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AI and IoT Architecture Based on Markov Blankets

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Advances in Information and Communication (FICC 2024)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 919))

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Abstract

The Internet of Things delivers data from sensors and devices around the environment. The problem for many organizations is making sense of all that data. Digital Agents (DA) can be used as a conceptual schema for modeling, planning actions, and predicting context behaviour starting from data. The Markov blanket allows to identification of the relevant characteristics for an analysis or a prediction and is connected to the principle of free energy and active inference, which are fundamental concepts to explain the autonomous systems. According to this perspective, DA systems consist of a hierarchy of nested Markov blankets, which self-organize to minimize surprise or uncertainty. It can be considered autonomous and context-independent if it respects these statistical parameters. A DA can be the basic building block to create a new generation of intelligent systems.

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References

  1. Carl, H., Bishop, P., Steiger, R.: A Universal Modular Actor Formalism for Artificial Intelligence IJCAI (1973)

    Google Scholar 

  2. Lehman, J., et al.: A Gentle Introduction to SOAR, An Architecture for Human Cognition: 2006 Update. University of Michigan (2006)

    Google Scholar 

  3. Sun, R.: Desiderata for cognitive architectures. Philos. Psychol. 17(3), 341–373 (2004)

    Article  Google Scholar 

  4. Anderson, J., et al.: An integrated theory of the mind. Psychol. Rev. 111(4), 1036–1060 (2004)

    Article  Google Scholar 

  5. Sun, R.: Cognition and multiagent interaction, from cognitive modeling to social simulation. In: Sun, R. (ed.) Rensselaer Polytechnic Institute, Cambridge U. Press, Cambridge (2005)

    Google Scholar 

  6. Friston, K.J., et al.: Parcels and particles: markov blankets in the brain. Netw. Neurosci. 1–76 (2020a)

    Google Scholar 

  7. Laird, J.E., Lebiere, C., Rosenbloom, P.S.: A Standard Model for the Mind: Toward a Common Computational Framework across (2017)

    Google Scholar 

  8. Wermter, S., Sun, R. (eds.): Hybrid Neural Symbolic Integration. Springer Verlag, Berlin (2000)

    Google Scholar 

  9. Miikkulainen, R., Dyer, M.: Natural language processing with modular PDP networks and distributed lexicon. Cogn. Sci. 15, 343–399 (1991)

    Article  Google Scholar 

  10. Kotseruba, J.T.: 40 Years of Cognitive Architectures Core Cognitive Abilities and Practical Applications. 27 Oct 2016. arXiv preprint arXiv:1610.08602

  11. Tabuada, P.: Symbolic control of linear systems based onsymbolic subsystems. IEEE Trans. On Automatic Control 51(6), 1003 (2006)

    Google Scholar 

  12. Zhang, Y.: Adaptive neural network based control of noncanonical nonlinear systems. IEEE Transactions on Neural Nnetwork 27(9)

    Google Scholar 

  13. Wallace, S.A., Laird, J.E.: Toward a methodology for AI architecture evaluation: comparing soar and CLIPS. In: Jennings N.R., Lespérance Y. (eds) Intelligent Agents VI. Agent Theories, Architectures, and Languages. ATAL 1999. Lecture Notes in Computer Science, vol 1757. Springer, Berlin, Heidelberg

    Google Scholar 

  14. Newell, A.: Unified Theories of Cognition. In: Shapiro, D., Langley, P. (ed.) Harvard Press, Boston, MA. (1990)

    Google Scholar 

  15. Artificial Intelligence, Cognitive Science, Neuroscience, and Robotics, AI Magazine 38(4). Robin Milner. Processes: A Mathematical Model of Computing Agents in Logic Colloquium (1973)

    Google Scholar 

  16. Pollack, M.E., Ringuette, M.: Introducing the tileworld: experimentally evaluating agent architectures. In: Proceedings of the Eighth National Conference on Artificial Intelligence, 1, pp. 183–189. MIT Press (1990)

    Google Scholar 

  17. Gat, E.: Integrating planning and reacting in a heterogeneous asynchronous architecture for mobile robots. In: Proceedings Tenth National Conference on Artificial Intelligence, pp. 809–15. AAAI Press (1992)

    Google Scholar 

  18. Lee, J., Yoo, S.I.: Reactive-system approaches to agent architectures. In: Jennings, N.R., Lesp, Y. (ed.) IIntelligent Agents VI — Proceedings of the Sixth International Workshop on Agent Theories, Architectures, and Languages (ATAL-99)

    Google Scholar 

  19. Newell. Unified Theories of Cognition. Harvard University Press, Cambridge, MA (1990)

    Google Scholar 

  20. Anderson, J.R., Lebiere, C.: The Atomic Components of Thought, Lawrence Erlbaum Associates

    Google Scholar 

  21. Langley, P., Laird, J.E.: Cognitive Architectures: Research Issues and Challenges, (Technical Report), Institute for the Study of Learning and Expertise, Palo Alto, CA

    Google Scholar 

  22. Controlling Physical Agents Through Reactive Logic Programming. Proceedings of the Third international Conference on Autonomous Agents, pp. 386–387(1999)

    Google Scholar 

  23. Ramstead, M.J.D., Badcock, P.B., Friston, K.J.: Answering Schrödinger’s question: a free-energy formulation. Phys. Life Rev. (2017)

    Google Scholar 

  24. Friston, K.: The free-energy principle: A unified brain theory?. (PDF).Nature Reviews Neuroscience 11(2), 127–138 (2010). https://doi.org/10.1038/nrn2787. PMID 20068583. S2CID 5053247. http://www.fil.ion.ucl.ac. uk/~karl/The%20free-energy%20principle%20A%20unified%20brain%20theory.pdf. 10.1038%2Fnrn2787

  25. Ramstead, M.J.D., Veissière, S.P.l., Kirmayer, L.: Cultural affordances: scaffolding local worlds through shared intentionality and regimes of attention. Front Psychol. 7 (2016)

    Google Scholar 

  26. Knill, D.C., Pouget, A.: The Bayesian brain: the role of uncertainty in neuralcoding and computation. Trends in Neurosciences 27(12), 712–719 (2004)

    Google Scholar 

  27. Euzenat, J., Shvaiko, P.: Ontology matching Archived 2010–01–16 at the Wayback Machine, Springer-Verlag, 978–3–642–38720–3 (2013)

    Google Scholar 

  28. Pearl, J.: Probabilistic reasoning in intelligent systems: networks of plausible inference. Representation and Reasoning Series. San Mateo CA: Morgan Kaufmann. ISBN 0–934613–73–7 (1988)

    Google Scholar 

  29. Jabraeil Jamali, M.A., Bahrami, B., Heidari, A., Allahverdizadeh, P., Norouzi, F.: IoT architecture. In: Towards the Internet of Things. EAI/Springer Innovations in Communication and Computing. Springer (2020)

    Google Scholar 

  30. Gubbi, J., Buyya, R.: Internet of Things (IoT): a vision, architectural elements, and future directions. Futur. Gener. Comput. Syst. 29(7), 1645–1660 (2013)

    Article  Google Scholar 

  31. The Internet of Things - High-Tech Strategy. http://www.gtai.de/GTAI/Navigation/EN/Invest/Industries/Smarterbusiness/Smart-systems/internet-of-things.html [2] ***, Recommendations for implementing the strategic initiative INDUSTRIE 4.0, Final report of the Industrie 4.0 Working Group (2013). http://www.plattform-i40.de/finalreport2013

  32. Fantana, N.L., et al.: IoT applications—value creation for industry. Internet of Things: Converging Technologies for Smart Environments and Integrated Ecosystems, p. 153, River Publishers (2013)

    Google Scholar 

  33. Broy, M.: Challenges in modeling cyber-physical systems. Proceedings of the 12th international conference on Information processing in sensor networks, ACM (2013)

    Google Scholar 

  34. Wu, M., Lu, T.J., Ling, F.Y., Sun, J., Du, H.Y.: Research on the architecture of Internet of Things. Advanced Computer Theory and Engineering (ICACTE), 2010 3rd International Conference on, 5, pp.V5–484-V5–487 (2010)

    Google Scholar 

  35. A Generic IoT Architecture for Smart Cities Ivan Ganchev, Zhanlin Ji, M´airt´ın O’Droma Telecommunications Research Centre (TRC) University of Limerick, Ireland

    Google Scholar 

  36. Stewart, T.C., West, R.L.: Deconstructing ACT-R. Proceedings of the seventh international conference on cognitive modeling. Carleton Cognitive Modelling Lab Institute of Cognitive Science, Carleton University (2006)

    Google Scholar 

  37. Albus, J., et al.: 4D/RCS: A Reference Model Architecture For Unmanned Vehicle Systems Version 2.2. NIST (2002)

    Google Scholar 

  38. Thibadeau, R., Just, M.A., Carpenter, P.A.: A model of the time course and content of reading. Cogn. Sci. 6, 157–203 (1982)

    Google Scholar 

  39. Arena, P., Baglio, S., Fortuna, L., Manganaro, G.: Cellular neural networks: a survey. IFAC Proceedings 28(10), pp. 43–48 (1995). IFAC Proceedings

    Google Scholar 

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Correspondence to Francesco Rago .

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Rago, F. (2024). AI and IoT Architecture Based on Markov Blankets. In: Arai, K. (eds) Advances in Information and Communication. FICC 2024. Lecture Notes in Networks and Systems, vol 919. Springer, Cham. https://doi.org/10.1007/978-3-031-53960-2_14

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