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Active Situation Reporting: Definition and Analysis

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Multi-Agent Systems and Agreement Technologies (EUMAS 2017, AT 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10767))

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Abstract

In a lot of situations a human is incapable to observe their environment properly. This can be due to disabilities, extreme conditions or simply a complex and changing environment. In those cases, help from an artificial system can be beneficial. This system, equipped with appropriate sensors, would be capable of perceiving things that a human cannot and inform them about the current state of the situation. In this short position paper, we introduce the notion of Active Situation Reporting, in which an agent can inform another agent about the evolution of a situation. We define this notion, study the challenges such a system raises and identify the open research questions by reviewing the state of the art.

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References

  1. Alirezaie, et al.: An ontology-based context-aware system for smart homes: E-care@ home. Sensors 17(7), 1586 (2017)

    Article  Google Scholar 

  2. Bajcsy, R.: Active perception. Proc. IEEE 76(8), 966–1005 (1988)

    Article  Google Scholar 

  3. Banaee, H., Loutfi, A.: Data-driven rule mining and representation of temporal patterns in physiological sensor data. IEEE J. Biomed. Health Inform. 19(5), 1557–1566 (2015)

    Article  Google Scholar 

  4. Beeson, P., Kortenkamp, D., Bonasso, R.P., Persson, A., Loutfi, A., Bona, J.P.: An ontology-based symbol grounding system for human-robot interaction. In: Proceedings of the 2014 AAAI Fall Symposium Series, Arlington, pp. 13–15 (2014)

    Google Scholar 

  5. Benezeth, Y., Jodoin, P.M., Emile, B., Laurent, H., Rosenberger, C.: Comparative study of background subtraction algorithms. J. Electron. Imaging 19(3), 033003–033003 (2010)

    Article  Google Scholar 

  6. Bouayad-Agha, N., Casamayor, G., Wanner, L.: Natural language generation in the context of the semantic web. Semant. Web 5(6), 493–513 (2014)

    Google Scholar 

  7. Compton, M., et al.: The SSN ontology of the W3C semantic sensor network incubator group. Web Semant. Sci. Serv. Agents World Wide Web 17, 25–32 (2012)

    Article  Google Scholar 

  8. Coradeschi, S., Loutfi, A., Wrede, B.: A short review of symbol grounding in robotic and intelligent systems. KI - Künstliche Intelligenz 27(2), 129–136 (2013)

    Article  Google Scholar 

  9. Coradeschi, S., Saffiotti, A.: An introduction to the anchoring problem. Robot. Auton. Syst. 43(2–3), 85–96 (2003)

    Article  Google Scholar 

  10. Croft, B., Lafferty, J.: Language Modeling for Information Retrieval, vol. 13. Springer, Dordrecht (2013). https://doi.org/10.1007/978-94-017-0171-6

    Book  MATH  Google Scholar 

  11. Desobry, F., Davy, M., Doncarli, C.: An online kernel change detection algorithm. Trans. Signal Process. 53(8), 2961–2974 (2005)

    Article  MathSciNet  Google Scholar 

  12. Devin, S., Alami, R.: An implemented theory of mind to improve human-robot shared plans execution. In: The Eleventh ACM/IEEE International Conference on Human Robot Interaction, HRI 2016, pp. 319–326. IEEE Press, Piscataway (2016)

    Google Scholar 

  13. Faivishevsky, L.: Information theoretic multivariate change detection for multisensory information processing in Internet of Things. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6250–6254. IEEE, March 2016. http://ieeexplore.ieee.org/document/7472879/

  14. Floridi, L.: Understanding epistemic relevance. Erkenntnis 69(1), 69–92 (2008)

    Article  Google Scholar 

  15. Gama, J., Žliobaitė, I., Bifet, A., Pechenizkiy, M., Bouchachia, A.: A survey on concept drift adaptation. ACM Comput. Surv. (CSUR) 46(4), 44 (2014)

    Article  Google Scholar 

  16. Glazer, A., Lindenbaoum, M., Markovitch, S.: Learning high-density regions for a generalized Kolmogorov-Smirnov test in high-dimensional data. In: Proceedings of the 25th International Conference on Neural Information Processing Systems, NIPS 2012, pp. 728–736. Curran Associates Inc., USA (2012). http://dl.acm.org/citation.cfm?id=2999134.2999216

  17. Han, B., Davis, L.S.: Density-based multifeature background subtraction with support vector machine. IEEE Trans. Pattern Anal. Mach. Intell. 34(5), 1017–1023 (2012)

    Article  Google Scholar 

  18. Henson, C., Thirunarayan, K., Sheth, A.: An ontological approach to focusing attention and enhancing machine perception on the web. Appl. Ontol. 6(4), 345–376 (2011)

    Google Scholar 

  19. Langley, P.: Explainable agency in human-robot interaction. In: AAAI Fall Symposium Series (2016)

    Google Scholar 

  20. Liu, A., Zhang, G., Lu, J., Lu, N., Lin, C.-T.: An online competence-based concept drift detection algorithm. In: Kang, B.H., Bai, Q. (eds.) AI 2016. LNCS (LNAI), vol. 9992, pp. 416–428. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-50127-7_36

    Chapter  Google Scholar 

  21. Lyons, J.B.: Being transparent about transparency: a model for human-robot interaction. In: AAAI Spring Symposium Series (2013)

    Google Scholar 

  22. Milliez, G., Warnier, M., Clodic, A., Alami, R.: A framework for endowing an interactive robot with reasoning capabilities about perspective-taking and belief management. In: 2014 RO-MAN: The 23rd IEEE International Symposium on Robot and Human Interactive Communication, pp. 1103–1109. IEEE (2014)

    Google Scholar 

  23. Nevatia, R.: Machine perception, p. 209. Prentice-Hall Inc., Englewood Cliffs (1982). 07632

    Google Scholar 

  24. Reiter, E., Dale, R.: Building Natural Language Generation Systems. Cambridge University Press, New York (2000)

    Book  Google Scholar 

  25. Renoux, J.: Contribution to multiagent planning for active information gathering. Ph.D. thesis, Normandie Université (2015)

    Google Scholar 

  26. Renoux, J., Mouaddib, A.-I., LeGloannec, S.: Distributed decision-theoretic active perception for multi-robot active information gathering. In: Torra, V., Narukawa, Y., Endo, Y. (eds.) MDAI 2014. LNCS (LNAI), vol. 8825, pp. 60–71. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12054-6_6

    Chapter  Google Scholar 

  27. Rijsbergen, C.J.V.: Information Retrieval, 2nd edn. Butterworth-Heinemann, Newton (1979)

    MATH  Google Scholar 

  28. Roussel, S., Cholvy, L.: Cooperative interpersonal communication and relevant information. In: ESSLLI Workshop on Logical Methods for Social Concepts, Bordeaux. Citeseer (2009)

    Google Scholar 

  29. Saracevic, T.: Why is relevance still the basic notion in information science. In: Re: inventing Information Science in the Networked Society. Proceedings of the 14th International Symposium on Information Science (ISI 2015), pp. 26–35 (2015)

    Google Scholar 

  30. Sheth, A., Henson, C., Sahoo, S.S.: Semantic sensor web. IEEE Internet Comput. 12(4), 78–83 (2008)

    Article  Google Scholar 

  31. Spaan, M.T.J., Veiga, T.S., Lima, P.U.: Decision-theoretic planning under uncertainty with information rewards for active cooperative perception. Auton. Agents Multi-Agent Syst. 29(6), 1157–1185 (2015)

    Article  Google Scholar 

  32. St-Charles, P.L., Bilodeau, G.A., Bergevin, R.: Flexible background subtraction with self-balanced local sensitivity. In: Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2014, pp. 414–419. IEEE Computer Society, Washington, DC (2014)

    Google Scholar 

  33. Vavrečka, M., Farkaš, I., Lhotská, L.: Bio-inspired model of spatial cognition. In: Lu, B.-L., Zhang, L., Kwok, J. (eds.) ICONIP 2011. LNCS, vol. 7062, pp. 443–450. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-24955-6_53

    Chapter  Google Scholar 

  34. Wang, R., Bunyak, F., Seetharaman, G., Palaniappan, K.: Static and moving object detection using flux tensor with split Gaussian models. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 414–418 (2014)

    Google Scholar 

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Acknowledgment

This work has been funded by the MoveCare project (ID 732158), which is funded by the European Commission under the H2020 framework program for research and innovation.

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Authors and Affiliations

  1. Machine Perception and Interaction Lab, AASS, Örebro Universitet, Fakultetsgatan 1, 702 81, Örebro, Sweden

    Jennifer Renoux

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  1. Jennifer Renoux
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Correspondence to Jennifer Renoux .

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Editors and Affiliations

  1. University of Évry Val d'Essonne, Paris Saclay, France

    Francesco Belardinelli

  2. Polytechnic University of Valencia, Valencia, Spain

    Estefanía Argente

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Renoux, J. (2018). Active Situation Reporting: Definition and Analysis. In: Belardinelli, F., Argente, E. (eds) Multi-Agent Systems and Agreement Technologies. EUMAS AT 2017 2017. Lecture Notes in Computer Science(), vol 10767. Springer, Cham. https://doi.org/10.1007/978-3-030-01713-2_6

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  • DOI: https://doi.org/10.1007/978-3-030-01713-2_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-01712-5

  • Online ISBN: 978-3-030-01713-2

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