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Machine consciousness as a service (MCaaS): a roadmap

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Abstract

The vision of self-managing software systems was proposed in the past two decades. We argue that to be able to realize such vision, a suitable machine consciousness computational model must be incorporated into the software design, as it will be responsible for all cognition, thinking, learning, planning, and decision making tasks. Currently, there exist many general computational models for machine consciousness that could be adopted. They differ in their complexity and their definition of what consciousness is. Therefore, in this paper, we propose to offer machine consciousness as a service (MCaaS). This enables any software system to become “self-managing” by loosely coupling itself with the MCaaS service via a suitable management interface. We propose a roadmap for building such MCaaS service as a composite web service, adopting the Starzyk–Prasad machine consciousness computational model.

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References

  1. Ahmad, S., Hawkins, J.: Properties of sparse distributed representations and their application to hierarchical temporal memory. CoRR (2015). arXiv:1503.07469

  2. Aleksander, I.: The potential impact of machine consciousness in science and engineering. Int. J. Mach. Conscious. 01(01), 1–9 (2009). https://doi.org/10.1142/S1793843009000037

    Article  Google Scholar 

  3. Aleksander, I., Dunmall, B.: Axioms and tests for the presence of minimal consciousness in agents. J. Conscious. Stud. 10, 7–18 (2003)

    Google Scholar 

  4. Andersen, R.A., Cui, H.: Intention, action planning, and decision making in parietal-frontal circuits. Neuron 63, 568–583 (2009)

    Article  Google Scholar 

  5. Bhatia, A., Maly, M.R., Kavraki, L.E., Vardi, M.Y.: Motion planning with complex goals. IEEE Robotics Autom. Mag. 18, 55–64 (2011)

    Article  Google Scholar 

  6. Chalmers, D.J.: Facing up to the problem of consciousness. J. Conscious. Stud. 2, 200–219 (1995)

    Google Scholar 

  7. Chella, A., Manzotti, R.: Machine consciousness: a manifesto for robotics. Int. J. Mach. Conscious. 01(01), 33–51 (2009). https://doi.org/10.1142/S1793843009000062

    Article  Google Scholar 

  8. Cui, Y., Surpur, C., Ahmad, S., Hawkins, J.: A comparative study of htm and other neural network models for online sequence learning with streaming data. In: 2016 International Joint Conference on Neural Networks (IJCNN), pp. 1530–1538 (2016)

  9. Elgedawy, I.: On-demand conversation customization for services in large smart environments. IBM J. Res. Dev. (Special issue on Smart Cities) 55(1/2), 5:1–5:14 (2011)

  10. Elliott, D., Hansen, S., Grierson, L.E.M., Lyons, J., Bennett, S.J., Hayes, S.J.: Goal-directed aiming: two components but multiple processes. Psychol. Bull. 136(6), 1023–44 (2010)

    Article  Google Scholar 

  11. Gamez, D.: Progress in machine consciousness. Conscious. Cogn. 17(3), 887–910 (2008). https://doi.org/10.1016/j.concog.2007.04.005

    Article  Google Scholar 

  12. George, D., Hawkins, J.: Towards a mathematical theory of cortical micro-circuits. PLoS Comput. Biol. 5(10), e1000532 (2009). https://doi.org/10.1371/journal.pcbi.1000532

  13. Graham, J., Starzyk, J.A., Ni, Z., He, H., Teng, T.H., Tan, A.H.: A comparative study between motivated learning and reinforcement learning. In: 2015 International Joint Conference on Neural Networks (IJCNN), pp. 1–8 (2015)

  14. Gupta, K., Majumdar, A.: Sparsely connected autoencoder. In: 2016 International Joint Conference on Neural Networks (IJCNN), pp. 1940–1947 (2016)

  15. Haladjian, H.H., Montemayor, C.: Artificial consciousness and the consciousness-attention dissociation. Conscious. Cogn. 45, 210–225 (2016). https://doi.org/10.1016/j.concog.2016.08.011

    Article  Google Scholar 

  16. Hawkins, J., George, D.: Hierarchical temporal memory concepts, theory, and terminology. In: Technical report, Numenta

  17. Hawkins, J., George, D., Niemasik, J.: Sequence memory for prediction, inference and behaviour. Philos. Trans. R. Soc. Lond. Ser. B Biol. Sci. 364(1521), 1203–9 (2009)

    Article  Google Scholar 

  18. Kephart, J.O., Chess, D.M.: The vision of autonomic computing. Computer 36(1), 41–50 (2003). https://doi.org/10.1109/MC.2003.1160055

    Article  MathSciNet  Google Scholar 

  19. Loar, B.: Phenomenal states. Philos. Perspect. 4, 81–108 (1990)

    Article  Google Scholar 

  20. Mirabella, G.: Should I stay or should I go? Conceptual underpinnings of goal-irected actions. Front. Syst. Neurosci. 8, 206 (2014). https://doi.org/10.3389/fnsys.2014.00206

  21. Müller-Schloer, C.: Organic computing: on the feasibility of controlled emergence. In: Proceedings of the 2nd IEEE/ACM/IFIP International Conference on Hardware/Software Codesign and System Synthesis, pp. 2–5 (2004). https://doi.org/10.1145/1016720.1016724

  22. Purdy, S.: Encoding data for htm systems. CoRR (2016). arXiv:1602.05925

  23. Reggia, J.A.: The rise of machine consciousness: studying consciousness with computational models. Neural Netw. 44, 112–131 (2013). https://doi.org/10.1016/j.neunet.2013.03.011

  24. Rogers, T.T., McClelland, J.L.: Prcis of semantic cognition: a parallel distributed processing approach. Behav. Brain Sci. 31(6), 689–714 (2008). https://doi.org/10.1017/S0140525X0800589X

    Article  Google Scholar 

  25. Searle, J.R.: Mind: a brief introduction. Oxford University Press, Oxford (2004)

    Google Scholar 

  26. Starzyk, J.A.: Mental saccades in control of cognitive process. In: The 2011 International Joint Conference on Neural Networks, pp. 495–502 (2011)

  27. Starzyk, J.A., Graham, J.T., Raif, P., Tan, A.H.: Motivated learning for the development of autonomous systems. Cogn. Syst. Res. 14(1), 10–25 (2012)

    Article  Google Scholar 

  28. Starzyk, J.A., Prasad, D.K.: A computational model of machine consciousness. Int. J. Mach. Conscious. 03(02), 255–281 (2011). https://doi.org/10.1142/S1793843011000819

    Article  Google Scholar 

  29. Tamborello I., F.P.: A computational model of routine procedural memory. Ph.D. thesis (2009). https://search.proquest.com/docview/304989696?accountid=13014

  30. van der Velde, F., de Kamps, M.: Neural blackboard architectures of combinatorial structures in cognition. Behav. Brain Sci. 29(1), 37–70 (2006). https://doi.org/10.1017/S0140525X06009022

    Google Scholar 

  31. Wang, W., Subagdja, B., Tan, A.H., Starzyk, J.A.: Neural modeling of episodic memory: encoding, retrieval, and forgetting. IEEE Trans. Neural Netw. Learn. Syst. 23(10), 1574–1586 (2012). https://doi.org/10.1109/TNNLS.2012.2208477

    Article  Google Scholar 

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  1. Computer Engineering Department, Middle East Technical University, Northern Cyprus Campus, Kalkanli, Guzelyurt, Mersin 10, Turkey

    Islam Elgedawy

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  1. Islam Elgedawy
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Elgedawy, I. Machine consciousness as a service (MCaaS): a roadmap. Iran J Comput Sci 1, 19–30 (2018). https://doi.org/10.1007/s42044-017-0002-1

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  • DOI: https://doi.org/10.1007/s42044-017-0002-1

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