: 15 start-page: e0235750 issue: 7 year: 2020 ident: 10.1016/j.procs.2021.08.045_bib00013 article-title: ”Machine learning for modeling animal movement,” publication-title: PloS one doi: 10.1371/journal.pone.0235750 – year: 1992 ident: 10.1016/j.procs.2021.08.045_bib00010 article-title: “Evolving Dynamical Neural Networks for Adaptive Behavior,” publication-title: Adaptive Behavior doi: 10.1177/105971239200100105 – year: 2013 ident: 10.1016/j.procs.2021.08.045_bib0002 article-title: “Predator confusion is sufficient to evolve swarming behaviour,” publication-title: Journal of The Royal Society Interface doi: 10.1098/rsif.2013.0305 – volume: 90 start-page: 106 year: 2020 ident: 10.1016/j.procs.2021.08.045_bib0006 article-title: “Markov network versus recurrent neural network in forming herd behavior based on sight and simple sound communication,” publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2020.106177 – volume: 21 issue: 4 year: 1987 ident: 10.1016/j.procs.2021.08.045_bib0007 article-title: “Flocks, Herds, and Schools: A Distributed Behavioral Model,” publication-title: Computer Graphics doi: 10.1145/37402.37406 – volume: 7 start-page: 191 issue: 2 year: 2001 ident: 10.1016/j.procs.2021.08.045_bib0009 article-title: “Evolving Collective Behavior in an Artificial Ecology,” publication-title: Spring – volume: 404 year: 2020 ident: 10.1016/j.procs.2021.08.045_bib00012 article-title: ”Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network,” publication-title: Physica D: Nonlinear Phenomena doi: 10.1016/j.physd.2019.132306 – year: 2015 ident: 10.1016/j.procs.2021.08.045_bib0005 article-title: “Simulating predator attacks on schools: Evolving composite tactics,” publication-title: Ecological Modelling doi: 10.1016/j.ecolmodel.2015.02.018 – ident: 10.1016/j.procs.2021.08.045_bib0003 |
| SSID | ssj0000388917 |
| Score | 2.2003098 |
| Snippet | The paper focuses on simulating an artificial life in which neural networks (recurrent (RNN) and Long Short Term Memory (LSTM) networks) control prey and... |
| SourceID | crossref elsevier |
| SourceType | Index Database Publisher |
| StartPage | 437 |
| SubjectTerms | 2010 MSC: : 92B20 62M45 68T05 adaptive systems learning Neural networks neural networks artificial life and related topics related approaches |
| Title | Creating herd behavior by virtual agents using neural networks |
| URI | https://dx.doi.org/10.1016/j.procs.2021.08.045 |
| Volume | 192 |
| WOSCitedRecordID | wos000720289000044&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 1877-0509 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000388917 issn: 1877-0509 databaseCode: M~E dateStart: 20100101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3JSsRAEG3cDl7cxZ0-eNOA2dMXQUQRFBFc8BaSdEVGx4wkM24Hv92qXuKK6MFLSBrSM9P1pvp1UfWKsfWSSrvAKxC8QeYEmZ87iZCR40MIW3kMEKleBBdH8fFxcnkpTkzFdaPaCcRVlTw-irt_NTWOobGpdPYP5m4nxQG8R6PjFc2O118ZflfRwOoKOWAt2zJ8opn3nVpVi2RXqqxtoMIEJGiJY5VOB2_ek1VVRID4UXnn1Pphw-yXb2Hs-qb30NxkzmFWPJtc7fO6eR50W29_2u3cZsrl7sjaYtGEGXTVsvGJSRw7JBOjt4xvxqwj1V3tjCsMtJiL2VUDHWj84rB17OCatouC1NM9raiqJSY_ymN_2rbaZEKbp3adqklSmiSl3ppBOMxGvRiPTJTS-fIWeyMFHKGaMbe_wwpSqdS_L1_me9LyjoicTbEJc4LgO9ry02wIqhk2abtzcOOsZ9m2BQInIHALBJ4_cQMEroHAFRC4BgK3QJhj5_t7Z7sHjumW4RSeSPpOKeisCgB56EpfijIA9NX0APjvywQd9MGHIPckgC99dL6lV7qADNwPtmTiz7ORqlfBAuMikmEZ5cKTSLfjsMxFnCRQSDdzJVJSucg27XLgmitRlPQHKyyyyC5ZanCq-VqKKPjpxaW_fc4yG6cnHSBbYSP9egCrbKy473eaek1B4BXp7nH7 |
| linkProvider | ISSN International Centre |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Creating+herd+behavior+by+virtual+agents+using+neural+networks&rft.jtitle=Procedia+computer+science&rft.au=Markowska-Kaczmar%2C+Urszula&rft.au=Slimak%2C+Adrian&rft.date=2021&rft.issn=1877-0509&rft.eissn=1877-0509&rft.volume=192&rft.spage=437&rft.epage=446&rft_id=info:doi/10.1016%2Fj.procs.2021.08.045&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_procs_2021_08_045 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1877-0509&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1877-0509&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1877-0509&client=summon |