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From Cognitive Modeling to Robotics: How Research on Human Cognition and Computational Cognitive Architectures can be Applied to Robotics Problems

  • Troy Dale KelleyEmail author
  • Christian Lebiere
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 780)

Abstract

Cognitive psychology and Artificial Intelligence (AI) have long been intertwined in the study of problem solving, learning, and perception. The early pioneers of AI, Herbert Simon and Allen Newell, drew as inspiration chess masters and from their study developed computer programs to mimic the problem solving abilities identified in chess masters. The understanding of chess strategies relied heavily upon characterizing the problem space as a combination of symbolic inference and statistical pattern matching, which allowed for a quick understanding of the environment by computer systems. Recently, robotics has emerged as an AI domain, and the problem space has proven a difficult one due to the sub-symbolic nature of the knowledge. As robotics has emerged as a field in AI, cognitive architecture researchers have continued to refine their understanding of cognition in new ways that allow for the duplication of human problem solving with limited resources. The goal of this manuscript is to inform the AI world of the successes cognitive architectures have produced with the hope that this knowledge can be transferred to AI, and more specifically, robotics.

Keywords

Cognitive architectures Artificial Intelligence Robotics 

References

  1. 1.
    Newell, A.: You can’t play 20 questions with nature and win: Projective comments on the papers of this symposium (1973)Google Scholar
  2. 2.
    Newell, A.: Unified Theories of Cognition. Harvard University Press, Boston (1990)Google Scholar
  3. 3.
    Anderson, J.R., Lebiere, C.: The Newell test for a theory of cognition. Behav. Brain Sci. 26, 587–637 (2003)Google Scholar
  4. 4.
    Rumelhart, D.E., McClelland, J.L.: PDP Research Group: Parallel distributed processing, vol. 1, p. 184. MIT press, Cambridge (1987)Google Scholar
  5. 5.
    Fahlman, S.E., Lebiere, C.: The cascade-correlation learning architecture. In: Advances in neural information processing systems, pp. 524–532 (1990)Google Scholar
  6. 6.
    Arel, I., Rose, D.C., Karnowski, T.P.: Deep machine learning-a new frontier in artificial intelligence research [research frontier]. IEEE Comput. Intell. Mag. 5(4), 13–18 (2010)CrossRefGoogle Scholar
  7. 7.
    Shlens, J.: A tutorial on principal component analysis. arXiv preprint arXiv:1404.1100 (2014)
  8. 8.
    Koenig, S., Xavier, R.: A robot navigation architecture based on partially observable markov decision process models. In: Artificial Intelligence Based Mobile Robotics: Case Studies of Successful Robot Systems, pp. 91–122 (1998)Google Scholar
  9. 9.
    Kallianpur, G., Mandrekar, V.: The Markov property for generalized Gaussian random fields. Ann. Inst. Fourier 24(2), 143–167 (1974)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Cooper, G.F.: The computational complexity of probabilistic inference using Bayesian belief networks. Artif. Intell. 42(2–3), 393–405 (1990)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Hanford, S.D., Janrathitikarn, O., Long, L.N.: Control of mobile robots using the soar cognitive architecture. J. Aerosp. Comput. Inf. Commun. 6(2), 69–91 (2009)CrossRefGoogle Scholar
  12. 12.
    Milner, B., Corkin, S., Teuber, H.L.: Further analysis of the hippocampal amnesic syndrome: 14-year follow-up study of HM. Neuropsychologia 6(3), 215–234 (1968)CrossRefGoogle Scholar
  13. 13.
    Nissen, M.J., Knopman, D.S., Schacter, D.L.: Neurochemical dissociation of memory systems. Neurology 37(5), 789–794 (1987)CrossRefGoogle Scholar
  14. 14.
    Dean, R.M.S.: Common world model for unmanned systems. In: Unmanned Systems Technology XV, vol. 8741, p. 87410O. International Society for Optics and Photonics, May 2013Google Scholar
  15. 15.
    Newell, A., Rosenbloom, P.S.: Mechanisms of skill acquisition and the law of practice. In: Anderson, J.R. (ed.) Cognitive Skills and Their Acquisition, pp. 1–55. Lawrence Erlbaum Associates, Hillsdale (1981)Google Scholar
  16. 16.
    Anderson, J.R., Schooler, L.J.: The adaptive nature of memory. In: Tulving, E., Craik, F.I.M. (eds.) Handbook of Memory, pp. 557–570. Oxford University Press, New York (2000)Google Scholar
  17. 17.
    Laird, J.E., Lebiere, C., Rosenbloom, P.S.: A standard model of the mind: toward a common computational framework across artificial intelligence, cognitive science, neuroscience, and robotics. AI Mag. 38(4) (2017).  https://doi.org/10.1609/aimag.v38i4.2744
  18. 18.
    Anderson, J.R., Lebiere, C.: The Atomic Components of Thought. Lawrence Erlbaum Associates, Mahwah (1998)Google Scholar
  19. 19.
    Jilk, D.J., Lebiere, C., O’Reilly, R.C., Anderson, J.R.: SAL: an explicitly pluralistic cognitive architecture. J. Exp. Theor. Artif. Intell. 20(3), 197–218 (2008)CrossRefGoogle Scholar
  20. 20.
    Anderson, J.R., Bothell, D., Byrne, M.D., Douglass, S., Lebiere, C., Qin, Y.: An integrated theory of the mind. Psychol. Rev. 111(4), 1036 (2004)CrossRefGoogle Scholar
  21. 21.
    O’Reilly, R.C., Munakata, Y.: Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain. MIT Press, Cambridge (2000)Google Scholar
  22. 22.
    Vinokurov, Y., Lebiere, C., Wyatte, D., Herd, S., O’Reilly, R.: Unsupervised Learning in Hybrid Cognitive Architectures. In: Proceedings of AAAI-12 Workshop on Neural-Symbolic Learning and Reasoning (2012)Google Scholar
  23. 23.
    Vinokurov, Y., Lebiere, C., Szabados, A., Herd, S., O’Reilly, R.: Integrating top-down expectations with bottom-up perceptual processing in a hybrid neural-symbolic architecture. In: Proceedings of the Fourth Annual Meeting of the BICA Society (BICA-2013) (2013)Google Scholar
  24. 24.
    Fields, M., Lennon, C., Lebiere, C., Martin, M.K.: Recognizing scenes by simulating implied social interaction networks. In: Proceedings of the 8th International Conference on Intelligent Robotics and its Applications, Portsmouth, UK, 24–27 August 2015 (2015)Google Scholar
  25. 25.
    Sycara, K., Lebiere, C., Pei, Y., Morrison, D., Tang, Y., Lewis, M.: Abstraction of analytical models from cognitive models of human control of robotic swarms. In: Proceedings of the 13th International Conference on Cognitive Modeling (ICCM-2015), Groningen, NL (2015)Google Scholar
  26. 26.
    Oltramari, A., Lebiere, C.: Knowledge in action: Integrating cognitive architectures and ontologies. In: Oltramari, A., Vossen, P., Qin, L., Hovy, E. (Eds.) New Trends of Research in Ontologies and Lexical Resources: Ideas, Projects, Systems. Springer, Germany (2013)Google Scholar
  27. 27.
    Oltramari, A., Lebiere, C.: Using ontologies in a cognitive-grounded system: automatic action recognition in video-surveillance. In: Proceedings of The 7th International Conference on Semantic Technologies for Intelligence, Defense, and Security (STIDS 2012). Fairfax, VA (2012)Google Scholar
  28. 28.
    Kurup, U., Lebiere, C., Stentz, A., Hebert, M.: Predicting and classifying pedestrian behavior using an integrated cognitive architecture. In: Proceedings of the Behavior Representation in Modeling and Simulation (BRIMS-12) Conference, Amelia Island, FL (2012)Google Scholar
  29. 29.
    Oltramari, A., Vinokurov, Y., Lebiere, C., Oh, J., Stentz, A.: Ontology-based Cognitive System for Contextual Reasoning in Robot Architectures. Presented at the AAAI Spring Symposium on Knowledge Representation and Reasoning in Robotics. AAAI Spring Symposium Technical Report SS-14-04. Menlo Park, CA: AAAI Press (2014)Google Scholar
  30. 30.
    Lieto, A., Lebiere, C., Oltramari, A.: The knowledge level in cognitive architectures: Current limitations and possible developments. J. Cogn. Syst. Res. (2017). http://dx.doi.org/10.1016/j.cogsys.2017.05.001

Copyright information

© Springer International Publishing AG, part of Springer Nature (outside the USA) 2019

Authors and Affiliations

  1. 1.U.S. Army Research Laboratory Aberdeen Proving GroundAberdeenUSA
  2. 2.Carnegie Mellon University PittsburghPittsburghUSA

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