Abstract
Biomimetic (“life mimicking”) systems automate functionality by replicating biological forms and processes (e.g., early airplane designs were trying to model winged flight in birds). In this way, organic systems not only supply proof of concept (“heavier-than-air flight is possible”), but also inform attempts to implement functionality by reproducing form in automated systems.
Important sub-disciplines within the science of machine learning have developed in this same way. The most widely used cognitive architectures closely resemble their biological starting points. Obvious examples are neural networks, expert systems, and genetic algorithms. The application of sophisticated optimization techniques to these systems can supersede unscalable aspects of the underlying biological metaphor (“airplanes do not flap their wings”). However, biomimetic principles inform possible approaches to solving many problems.
This paper describes the application of biomimetic design to augmented cognition and analyzes machine learning developments from a biomimetic lens. An experiment follows comparing Neural Networks and Bias-Based Learning and assesses their performance against human subjects. The last part synthesizes the results of the experiment with the principles underlying biomimetic design to foster effective problem-solving.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Svozil, D., Kvasnicka, V., Pospichal, J.: Introduction to multi-layer feed-forward neural networks. Chemom. Intell. Lab. Syst. 39(1), 43–62 (1997)
McCulloch, W.S., Pitts, W.: A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 5(4), 115–133 (1943)
Dreyfus, S.E.: Artificial neural networks, back propagation, and the Kelley-Bryson gradient procedure. J. Guid. Control Dyn. 13(5), 926–928 (1990)
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323(6088), 533–536 (1986)
Hancock, M., et al.: Visualizing parameter spaces of deep-learning machines. In: Schmorrow, D.D., Fidopiastis, C.M. (eds.) HCII 2019. LNCS (LNAI), vol. 11580, pp. 192–210. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-22419-6_15
Vu-Quoc, L.: Neuron and myelinated axon, with signal flow from inputs at dendrites to outputs at axon terminals. Wikimedia Commons, Wikipedia. https://commons.wikimedia.org/wiki/File:Neuron3.png. Accessed 16 Dec 2018
Rules for Actions and Constraints - ScienceDirect. https://www.sciencedirect.com/science/article/pii/B9780128051603000053. Accessed 25 Jan 2020
CiteSeerX—Expert Systems: Principles and Practice. http://citeseerx.ist.psu.edu/viewdoc/summary??doi=10.1.1.34.9207. Accessed 25 Jan 2020
Joyce, J.: Bayes’ Theorem. In: Zalta, E.N. (ed.) The Stanford Encyclopedia of Philosophy (Spring 2019 Edition). https://plato.stanford.edu/archives/spr2019/entries/bayes-theorem/
Leung, K.M.: Naive Bayesian classifier. Polytechnic University Department of Computer Science/Finance and Risk Engineering (2007)
Hancock, M.: Non-monotonic, bias-based reasoning under uncertainty. In: Proceedings of the 22nd International Conference on Human-Computer Interaction, Copenhagen, Denmark (July 2020)
Koza, J.R., Poli, R.: Genetic programming. In: Burke, E.K., Kendall, G. (eds.) Search Methodologies. Springer, Boston (2005). https://doi.org/10.1007/0-387-28356-0_5
Cordeschi, R.: Searching in a maze, in search of knowledge: issues in early artificial intelligence. In: Stock, O., Schaerf, M. (eds.) Reasoning, Action and Interaction in AI Theories and Systems. LNCS (LNAI), vol. 4155, pp. 1–23. Springer, Heidelberg (2006). https://doi.org/10.1007/11829263_1
Kaur, N.K.S.: A review of various maze solving algorithms based on graph theory. IJSRD, 6(12), 431–434 (2019). ISSN (online): 2321-0613
Knuth, D.E., Moore, R.W.: An analysis of alpha-beta pruning. Artif. Intell. 6(4), 293–326 (1975)
Swarms and Swarm Intelligence - IEEE Journals & Magazine. https://ieeexplore.ieee.org/abstract/document/4160239. Accessed 25 Jan 2020
Honey Bees Inspired Optimization Method: The Bees Algorithm. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4553508/. Accessed 25 Jan 2020
Swarm intelligence for routing in communication networks - IEEE Conference Publication. https://ieeexplore.ieee.org/abstract/document/966355. Accessed 25 Jan 2020
A botnet-based command and control approach relying on swarm intelligence - ScienceDirect. https://www.sciencedirect.com/science/article/pii/S1084804513001161. Accessed 25 Jan 2020
Zadeh, L.A.: Fuzzy logic. Computer 21(4), 83–93 (1988)
Zadeh, L.A.: Fuzzy logic = computing with words. In: Zadeh, L.A., Kacprzyk, J. (eds.) Computing with Words in Information/Intelligent Systems 1. Studies in Fuzziness and Soft Computing, vol. 33. Physica, Heidelberg (1999). https://doi.org/10.1007/978-3-7908-1873-4_1
Shaw, R.S.: Wikimedia. Wikimedia, Wikipedia. https://commons.wikimedia.org/wiki/File:Warm_fuzzy_logic_member_function.gif. Accessed 15 Nov 2004
Subramanya, S.R., Lakshminarasimhan, N.: Computer viruses. IEEE Potentials 20(4), 16–19 (2001). https://doi.org/10.1109/45.969588
Serazzi, G., Zanero, S.: Computer virus propagation models. In: Calzarossa, M.C., Gelenbe, E. (eds.) MASCOTS 2003. LNCS, vol. 2965, pp. 26–50. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24663-3_2
Denning, P.J.: Computer viruses. Research Institute for Advanced Computer Science (1988). https://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/19890017050.pdf
Fosnock, C.: Computer Worms: Past, Present, and Future, vol. 8. East Carolina University, Greenville (2005)
Haas, W.: Parasitic worms: strategies of host finding, recognition and invasion. Zoology 106(4), 349–364 (2003). https://doi.org/10.1078/0944-2006-00125
Zou, C.C., Gong, W., Towsley, D.: Code red worm propagation modeling and analysis. In: Proceedings of the 9th ACM Conference on Computer and Communications Security. ACM (2002)
Moore, D., Shannon, C., Claffy, K.: Code-red: a case study on the spread and victims of an Internet worm. In: Proceedings of the 2nd ACM SIGCOMM Workshop on Internet measurement (2002)
Tetko, I.V., Livingstone, D.J., Luik, A.I.: Neural network studies. 1. Comparison of overfitting and overtraining. J. Chem. Inf. Comput. Sci. 35(5), 826–833 (1995)
Festinger, L.: Cognitive dissonance. Sci. Am. 207(4), 93–106 (1962)
Ratio Between Training Error and Validation Error. Stack Exchange. https://i.stack.imgur.com/HxlMa.png. Accessed 1 Aug 2015
Designing the Flier. https://airandspace.si.edu/exhibitions/wright-brothers/online/fly/1903/designing.cfm. Accessed 8 Jan 2020
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Bowles, B., Hancock, M., Kirshner, M., Shaji, T. (2020). Biomimetic Design in Augmented Cognition. In: Schmorrow, D.D., Fidopiastis, C.M. (eds) Augmented Cognition. Theoretical and Technological Approaches. HCII 2020. Lecture Notes in Computer Science(), vol 12196. Springer, Cham. https://doi.org/10.1007/978-3-030-50353-6_17
Download citation
DOI: https://doi.org/10.1007/978-3-030-50353-6_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-50352-9
Online ISBN: 978-3-030-50353-6
eBook Packages: Computer ScienceComputer Science (R0)