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Explanatory AI for Pertinent Communication in Autonomic Systems

  • Marius Pol
  • Jean-Louis Dessalles
  • Ada Diaconescu
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1037)

Abstract

Autonomic computing systems maintain high-level goals by continuously adapting to a changing environment, yet their internal operations have no comprehensible meaning to humans. This paper proposes a dialogue management system based on a communication interface acting as a bridge between subsymbolic and symbolic knowledge representation levels. The communication interface explains the autonomic system operation in a human comprehensible form by representing the sensed data in conceptual spaces. Based on a knowledge base generated by the communication interface, the dialogue management system produces a pertinent flow of conversation between autonomic systems and humans, activating only when exceptional situations are encountered. Our approach is incremental, with the objective of enabling pertinent communication with any artificial system. We build a proof-of-concept implementation of the proposed solution in a smart home platform managed by an autonomic system.

Keywords

Argumentation Automated planning Autonomic computing Conceptual spaces Dialogue management 

References

  1. 1.
    Adams, B., Raubal, M.: A metric conceptual space algebra. In: COSIT (2009)Google Scholar
  2. 2.
    Chella, A., Coradeschi, S., Frixione, M., Saffiotti, A.: Perceptual anchoring via conceptual spaces. In: Proceedings of the AAAI 2004 Workshop on Anchoring Symbols to Sensor Data, pp. 40–45 (2004)Google Scholar
  3. 3.
    Dessalles, J.: A cognitive approach to relevant argument generation. In: Principles and Practice of Multi-Agent Systems - International Workshops (2015)Google Scholar
  4. 4.
    Dessalles, J.L.: La pertinence et ses origines cognitives - Nouvelles théories. Hermes-Science Publications (2008)Google Scholar
  5. 5.
    Diaconescu, A., Frey, S., Müller-Schloer, C., Pitt, J., Tomforde, S.: Goal-oriented holonics for complex system (self-)integration: concepts and case studies. In: 2016 IEEE 10th International Conference on Self-Adaptive and Self-Organizing Systems (SASO), pp. 100–109 (2016)Google Scholar
  6. 6.
    Diaconescu, A., Mata, P., Bellman, K.: Self-integrating organic control systems: from crayfish to smart homes. In: 6th International Workshop on Self-Optimisation in Autonomic and Organic Computing Systems (2018)Google Scholar
  7. 7.
    Escoffier, C., Chollet, S., Lalanda, P.: Lessons learned in building pervasive platforms, pp. 7–12 (2014)Google Scholar
  8. 8.
    Frey, S., Diaconescu, A., Menga, D., Demeure, I.M.: A generic holonic control architecture for heterogeneous multiscale and multiobjective smart microgrids. TAAS 10(2), 9:1–9:21 (2015)CrossRefGoogle Scholar
  9. 9.
    Ganek, A.G., Corbi, T.A.: The dawning of the autonomic computing era. IBM Syst. J. 42(1), 5–18 (2003)CrossRefGoogle Scholar
  10. 10.
    Gärdenfors, P.: Conceptual Spaces - The Geometry of Thought. MIT Press, Cambridge (2000)CrossRefGoogle Scholar
  11. 11.
    Gärdenfors, P.: Symbolic, conceptual and subconceptual representations (2002)Google Scholar
  12. 12.
    Gunning, D.: Explainable artificial intelligence (XAI) (2017)Google Scholar
  13. 13.
    Jurafsky, D., Martin, J.H.: Speech and Language Processing. Prentice Hall, Upper Saddle River (2008)Google Scholar
  14. 14.
    Kakas, A.C., Kowalski, R.A., Toni, F.: The role of abduction in logic programming. In: Handbook of Logic in Artificial Intelligence and Logic Programming, vol. 5, pp. 235–324 (1998)Google Scholar
  15. 15.
    Kakas, A.C., Michael, L., Toni, F.: Argumentation: Reconciling human and automated reasoning. In: Bridging@IJCAI (2016)Google Scholar
  16. 16.
    Langley, P.: The cognitive system paradigm. Adv. Cogn. Syst. 1, 3–13 (2012)Google Scholar
  17. 17.
    Levin, E., Pieraccini, R., Eckert, W.: Using Markov decision process for learning dialogue strategies. In: ICASSP (1998)Google Scholar
  18. 18.
    Lim, B.Y., Dey, A.K., Avrahami, D.: Why and why not explanations improve the intelligibility of context-aware intelligent systems, pp. 2119–2128 (2009)Google Scholar
  19. 19.
    Russell, D.M., Maglio, P.P., Dordick, R., Neti, C.: Dealing with ghosts: managing the user experience of autonomic computing. IBM Syst. J. 42(1), 177–188 (2003)CrossRefGoogle Scholar
  20. 20.
    Serban, I.V., Sordoni, A., Bengio, Y., Courville, A.C., Pineau, J.: Building end-to-end dialogue systems using generative hierarchical neural network models. In: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, Phoenix, Arizona, USA, 12–17 February 2016, pp. 3776–3784 (2016)Google Scholar
  21. 21.
    Srivastava, B., Kambhampati, S.: The case for automated planning in autonomic computing, pp. 331–332 (2005)Google Scholar
  22. 22.
    Warglien, M., Gardenfors, P., Westera, M.: Event structure, conceptual spaces and the semantics of verbs, pp. 159–193. De Gruyter (2012)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Marius Pol
    • 1
  • Jean-Louis Dessalles
    • 2
  • Ada Diaconescu
    • 2
  1. 1.ParisFrance
  2. 2.Télécom ParisTech, LTCI, IMTParisFrance

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