Skip to main content
Springer Nature Link
Log in

Constructing observational learning agents using self-organizing maps

  • Original Article
  • Published:
Artificial Life and Robotics Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Bandura A (1971) Social learning theory. In: General Learning Corporation

  2. Bandura A, Ross D, Ross SA (1961) Transmission of aggression through imitation of aggressive models. J Abnorm Psychol 63:575–582

    Google Scholar 

  3. Bandura A, Ross D, Ross SA (1963) Vicarious reinforcement and imitative learning. J Abnorm Psychol 67:527–534

    Google Scholar 

  4. Thorpe WH (1963) Learning and instinct in animals. Harvard University Press, Cambridge

    Google Scholar 

  5. Olsson A, Ebert JP, Banaji MR, Phelps EA (2005) The role of social groups in the persistence of learned fear. Science 309:785–787

    Article  Google Scholar 

  6. Zajonc RB (1965) Social facilitation. Science 149:269–274

    Article  Google Scholar 

  7. Olsson A, Phelps EA (2007) Social learning of fear. Nat Neurosci 10:1095–1102

    Article  Google Scholar 

  8. Tomasello M, Carpenter M, Call J, Behne T (2005) Understanding and sharing intentions: the origins of cultural cognition. Behav Brain Sci 28(5):675–691

    Article  Google Scholar 

  9. Meltzoff AN, Williamson RA (2010) The importance of imitation for theories of social-cognitive development. Wiley-Blackwell Handb Infant Dev 1:345–364

    Article  Google Scholar 

  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):1–14

    Article  Google Scholar 

  11. Montessori M (1966) The human tendencies and Montessori education. American Montessori Society, New York

    Google Scholar 

  12. Kozulin A (1986) Vygotsky in Context. MIT Press, Cambridge

    Google Scholar 

  13. Raos V, Evangeliou MN, Savaki HE (2004) Observation of action: grasping with the mind’s hand. NeuroImage 23(1):193–201

    Article  Google Scholar 

  14. Raos V, Evangeliou MN, Savaki HE (2007) Mental simulation of action in the service of action perception. NeuroImage 27(46):12675–12683

    Google Scholar 

  15. Caspers S, Zilles K, Laird AR, Eickhoff SB (2010) ALE meta-analysis of action observation and imitation in the human brain. NeuroImage 50:1148–1167

    Article  Google Scholar 

  16. Iacoboni M (2009) Imitation, empathy, and mirror neurons. Annu Rev Psychol 60:653–670

    Article  Google Scholar 

  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 32:82–103

    Google Scholar 

  18. Billard A, Hayes G (1999) DRAMA, a connectionist architecture for control and learning in autonomous robots. Adapt Behavior 7(1):35–63

    Article  Google Scholar 

  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 327–361

  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 309–330

  21. MacLeod CM (1989) Directed forgetting affects both direct and indirect tests of memory. J Exp Psychol Learn Mem Cogn 15(1):13–21

    Article  MathSciNet  Google Scholar 

  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):409–430

    Article  Google Scholar 

  23. Hasher L, Zacks R (1988) Working memory, comprehension, and aging: a review and a new view. Psychol Learn Motivat 22:193–225

    Article  Google Scholar 

  24. Racsmány M, Keresztes A, Pajkossy P, Demeter G (2012) Mirroring intentional forgetting in a shared-goal learning situation. PLoS One 7:1

    Article  Google Scholar 

  25. Tennie C, Call J, Tomasello M (2010) Evidence for emulation in chimpanzees in social settings using the floating peanut task. PLoS One 5:5

    Article  Google Scholar 

  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 281:1793

    Article  Google Scholar 

  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 275:1636

    Article  Google Scholar 

  28. Kohonen T (1995) Self-organizing maps. Springer, Berlin

    Book  MATH  Google Scholar 

  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):1180–1191

    Article  Google Scholar 

  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):1446–1459

    Article  Google Scholar 

  31. Belkhiri L, Mouni L, Tiri A, Narany TS, Nouibet R (2018) Spatial analysis of groundwater quality using self-organizing maps. Groundw Sustain Dev 7:121–132

    Article  Google Scholar 

  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):947–965

    Article  Google Scholar 

  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):474–483

    Article  Google Scholar 

  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 43:141–148

    Article  Google Scholar 

  35. Furao S, Hasegawa O (2005) An incremental network for on-line unsupervised classification and topology learning. Neural Netw 19(1):90–106

    Article  MATH  Google Scholar 

  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 2016:25–32

    Google Scholar 

  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 4309–4315

  38. McCloskey M, Cohen NJ (1989) Catastrophic interference in connectionist networks: the sequential learning problem. Psychol Learn Motiv 24:109–165

    Article  Google Scholar 

  39. Ratcliff R (1990) Connectionist models of recognition memory: constraints imposed by learning and forgetting functions. Psychol Rev 97:285–308

    Article  Google Scholar 

  40. Sharkey NE, Sharkey AJC (1995) An analysis of catastrophic interference. Connect Sci 7:301–329

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. SoftBank Robotics Group Corp., Tokyo, Japan

    Nobuhito Manome & Kouta Suzuki

  2. The University of Tokyo, Tokyo, Japan

    Nobuhito Manome, Shuji Shinohara, Kouta Suzuki, Yu Chen & Shunji Mitsuyoshi

Authors
  1. Nobuhito Manome
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Shuji Shinohara
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Kouta Suzuki
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Yu Chen
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Shunji Mitsuyoshi
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Nobuhito Manome.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Manome, N., Shinohara, S., Suzuki, K. et al. Constructing observational learning agents using self-organizing maps. Artif Life Robotics 25, 73–80 (2020). https://doi.org/10.1007/s10015-019-00574-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10015-019-00574-6

Keywords