Issues in Applying Bio-Inspiration, Cognitive Critical Mass and Developmental-Inspired Principles to Advanced Intelligent Systems

  • Gary Berg-Cross
  • Alexei V. Samsonovich


This Chapter summarizes ideas presented at the special PerMIS 2008 session on Biological Inspiration for Intelligent Systems. Bio-inspired principles of development and evolution are a special part of the bio-models and principles that can be used to improve intelligent systems and related artifacts. Such principles are not always explicit. They represent an alternative to incremental engineering expansion using new technology to replicate human intelligent capabilities. They are more evident in efforts to replicate and produce a “critical mass” of higher cognitive functions of the human mind or their emergence through cognitive developmental robotics (DR) and self-regulated learning (SRL). DR approaches takes inspiration from natural processes, so that intelligently engineered systems may create solutions to problems in ways similar to what we hypothesize is occurring with biologics in their natural environment. This Chapter discusses how an SRL-based approach to bootstrap a “critical mass” can be assessed by a set of cognitive tests. It also uses a three-level bio-inspired framework to illustrate methodological issues in DR research. The approach stresses the importance of using bio-realistic developmental principles to guide and constrain research. Of particular importance is keeping models and implementation separate to avoid the possible of falling into a Ptolemaic paradigm that may lead to endless tweaking of models. Several of Lungarella’s design principles [36] for developmental robotics are discussed as constraints on intelligence as it emerges from an ecologically balanced, three-way interaction between an agents’ control systems, physical embodiment, and the external environment. The direction proposed herein is to explore such principles to avoid slavish following of superficial bio-inspiration. Rather we should proceed with a mature and informed developmental approach using developmental principles based on our incremental understanding of how intelligence develops.


Episodic Memory Intelligent System Critical Mass Cognitive Architecture Cognitive Growth 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Albus, J. S. (2008) Reverse engineering the brain. In A. V. Samsonovich (Ed.). Biologically Inspired Cognitive Architectures. Papers from the AAAI Fall Symposium. AAAI Technical Report FS-08-04, pp. 5–14. Menlo Park, CA: AAAI Press. ISBN 978-1-57735-396-6.Google Scholar
  2. 2.
    Baron-Cohen, S. (1995) Mindblindness: An Essay on Autism and Theory of Mind. Cambridge, MA: MIT Press.Google Scholar
  3. 3.
    Baron-Cohen, S Leslie., A. M., and Frith U. (1985) Does the autistic child have a “theory of mind”?, Cognition 21, 37–46.CrossRefGoogle Scholar
  4. 4.
    Barto, A.G. (1991) Learning and incremental dynamic programming. 14, 94–94.Google Scholar
  5. 5.
    Barresi, J. (2001) Extending self-consciousness into the future. In C. Moore and K. Lemmon (Eds.). The Self in Time: Developmental Perspectives, pp. 141–161. Mahwah, NJ: Erlbaum.Google Scholar
  6. 6.
    Bartsch, K., and Wellman, H. M. (1995) Children Talk About the Mind. Oxford: Oxford University Press.Google Scholar
  7. 7.
    Berg-Cross, G. (2003) A Pragmatic Approach to Discussing Intelligence in Systems, Performance Metrics for Intelligent Systems (PerMIS) Proceedings 2003.Google Scholar
  8. 8.
    Berg-Cross, G (2007) Panel discussion, Can the Development of Intelligent Robots be Benchmarked? Concepts and Issues from Epigenetic Robotics, Performance Metrics for Intelligent Systems (PerMIS) Proceedings 2007.Google Scholar
  9. 9.
    Berg-Cross, G (2008) Introduction to Biological Inspirations for Intelligent Systems, Performance Metrics for Intelligent Systems (PerMIS) Proceedings 2008.Google Scholar
  10. 10.
    Berg-Cross, G. (2004) Developing Rational-Empirical Views of Intelligent Adaptive Behavior. Performance Metrics for Intelligent Systems (PerMIS) conference.Google Scholar
  11. 11.
    Blank, D. S., Kumar, D., and Meeden, L. (2002) A developmental approach to intelligence. In S. J. Conlon (Ed.). Proceedings of the Thirteenth Annual Midwest Artificial Intelligence and Cognitive Science Society Conference.Google Scholar
  12. 12.
    Bongard, J., and Pfeifer, R. (2003) Evolving complete agents using artificial ontogeny. In F. Hara and R. Pfeifer (Eds.). Morpho-functional Machines – The New Species: Designing Embodied Intelligence. Berlin: Springer-Verlag.Google Scholar
  13. 13.
    Cassman, M. (2005) Barriers to progress in systems biology. Nature 438, 1079.Google Scholar
  14. 14.
    Cassman, M., Arkin, A., Doyle, F., Katagiri, F., Lauffenburger, D., and Stokes, C. (2007) Systems Biology: International Research and Development. Berlin: Springer.zbMATHGoogle Scholar
  15. 15.
    Coates, C. (2007) The Air Force ‘In Silico’ – Computational Biology in 2025, DTEC Report No.(s): AD-A474845; Nov 2007; 49 pp.Google Scholar
  16. 16.
    Croon, M. A., and van de Vijver, F. J. R. (1994) Introduction. In M. A. Croon and F. J. R. van de Vijver (Eds.). Viability of Mathematical Models in the Social and Behavioural Science. London: Swets and Zeitlinger.Google Scholar
  17. 17.
    Deakin, M. (2000) Modelling biological systems. In T. L. Vincent, A. I. Mees, and L. S. Jennings (Eds.). Dynamics of Complex Interconnected Biological Systems. Basel: Birkhauser.Google Scholar
  18. 18.
    Dere, E., Huston, J. P., and Silva, M. A. D. S. (2005) Integrated memory for objects, places, and temporal order: Evidence for episodic-like memory in mice. Neurobiology of Learning and Memory 84, 214–221.CrossRefGoogle Scholar
  19. 19.
    Dean, J. (1998) Animates and what they can tell us. Trends in Cognitive Sciences 2(2), 60–67.CrossRefMathSciNetGoogle Scholar
  20. 20.
    Freeman, W. (2002) On Communicating with Semantic Machines, PerMIS Proceedings, 2002.Google Scholar
  21. 21.
    Gallup, G. G. Jr. (1977) Absence of self-recognition in a monkey (Macaca fascicularis) following prolonged exposure to a mirror. Developmental Psychobiology 10, 281–284.CrossRefGoogle Scholar
  22. 22.
    Goldman, A. (1992) In defense of the simulation theory. Mind and Language 7, 104–119.CrossRefGoogle Scholar
  23. 23.
    Gopnik A., and Wellman H. (1994) The “theory” theory. In L. Hirschfeld and S. Gelman (Eds.). Mapping the Mind: Domain Specificity in Cognition and Culture, pp. 257–293. New York: Cambridge University Press.CrossRefGoogle Scholar
  24. 24.
    Gordon, R. (1986) Folk psychology as simulation. Mind and Language 1, 158–170.CrossRefGoogle Scholar
  25. 25.
    Hayes S. M., Ryan, L., Schnyer, D. M., and Nadel, L. (2004) An fMRI study of episodic memory: Retrieval of object, spatial, and temporal information. Behavioral Neuroscience 118, 885–896.CrossRefGoogle Scholar
  26. 26.
    Graham-Rowe, D. (2005) Mission to build a simulated brain begins. New Scientist, June.Google Scholar
  27. 27.
    Hagoort, P. (2005). On Broca, brain, and binding: a new framework. Trends in Cognitive Sciences 9, 416–423.Google Scholar
  28. 28.
    Heal, J. (1996) Simulation and cognitive penetrability. Mind and Language 11, 44–67.CrossRefGoogle Scholar
  29. 29.
    IBM Blue Brain Project (2005),
  30. 30.
    Indefrey, P., and Levelt, W. J. M. (2004) The spatial and temporal signatures of word production components. Cognition 92(1–2), 101–144.CrossRefGoogle Scholar
  31. 31.
    Kitano, H. (2002). Computational systems biology. Nature 420, 206–210.Google Scholar
  32. 32.
    Kurths, J., Hilgetag, C., Osipov, G., Zamora, G., Zemanova, L., and Zhou, C. S. (2007) Network of Networks – a Model for Complex Brain Dynamics,∼juergen/juergen.html.
  33. 33.
    Lee, M., Meng, Q., and Chao, F. (2006) Developmental Robotics from Developmental Psychology, Proceedings of Towards Autonomous Robotic Systems (TAROS-06), pp. 103–09, University of Guildford, Surrey.Google Scholar
  34. 34.
    Lemmon, K., and Moore, C. (2001) Binding the self in time. In C. Moore and K. Lemmon (Eds.). The Self in Time: Developmental Perspectives, pp. 141–161. Mahwah, NJ: Erlbaum.Google Scholar
  35. 35.
    Levelt, W. J. M. (1989) Speaking: From Intention to Articulation. Cambridge, MA: MIT Press.Google Scholar
  36. 36.
    Lungarella, M. (2004) Exploring Principles Toward a Developmental Theory of Embodied Artificial Intelligence, Ph.D. Dissertation, Zurich. http://Download/PhDThesis/thesis040504_complete.pdf.
  37. 37.
    McCarthy, J., Minsky, M., Rochester, N., and Shannon, C. (1955/2006) A proposal for the dartmouth summer research project on artificial intelligence. AI Magazine 27(4), 12–14.Google Scholar
  38. 38.
    McCulloch, W. S., and Pitts, W. H. (1943) A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics 5, 115–133.zbMATHCrossRefMathSciNetGoogle Scholar
  39. 39.
    Miconi, T., and Channon, A. (2005) Analysing coevolution among artificial creatures. In E. G. Talbi (Ed.). Procs Evolution Artificielle 2005 (EA 05). Berlin: Springer-Verlag.Google Scholar
  40. 40.
    Mueller, S. T., and Minnery, B. S. (2008) Adapting the Turing test for embodied neurocognitive evaluation of biologically-inspired cognitive agents. In A. V. Samsonovich (Ed.). Biologically Inspired Cognitive Architectures. Papers from the AAAI Fall Symposium. AAAI Technical Report FS-08-04, pp. 117–126. Menlo Park, CA: AAAI Press. ISBN 978-1-57735-396-6.Google Scholar
  41. 41.
    Nichols S., and Stich, S. (2003) Mindreading: An Integrated Account of Pretence, Self-Awareness, and Understanding Other Minds. Oxford: Oxford University Press.Google Scholar
  42. 42.
    Nolfi, N., Ikegami,T., and Tani, J. (2008), Behavior as a complex adaptive system: On the role of self-organization in the development of individual and collective behavior. Adaptive Behavior 16(2–3), 101–103.CrossRefGoogle Scholar
  43. 43.
    Penrose, R. (1989) The Emperor’s New Mind: Concerning Computers, Minds, and the Laws of Physics. New York: Oxford University Press.Google Scholar
  44. 44.
    Pfeifer, R., and Bongard, J. C. (2006) How the Body Shapes the Way We Think—A New View of Intelligence. Cambridge, MA: MIT Press.Google Scholar
  45. 45.
    Piaget, J., and Inhelder, B. (1998) Jean Piaget: Selected Works Third Edition). London: Routledge.Google Scholar
  46. 46.
    Pinker, S., and Jackendoff, R. (2005) The faculty of language: What’s special about it? Cognition 95, 201–236.CrossRefGoogle Scholar
  47. 47.
    Povinelli, J. (2001) The self: Elevated in consciousness and extended in time. In C. Moore and K. Lemmon (Eds.). The Self in Time: Developmental Perspectives, pp. 141–161. Mahwah, NJ: Erlbaum.Google Scholar
  48. 48.
    Rosenfeld, S., and Kapetanovic, I. (2008) Systems biology and cancer prevention: All options on the table. Gene Regulation and Systems Biology 2, 307–319.Google Scholar
  49. 49.
    Rubin, D. C., Schrauf, R. W., and Greenberg, D. L. (2004) Stability in autobiographical memories, Memory 12, 715–721.CrossRefGoogle Scholar
  50. 50.
    Samsonovich, A. (2000) Masked-priming ‘Sally-Anne’ test supports a simulationist view of human theory of mind. In B. W. Mel and T. J. Sejnowski (Eds.). Proceedings of the 7th Joint Symposium on Neural Computation, vol. 10, pp. 104–111. San Diego, CA: Institute for Neural Computation, UCSD.Google Scholar
  51. 51.
    Samsonovich, A. V. (Ed.). (2008) Biologically Inspired Cognitive Architectures. Papers from the AAAI Fall Symposium. AAAI Technical Report FS-08-04, 206 pp. Menlo Park, CA: AAAI Press. ISBN 978-1-57735-396-6.Google Scholar
  52. 52.
    Samsonovich, A. V., and Nadel, L. (2005) Fundamental principles and mechanisms of the conscious self. Cortex 41(5), 669–689.CrossRefGoogle Scholar
  53. 53.
    Samsonovich, A. V., and Ascoli, G. A. (2005) A simple neural network model of the hippocampus suggesting its pathfinding role in episodic memory retrieval. Learning & Memory 12, 193–208.CrossRefGoogle Scholar
  54. 54.
    Samsonovich, A. V., and De Jong, K. A.(2005) Designing a self-aware neuromorphic hybrid. In K. R. Thorisson, H. Vilhjalmsson, and S. Marsela (Eds.). AAAI-05 Workshop on Modular Construction of Human-Like Intelligence: AAAI Technical Report, vol. WS-05-08, pp. 71–78. Menlo Park, CA: AAAI Press.Google Scholar
  55. 55.
    Samsonovich, A. V., and De Jong, K. A. (2005) A general-purpose computational model of the conscious mind. In M. Lovett, C. Schunn, C. Lebiere, and P. Munro (Eds.). Proceedings of the Sixth International Conference on Cognitive Modeling, pp. 382–383. Mahwah, NJ: Erlbaum.Google Scholar
  56. 56.
    Samsonovich, A. V., Ascoli, G. A., and De Jong, K. A. (2006) Computational assessment of the ‘magic’ of human cognition. In Proceedings of the 2006 International Joint Conference on Neural Networks, pp. 1170–1177. Vancouver, BC: IEEE Press.Google Scholar
  57. 57.
    Samsonovich, A. V., Kitsantas, A., Dabbagh, N., and De Jong, K. A. (2008) Self-awareness as metacognition about own self concept. In M. T. Cox and A. Raja (Eds.). Metareasoning: Thinking about Thinking. Papers from the 2008 AAAI Workshop. AAAI Technical Report, vol. WS-08-07, pp. 159–162. Menlo Park, CA: AAAI Press.Google Scholar
  58. 58.
    Scassellati, B. (2001) Foundations for a Theory of Mind for a Humanoid Robot Ph.D. Dissertation, MIT,
  59. 59.
    Schrödinger, E. (1944) What is Life. Cambridge, MA: Cambridge University Press.Google Scholar
  60. 60.
    Sims, K. (1994) Evolving 3d morphology and behavior by competition. In R. Brooks and P. Maes (Eds.). Proceedings of SAB’98. Cambridge, MA: MIT Press.Google Scholar
  61. 61.
    Starns, J. J., and Hicks, J. L. (2004) Episodic generation can cause semantic forgetting: Retrieval-induced forgetting of false memories. Memory & Cognition 32, 602–609.Google Scholar
  62. 62.
    Thelin, J. W., and Fussner, J. C. (2005) Factors related to the development of communication in CHARGE syndrome. American Journal of Medical Genetics Part A 133A(3), 282–290.CrossRefGoogle Scholar
  63. 63.
    Tulving, E. (1983) Elements of Episodic Memory. New York: Clarendon.Google Scholar
  64. 64.
    Turing, A. (1950) Computing machinery and intelligence. Mind 59(236), 433–460.CrossRefMathSciNetGoogle Scholar
  65. 65.
    Webb, B. (2001) Can robots make good models of biological behaviour? Behavioral & Brain Sciences 24, 1033–1050.Google Scholar
  66. 66.
    Wheeler, M. A., Stuss, D. T., and Tulving, E. (1997) Toward a theory of episodic memory: The frontal lobes and autonoetic consciousness. Psychological Bulletin 121, 331–354.CrossRefGoogle Scholar
  67. 67.
    Wooley, J. C., and Lin, H. S. (2005) Biological inspiration for computing. In John C. Wooley and Herbert S. Lin (Eds.). Catalyzing Inquiry at the Interface of Computing and Biology, Computer Science and Telecommunications Board. Washington, DC: The National Academies Press.Google Scholar
  68. 68.
    Zimmerman, B. J. (2002) Becoming a self-regulated learner: An overview. Theory into Practice 41(2), 64–70.CrossRefMathSciNetGoogle Scholar
  69. 69.
    Zimmerman, B. J., and Kitsantas, A. (2006) The hidden dimension of personal competence: Self-regulated learning and practice. In A. J. Elliot and C. S. Dweck (Eds.). Handbook of Competence and Motivation, pp. 509–526. New York: The Guilford Press.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Engineering, Management and IntegrationPotomacUSA
  2. 2.Krasnow Institute for Advanced Study, George Mason UniversityFairfaxUSA

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