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Constructing observational learning agents using self-organizing maps

  • Nobuhito ManomeEmail author
  • Shuji Shinohara
  • Kouta Suzuki
  • Yu Chen
  • Shunji Mitsuyoshi
Original Article
  • 24 Downloads

Abstract

Observational learning is a form of social learning whose theory proposes that new behaviors can be acquired through observing and imitating others. We employed Kohonen self-organizing maps to create observational learning agents to model the real-world process of observational learning. Real-world observational learning is a process that occurs through constantly changing nature and imperfect observation. In this study, we use observational learning agents to conduct a multiagent simulation of a cleanup problem comprising the chained tasks of picking up trash and subsequently discarding it. The results indicate that the constructed observational learning agents produce new emergent behaviors under changing, imperfect observation. Furthermore, the agents demonstrated the best performance when observing others to a moderate degree.

Keywords

Computational modeling Emulation Multiagent systems Self-organizing maps Social learning 

Notes

References

  1. 1.
    Bandura A (1971) Social learning theory. In: General Learning CorporationGoogle Scholar
  2. 2.
    Bandura A, Ross D, Ross SA (1961) Transmission of aggression through imitation of aggressive models. J Abnorm Psychol 63:575–582Google Scholar
  3. 3.
    Bandura A, Ross D, Ross SA (1963) Vicarious reinforcement and imitative learning. J Abnorm Psychol 67:527–534Google Scholar
  4. 4.
    Thorpe WH (1963) Learning and instinct in animals. Harvard University Press, CambridgeGoogle Scholar
  5. 5.
    Olsson A, Ebert JP, Banaji MR, Phelps EA (2005) The role of social groups in the persistence of learned fear. Science 309:785–787CrossRefGoogle Scholar
  6. 6.
    Zajonc RB (1965) Social facilitation. Science 149:269–274CrossRefGoogle Scholar
  7. 7.
    Olsson A, Phelps EA (2007) Social learning of fear. Nat Neurosci 10:1095–1102CrossRefGoogle Scholar
  8. 8.
    Tomasello M, Carpenter M, Call J, Behne T (2005) Understanding and sharing intentions: the origins of cultural cognition. Behav Brain Sci 28(5):675–691CrossRefGoogle Scholar
  9. 9.
    Meltzoff AN, Williamson RA (2010) The importance of imitation for theories of social-cognitive development. Wiley-Blackwell Handb Infant Dev 1:345–364CrossRefGoogle Scholar
  10. 10.
    Want SC, Harris PL (2009) How do children ape? Applying concepts from the study of non-human primates to the developmental study of ‘imitation’ in children. Dev Sci 5(1):1–14CrossRefGoogle Scholar
  11. 11.
    Montessori M (1966) The human tendencies and Montessori education. American Montessori Society, New YorkGoogle Scholar
  12. 12.
    Kozulin A (1986) Vygotsky in Context. MIT Press, CambridgeGoogle Scholar
  13. 13.
    Raos V, Evangeliou MN, Savaki HE (2004) Observation of action: grasping with the mind’s hand. NeuroImage 23(1):193–201CrossRefGoogle Scholar
  14. 14.
    Raos V, Evangeliou MN, Savaki HE (2007) Mental simulation of action in the service of action perception. NeuroImage 27(46):12675–12683Google Scholar
  15. 15.
    Caspers S, Zilles K, Laird AR, Eickhoff SB (2010) ALE meta-analysis of action observation and imitation in the human brain. NeuroImage 50:1148–1167CrossRefGoogle Scholar
  16. 16.
    Iacoboni M (2009) Imitation, empathy, and mirror neurons. Annu Rev Psychol 60:653–670CrossRefGoogle Scholar
  17. 17.
    Rodríguez AL, Cheeran B, Koch G, Hortobagy T, Fernandez-del-Olmo M (2014) The role of mirror neurons in observational motor learning: an integrative review. Eur J Hum Movement 32:82–103Google Scholar
  18. 18.
    Billard A, Hayes G (1999) DRAMA, a connectionist architecture for control and learning in autonomous robots. Adapt Behavior 7(1):35–63CrossRefGoogle Scholar
  19. 19.
    Demiris J, Hayes G (2002) Imitation as a dual-route process featuring predictive and learning components: a biologically plausible computational model. In: Imitation in animals and artifacts, pp 327–361Google Scholar
  20. 20.
    Bjork RA (1989) Retrieval inhibition as an adaptive mechanism in human memory. In: Varieties of memory and consciousness: essays in honour of Endel Tulving, pp 309–330Google Scholar
  21. 21.
    MacLeod CM (1989) Directed forgetting affects both direct and indirect tests of memory. J Exp Psychol Learn Mem Cogn 15(1):13–21MathSciNetCrossRefGoogle Scholar
  22. 22.
    Conway MA, Harries K, Noyes J, Racsmány M, Frankish CR (2000) The disruption and dissolution of directed forgetting: inhibitory control of memory. J Mem Lang 43(3):409–430CrossRefGoogle Scholar
  23. 23.
    Hasher L, Zacks R (1988) Working memory, comprehension, and aging: a review and a new view. Psychol Learn Motivat 22:193–225CrossRefGoogle Scholar
  24. 24.
    Racsmány M, Keresztes A, Pajkossy P, Demeter G (2012) Mirroring intentional forgetting in a shared-goal learning situation. PLoS One 7:1CrossRefGoogle Scholar
  25. 25.
    Tennie C, Call J, Tomasello M (2010) Evidence for emulation in chimpanzees in social settings using the floating peanut task. PLoS One 5:5CrossRefGoogle Scholar
  26. 26.
    Auersperg AMI, von Bayern AMI, Weber S, Szabadvari A, Bugnyar T, Kacelnik A (2014) Social transmission of tool use and tool manufacture in Goffin cockatoos (Cacatua goffini). Proc R Soc B Biol Sci 281:1793CrossRefGoogle Scholar
  27. 27.
    Hopper LM, Lambeth SP, Schapiro SJ, Whiten A (2008) Observational learning in chimpanzees and children studied through ghost’ conditions. Proc R Soc B Biol Sci 275:1636CrossRefGoogle Scholar
  28. 28.
    Kohonen T (1995) Self-organizing maps. Springer, BerlinCrossRefGoogle Scholar
  29. 29.
    Li Z, Bagan H, Yamagata Y (2018) Analysis of spatiotemporal land cover changes in Inner Mongolia using self-organizing map neural network and grid cells method. Sci Total Environ 636(15):1180–1191CrossRefGoogle Scholar
  30. 30.
    Li T, Sun G, Yang C, Liang K, Ma S, Huang L (2018) Using self-organizing map for coastal water quality classification: towards a better understanding of patterns and processes. Sci Total Environ 628–629(1):1446–1459CrossRefGoogle Scholar
  31. 31.
    Belkhiri L, Mouni L, Tiri A, Narany TS, Nouibet R (2018) Spatial analysis of groundwater quality using self-organizing maps. Groundw Sustain Dev 7:121–132CrossRefGoogle Scholar
  32. 32.
    Camara-Turull X, Fernández-Izquierdo MÁ, Sorrosal-Forradellas MT (2017) Analysing capital structure of Spanish chemical companies using self-organizing maps. Kybernetes 46(6):947–965CrossRefGoogle Scholar
  33. 33.
    Tsai WP, Huang SP, Cheng ST, Shao KT, Chang FJ (2017) A data-mining framework for exploring the multirelation between fish species and water quality through self-organizing map. Sci Total Environ 579(1):474–483CrossRefGoogle Scholar
  34. 34.
    Onuki Y, Kosugi A, Hamaguchi M, Marumo Y, Kumada S, Hirai D, Ikeda J, Hayashi Y (2018) A comparative study of disintegration actions of various disintegrants using Kohonen’s self-organizing maps. J Drug Deliv Sci Technol 43:141–148CrossRefGoogle Scholar
  35. 35.
    Furao S, Hasegawa O (2005) An incremental network for on-line unsupervised classification and topology learning. Neural Netw 19(1):90–106CrossRefGoogle Scholar
  36. 36.
    Nakamura Y, Hasegawa O (2016) Nonparametric density estimation based on self-organizing incremental neural network for large noisy data. IEEE Trans Neural Netw Learn Syst 2016:25–32Google Scholar
  37. 37.
    Wang X, Hasegawa O (2017) Adaptive density estimation based on self-organizing incremental neural network using Gaussian process. In: Proceedings of international joint conference on neural networks, pp 4309–4315Google Scholar
  38. 38.
    McCloskey M, Cohen NJ (1989) Catastrophic interference in connectionist networks: the sequential learning problem. Psychol Learn Motiv 24:109–165CrossRefGoogle Scholar
  39. 39.
    Ratcliff R (1990) Connectionist models of recognition memory: constraints imposed by learning and forgetting functions. Psychol Rev 97:285–308CrossRefGoogle Scholar
  40. 40.
    Sharkey NE, Sharkey AJC (1995) An analysis of catastrophic interference. Connect Sci 7:301–329CrossRefGoogle Scholar

Copyright information

© International Society of Artificial Life and Robotics (ISAROB) 2019

Authors and Affiliations

  • Nobuhito Manome
    • 1
    • 2
    Email author
  • Shuji Shinohara
    • 2
  • Kouta Suzuki
    • 1
    • 2
  • Yu Chen
    • 2
  • Shunji Mitsuyoshi
    • 2
  1. 1.SoftBank Robotics Group Corp.TokyoJapan
  2. 2.The University of TokyoTokyoJapan

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