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Toward a Smart City Using Tentacular AI

  • Atriya SenEmail author
  • Selmer Bringsjord
  • Naveen Sundar Govindarajulu
  • Paul Mayol
  • Rikhiya Ghosh
  • Biplav Srivastava
  • Kartik Talamadupula
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11249)

Abstract

The European Initiative on Smart Cities [2] is an effort by the European Commission [4] to improve quality of life throughout Europe, while progressing toward energy and climate objectives. Many of its goals are relevant to and desirable in the world at large. We propose that it is essential that artificial agents in a Smart City have theories of the minds of its inhabitants. We describe a scenario in which such theories are indispensable, and cannot be adequately and usefully captured by current forms of ambient intelligence. Then, we show how a new form of distributed, multi-agent artificial intelligence, Tentacular AI, which among other things entails a capacity for reasoning and planning based in highly expressive cognitive calculi (logics), is able to intelligently address this situation.

Keywords

Internet of Things Artificial intelligence Multi-agent systems 

Notes

Acknowledgements

This research is made possible by joint support from RPI and IBM under the AIRC program; we are grateful for this support. Some of the research reported on herein has been enabled by support from ONR and AFOSR, and for this too we are grateful.

References

  1. 1.
    Semantic sensor network ontology (2017). https://www.w3.org/TR/vocab-ssn/. Accessed 7 July 2018
  2. 2.
  3. 3.
    Tentacular AI (2018). http://kryten.mm.rpi.edu/TAI/tai.html. Accessed 7 July 2018
  4. 4.
    The European Commission’s Priorities (2018). https://ec.europa.eu/commission/index_en. Accessed 25 June 2018
  5. 5.
    Alirezaie, M., et al.: An ontology-based context-aware system for smart homes: E-care@ home. Sensors 17(7), 1586 (2017)CrossRefGoogle Scholar
  6. 6.
    Bessghaier, N., Zargayouna, M., Balbo, F.: Management of urban parking: an agent-based approach. In: Ramsay, A., Agre, G. (eds.) Artificial Intelligence: Methodology, Systems, and Applications, pp. 276–285. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  7. 7.
    Bringsjord, S., Licato, J., Govindarajulu, N., Ghosh, R., Sen, A.: Real robots that pass tests of self-consciousness. In: Proceedings of the 24th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN 2015), pp. 498–504. IEEE, New York (2015)Google Scholar
  8. 8.
    Bringsjord, S., Govindaralajulu, N.S., Sen, A., Peveler, M., Srivastava, B., Talamadupula, K.: Tentacular artificial intelligence, and the architecture thereof, introduced. In: To be Presented at the FAIM Workshop on Architectures and Evaluation for Generality, Autonomy & Progress in AI (2018)Google Scholar
  9. 9.
    Dwork, C.: Differential privacy: a survey of results. In: Agrawal, M., Du, D., Duan, Z., Li, A. (eds.) TAMC 2008. LNCS, vol. 4978, pp. 1–19. Springer, Heidelberg (2008).  https://doi.org/10.1007/978-3-540-79228-4_1CrossRefzbMATHGoogle Scholar
  10. 10.
    Gehrke, J., Lui, E., Pass, R.: Towards privacy for social networks: a zero-knowledge based definition of privacy. In: Ishai, Y. (ed.) TCC 2011. LNCS, vol. 6597, pp. 432–449. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-19571-6_26CrossRefzbMATHGoogle Scholar
  11. 11.
    Gentzen, G.: Investigations into logical deduction. In: Szabo, M.E. (ed.) The Collected Papers of Gerhard Gentzen, pp. 68–131, North-Holland, Amsterdam, The Netherlands (1935). This is an English version of the well-known 1935 German versionGoogle Scholar
  12. 12.
    Govindarajulu, N.S., Bringsjord, S.: On automating the doctrine of double effect. In: Sierra, C. (ed.) Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI-2017, pp. 4722–4730, Melbourne, Australia (2017). https://doi.org/10.24963/ijcai.2017/658, Preprint available at this URL. https://arxiv.org/abs/1703.08922
  13. 13.
    Govindarajulu, N.S., Bringsjord, S.: Strength factors: an uncertainty system for a quantified modal logic (2017). https://arxiv.org/abs/1705.10726, Presented at Workshop on Logical Foundations for Uncertainty and Machine Learning at IJCAI 2017, Melbourne, Australia
  14. 14.
    Kowalski, R., Sergot, M.: A logic-based calculus of events. New Gener. Comput. 4(1), 67–95 (1986)CrossRefGoogle Scholar
  15. 15.
    Muñoz, A., Botía, J.A.: Developing an intelligent parking management application based on multi-agent systems and semantic web technologies. In: Graña Romay, M., Corchado, E., Garcia Sebastian, M.T. (eds.) Hybrid Artificial Intelligence Systems, pp. 64–72. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-13769-3_8CrossRefGoogle Scholar
  16. 16.
    Sana, B., Riadh, H., Rafaa, M.: Intelligent parking management system by multi-agent approach: the case of urban area of Tunis. In: 2014 International Conference on Advanced Logistics and Transport (ICALT), pp. 65–71 (2014)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Atriya Sen
    • 1
    Email author
  • Selmer Bringsjord
    • 1
  • Naveen Sundar Govindarajulu
    • 1
  • Paul Mayol
    • 1
  • Rikhiya Ghosh
    • 1
  • Biplav Srivastava
    • 2
  • Kartik Talamadupula
    • 2
  1. 1.Rensselaer Polytechnic Institute (RPI)TroyUSA
  2. 2.IBM ResearchYorktown HeightsUSA

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