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The Cyber-Physical System Approach Towards Artificial General Intelligence: The Problem of Verification

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Artificial General Intelligence (AGI 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9205))

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Abstract

Cyber-Physical Systems have many components including physical ones with heavy demands on workflow management; a real-time problem. Furthermore, the complexity of the system involves some degree of stochasticity, due to interactions with the environment. We argue that the factored version of the event-learning framework (ELF) being able to exploit robust controllers (RCs) can meet the requirements. We discuss the factored ELF (fELF) as the interplay between episodic and procedural memories, two key components of AGI. Our illustration concerns a fELF with RCs and is a mockup of an explosive device removal task. We argue that (i) the fELF limits the exponent of the state space and provides solutions in polynomial time, (ii) RCs decrease the number of variables and thus decrease the said exponent further, while the solution stays \(\epsilon \)-optimal, (iii) solutions can be checked/verified by the execution being linear in the number of states visited, and (iv) communication can be restricted to instructions between subcomponents of an AGI system.

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References

  1. Altmeyer, S., Cucu-Grosjean, L., Davis, R.I.: Static probabilistic timing analysis for real-time systems using random replacement caches. Real-Time Systems 51(1), 77–123 (2015)

    Article  Google Scholar 

  2. Angluin, D.: A note on the number of queries needed to identify regular languages. Information and Control 51(1), 76–87 (1981)

    Article  MathSciNet  MATH  Google Scholar 

  3. Boutilier, C., Dearden, R., Goldszmidt, M., et al.: Exploiting structure in policy construction. IJCAI 14, 1104–1113 (1995)

    Google Scholar 

  4. Carroll, J.B.: The higher-stratum structure of cognitive abilities. In: The Scientific Study of General Intelligence, ch., pp. 5–21. Pergamon (2003)

    Google Scholar 

  5. Graziano, M.: The organization of behavioral repertoire in motor cortex. Annu. Rev. Neurosci. 29, 105–134 (2006)

    Article  Google Scholar 

  6. Gyenes, V., Bontovics, Á., Lőrincz, A.: Factored temporal difference learning in the New Ties environment. Acta Cybern. 18(4), 651–668 (2008)

    MATH  Google Scholar 

  7. Kalmár, Z., Szepesvári, C., Lőrincz, A.: Module-based reinforcement learning: Experiments with a real robot. Machine Learning 31, 55–85 (1998)

    Article  Google Scholar 

  8. Lőrincz, A., Pólik, I., Szita, I.: Event-learning and robust policy heuristics. Cognitive Systems Research 4(4), 319–337 (2003)

    Article  Google Scholar 

  9. Orlosky, J., Toyama, T., Sonntag, D., Sárkány, A., Lőrincz, A.: On-body multi-input indoor localization for dynamic emergency scenarios. In: IEEE Int. Conf. on Pervasive Comp. Comm. Workshop, pp. 320–325. IEEE (2014)

    Google Scholar 

  10. Puterman, M.: Markov decision processes. John Wiley & Sons, New York (1994)

    Google Scholar 

  11. Ribeiro, L., Rocha, A., Veiga, A., Barata, J.: Collaborative routing of products using a self-organizing mechatronic agent framework - a simulation study. Comp. Ind. 68, 27–39 (2015)

    Article  Google Scholar 

  12. Schmidhuber, J.: Deep learning in neural networks: An overview. Neural Networks 61, 85–117 (2015)

    Article  Google Scholar 

  13. Szepesvári, C., Littman, M.L.: Generalized Markov decision processes. In: Proceedings of International Conference of Machine Learning 1996, Bari (1996)

    Google Scholar 

  14. Szita, I., Lőrincz, A.: Learning to play using low-complexity rule-based policies. J. Artif. Int. Res. 30, 659–684 (2007)

    MATH  Google Scholar 

  15. Szita, I., Lőrincz, A.: Optimistic initialization and greediness lead to polynomial time learning in factored MDPs. In: Int. Conf. Mach. Learn., pp. 1001–1008. Omnipress (2009)

    Google Scholar 

  16. Szita, I., Takács, B., Lőrincz, A.: Epsilon-MDPs. J. Mach. Learn. Res. 3, 145–174 (2003)

    MathSciNet  Google Scholar 

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Correspondence to András Lőrincz .

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Tősér, Z., Lőrincz, A. (2015). The Cyber-Physical System Approach Towards Artificial General Intelligence: The Problem of Verification. In: Bieger, J., Goertzel, B., Potapov, A. (eds) Artificial General Intelligence. AGI 2015. Lecture Notes in Computer Science(), vol 9205. Springer, Cham. https://doi.org/10.1007/978-3-319-21365-1_38

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  • DOI: https://doi.org/10.1007/978-3-319-21365-1_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-21364-4

  • Online ISBN: 978-3-319-21365-1

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