An Argumentative Approach to Smart Home Office Ambient Lighting

Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 737)

Abstract

Numerous studies have linked lighting conditions to how well humans have performed their daily activities. We have designed a system for the purpose of increasing productivity by tailoring the ambient lighting for various tasks, but which at the same time can gain the trust of its users by exposing the way it “thinks” through computational argumentation. With the recent emergence of smart bulbs, we now have the means for creating a software system that is able to understand which task is performed and adapt to it accordingly. While the key role of this system is to make activities more pleasurable and easier to perform, it can also predict future activities and make suitable recommendations.

Keywords

Argumentation Ambient lighting Internet of Things Smart home 

References

  1. 1.
    Amores, J., Maes, P.: Influencing human behavior by means of subliminal stimuli using scent, light and brain computer interfaces. In: Proceedings of the 9th ACM International Conference on PErvasive Technologies Related to Assistive Environments, pp. 62:1–62:4. PETRA ’16, ACM, New York, NY, USA (2016). doi: 10.1145/2910674.2935853
  2. 2.
    Bench-Capon, T.J., Dunne, P.E.: Argumentation in artificial intelligence. Artif. Intell. 171(10–15), 619–641 (2007)MathSciNetCrossRefMATHGoogle Scholar
  3. 3.
    Bistarelli, S., Santini, F.: A common computational framework for semiring-based argumentation systems1, 2. In: ECAI 2010: 19th European Conference on Artificial Intelligence, 16–20 August 2010, Lisbon, Portugal: Including Prestigious Applications of Artificial Intelligence (PAIS-2010): Proceedings. vol. 215, p. 131. IOS Press (2010)Google Scholar
  4. 4.
    Biswas, D., Szocs, C., Chacko, R., Wansink, B.: Shining light on atmospherics: how ambient light influences food choices. J. Mark. Res. 54(1), 111–123 (2017). doi: 10.1509/jmr.14.0115
  5. 5.
    Diederiks, E.M.A., Hoonhout, H.J.C.M.: Radical innovation and end-user involvement: the ambilight case. Knowl. Technol. Policy 20(1), 31–38 (2007). doi: 10.1007/s12130-007-9002-z
  6. 6.
    Dumoulin, J., Affi, D., Mugellini, E., Abou Khaled, O., Bertini, M., Del Bimbo, A.: Movie’s affect communication using multisensory modalities. In: Proceedings of the 23rd ACM International Conference on Multimedia, pp. 739–740. MM ’15, ACM, New York, NY, USA (2015). doi: 10.1145/2733373.2807965
  7. 7.
    Dung, P.M.: On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games. Artif. Intell. 77(2), 321–357 (1995)MathSciNetCrossRefMATHGoogle Scholar
  8. 8.
    Dung, P.M., Kowalski, R.A., Toni, F.: Argumentation in Artificial Intelligence, chap. Assumption-Based Argumentation, pp. 199–218. Springer US, Boston, MA (2009). doi: 10.1007/978-0-387-98197-0_10
  9. 9.
    Dunne, P.E., Hunter, A., McBurney, P., Parsons, S., Wooldridge, M.: Inconsistency tolerance in weighted argument systems. In: Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems-Volume 2, pp. 851–858. International Foundation for Autonomous Agents and Multiagent Systems (2009)Google Scholar
  10. 10.
    Fox, J., Glasspool, D., Patkar, V., Austin, M., Black, L., South, M., Robertson, D., Vincent, C.: Delivering clinical decision support services: there is nothing as practical as a good theory. J. Biomed. Inform. 43(5), 831–843 (2010)CrossRefGoogle Scholar
  11. 11.
    Holthaus, P., Leichsenring, C., Bernotat, J., Richter, V., Pohling, M., Carlmeyer, B., Köster, N., Meyer zu Borgsen, S., Zorn, R., Schiffhauer, B., et al.: How to adress smart homes with a social robot? a multi-modal corpus of user interactions with an intelligent environment. In: Proceedings of the 10th Language Resources and Evaluation Conference (2016)Google Scholar
  12. 12.
    Küller, R., Ballal, S., Laike, T., Mikellides, B., Tonello, G.: The impact of light and colour on psychological mood: a cross-cultural study of indoor work environments. Ergonomics 49(14), 1496–1507 (2006). doi: 10.1080/00140130600858142, pMID: 17050390
  13. 13.
    Mocanu, A.: Envisioning a collaborative smart home solution based on argumentative dialogues. In: Proceedings of the 7th Balkan Conference on Informatics Conference, p. 23. ACM (2015)Google Scholar
  14. 14.
    Morgner, P., Mattejat, S., Benenson, Z.: All your bulbs are belong to us: investigating the current state of security in connected lighting systems. CoRR abs/1608.03732 (2016). arXiv:1608.03732
  15. 15.
    Pfleging, B., Fekety, D.K., Schmidt, A., Kun, A.L.: A model relating pupil diameter to mental workload and lighting conditions. In: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. pp. 5776–5788. CHI ’16, ACM, New York, NY, USA (2016). doi: 10.1145/2858036.2858117
  16. 16.
    Ronen, E., O’Flynn, C., Shamir, A., Weingarten, A.O.: IoT goes nuclear: creating a zigbee chain reaction. Cryptology ePrint Archive, Report 2016/1047 (2016). http://eprint.iacr.org/2016/1047
  17. 17.
    Weffers-Albu, A., de Waele, S., Hoogenstraaten, W., Kwisthout, C.: Immersive tv viewing with advanced ambilight. In: 2011 IEEE International Conference on Consumer Electronics (ICCE), pp. 753–754, Jan 2011Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.University of CraiovaCraiovaRomania

Personalised recommendations