Stream Reasoning to Improve Decision-Making in Cognitive Systems - Publikacja - MOST Wiedzy

Wyszukiwarka

Stream Reasoning to Improve Decision-Making in Cognitive Systems

Abstrakt

ABSTRACT Cognitive Vision Systems have gained a lot of interest from industry and academia recently, due to their potential to revolutionize human life as they are designed to work under complex scenes, adapting to a range of unforeseen situations, changing accordingly to new scenarios and exhibiting prospective behavior. The combination of these properties aims to mimic the human capabilities and create more intelligent and efficient environments. Contextual information plays an important role when the objective is to reason such as humans do, as it can make the difference between achieving a weak, generalized set of outputs and a clear, target and confident understanding of a given situation. Nevertheless, dealing with contextual information still remains a challenge in cognitive systems applications due to the complexity of reasoning about it in real time in a flexible but yet efficient way. In this paper, we enrich a cognitive system with contextual information coming from different sensors and propose the use of stream reasoning to integrate/process all these data in real time, and provide a better understanding of the situation in analysis, therefore improving decision-making. The proposed approach has been applied to a Cognitive Vision System for Hazard Control (CVP-HC) which is based on Set of Experience Knowledge Structure (SOEKS) and Decisional DNA (DDNA) and has been designed to ensure that workers remain safe and compliant with Health and Safety policy for use of Personal Protective Equipment (PPE).

Cytowania

  • 1

    CrossRef

  • 0

    Web of Science

  • 1

    Scopus

Autorzy (5)

Cytuj jako

Pełna treść

pobierz publikację
pobrano 8 razy
Wersja publikacji
Accepted albo Published Version
Licencja
Copyright (2020 Taylor & Francis Group, LLC)

Słowa kluczowe

Informacje szczegółowe

Kategoria:
Publikacja w czasopiśmie
Typ:
artykuły w czasopismach
Opublikowano w:
CYBERNETICS AND SYSTEMS nr 51, strony 214 - 231,
ISSN: 0196-9722
Język:
angielski
Rok wydania:
2020
Opis bibliograficzny:
de Oliveira C., Giustozzi F., Zanni-Merk C., Sanin C., Szczerbicki E.: Stream Reasoning to Improve Decision-Making in Cognitive Systems// CYBERNETICS AND SYSTEMS -Vol. 51,iss. 2 (2020), s.214-231
DOI:
Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.1080/01969722.2019.1705553
Bibliografia: test
  1. Sanin, C., Haoxi, Z., Shafiq, I., Waris, M. M., de Oliveira, C. S., & Szczerbicki, E.: Experience based knowledge representation for Internet of Things and Cyber Physical Systems with case studies. Future Generation Computer Systems, 2018. otwiera się w nowej karcie
  2. Vernon, D.: The space of cognitive vision. In Cognitive Vision Systems, 7-24, Springer, Berlin, Heidelberg, 2006. otwiera się w nowej karcie
  3. Chmaj, J.: Contextual Knowledge: Your Key to Building Effective Knowledge Tools, KM World, 2019.
  4. Gregory, R. L. (1973). Eye and brain: The psychology of seeing. McGraw-Hill. otwiera się w nowej karcie
  5. Brézillon, P. (2003). Representation of procedures and practices in contextual graphs. The Knowledge Engineering Review, 18(2), 147-174. otwiera się w nowej karcie
  6. Bauckhage, C., Wachsmuth, S., Hanheide, M., Wrede, S., Sagerer, G., Heidemann, G., & Ritter, H. (2008). The visual active memory perspective on integrated recognition systems. Image and Vision Computing, 26(1), 5-14. otwiera się w nowej karcie
  7. Crowley, J. L., Coutaz, J., Rey, G., & Reignier, P. (2002, September). Perceptual components for context aware computing. In International conference on ubiquitous computing (pp. 117-134). Springer, Berlin, Heidelberg. otwiera się w nowej karcie
  8. Beck, H., Dao-Tran, M., Eiter, T., & Folie, C. (2018). Stream Reasoning with LARS. KI- Künstliche Intelligenz, 32(2-3), 193-195. otwiera się w nowej karcie
  9. Sanin, C., Toro, C., Haoxi, Z., Sanchez, E., Szczerbicki, E., Carrasco, E.,. & Man-cilla-Amaya, L. (2012). Decisional DNA: A multi-technology shareable knowledge structure for decisional experience. Neurocomputing, 88, 42-53. otwiera się w nowej karcie
  10. C. Sanin, E. Szczerbicki, Experience-based Knowledge Representation SOEKS. Cybernet Sys. 40(2) (2009) 99-122. otwiera się w nowej karcie
  11. Sanin, C., Szczerbicki, E.: Decisional DNA and the smart knowledge management system: A process of transforming information into knowledge. In Techniques and tool for the design and implementation of enterprise information systems, ed. A. Gunasekaran, 149-175. New York: IGI Global (2008). otwiera się w nowej karcie
  12. Han, S., & Lee, S. (2013). A vision-based motion capture and recognition framework for behavior-based safety management. Automation in Construction, 35, 131-141. otwiera się w nowej karcie
  13. Ciresan, D. C., Meier, U., Masci, J., Maria Gambardella, L., & Schmidhuber, J. (2011, July). otwiera się w nowej karcie
  14. Flexible, high performance convolutional neural networks for image classification. In IJCAI Proceedings-International Joint Conference on Artificial Intelligence (Vol. 22, No. 1, p. 1237). otwiera się w nowej karcie
  15. Little, S., Jargalsaikhan, I., Clawson, K., Nieto, M., Li, H., Direkoglu, C., ... & Liu, J. (2013, April). An information retrieval approach to identifying infrequent events in surveillance video. In Proceedings of the 3rd ACM conference on International conference on multimedia retrieval (pp. 223-230). ACM. otwiera się w nowej karcie
  16. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097-1105). otwiera się w nowej karcie
  17. Mosberger, R., Andreasson, H., & Lilienthal, A. J. (2013, November). Multi-human tracking using high-visibility clothing for industrial safety. In Intelligent Robots and Systems (IROS), 2013 IEEE/RSJ International Conference on (pp. 638-644). IEEE. otwiera się w nowej karcie
  18. Chen, L., Hoey, J., Nugent, C. D., Cook, D. J., & Yu, Z. (2012). Sensor-based activity recognition. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Re-views), 42(6), 790-808.
  19. Zambrano, A., Toro, C., Nieto, M., Sotaquirá, R., Sanín, C., Szczerbicki, E.: Video semantic analysis framework based on run-time production rules -towards cognitive vision. J. Univ. Comput. Sci. 21(6), 856-870 (2015).
  20. S. Aditya, Y. Yang, C. Baral, Y. Aloimonos and C. Fermuller, "Image Understanding using vision and reasoning through Scene Description Graph," Computer Vision and Image Understanding, pp. 1-13, 2017. otwiera się w nowej karcie
  21. Hollnagel, E., & Woods, D. D. (2005). Joint cognitive systems: Foundations of cognitive systems engineering. CRC Press. otwiera się w nowej karcie
  22. Cole, G. S. (1990). Tort liability for artificial intelligence and expert systems. Computer/LJ, 10, 127. otwiera się w nowej karcie
  23. Ashby, W. R. (1961). An introduction to cybernetics. Chapman & Hall Ltd. otwiera się w nowej karcie
  24. Yu, Y., Pan, G., Gong, Y., Xu, K., Zheng, N., Hua, W., ... & Wu, Z. (2016). Intelligence- augmented rat cyborgs in maze solving. PloS one, 11(2), e0147754. otwiera się w nowej karcie
  25. Pathak, N. (2017). The Future of AI. In Artificial Intelligence for. NET: Speech, Language, and Search (pp. 247-259). Apress, Berkeley, CA. otwiera się w nowej karcie
  26. Vernon, D.: The space of cognitive vision. In Cognitive Vision Systems, 7-24, Springer, Berlin, Hei-delberg (2006). otwiera się w nowej karcie
  27. Brézillon, P., & Pomerol, J. C.: Contextual knowledge and proceduralized context. In Pro- ceedings of the AAAI-99 Workshop on Modeling Context in AI Applications, Orlando, Florida, USA, July. AAAI Technical Report (1999).
  28. Sheth, A., Henson, C., Sahoo, S.S., 2008. Semantic Sensor Web. IEEE Internet Computing 12, 78-83 otwiera się w nowej karcie
  29. Stuckenschmidt, H., Ceri, S., Della Valle, E., Harmelen, F., Milano, P., 2019. Towards expressive stream reasoning. Proceedings of the Dagstuhl Seminar on Semantic Aspects of Sensor Networks.
  30. Haller, A., Janowicz, K., Cox, S.J., Lefranc¸ois, M., Taylor, K., Le Phuoc, D., Lieberman, J., Garc´ıa-Castro, R., Atkinson, R., Stadler, C., 2018. The modular SSN ontology: A joint W3C and OGC standard specifying the semantics of sensors, observations, sampling, and actuation. Semantic Web , 1-24. otwiera się w nowej karcie
  31. Tallevi-Diotallevi, S., Kotoulas, S., Foschini, L., Lecue, F., Corradi, A., 2013. Real- Time Urban Monitoring in Dublin Using Semantic and ´ Stream Technologies, in: The Semantic Web -ISWC 2013, Springer Berlin Heidelberg. pp. 178-194. otwiera się w nowej karcie
  32. Lecue, F., Kotoulas, S., Aonghusa, P.M., 2012. Capturing the Pulse of Cities: Opportunity and Research Challenges for Robust Stream Data ´ Reasoning, in: Semantic Cities @ AAAI.
  33. Calbimonte, J.P., Ranvier, J.E., Dubosson, F., Aberer, K., 2017. Semantic representation and processing of hypoglycemic events derived from wearable sensor data. Journal of Ambient Intelligence and Smart Environments 9, 97-109. otwiera się w nowej karcie
  34. Shojanoori, R., Juric, R., 2013. Semantic Remote Patient Monitoring System. Telemedicine journal and e-health : the official journal of the American Telemedicine Association 19. otwiera się w nowej karcie
  35. Santipantakis, G.M., Vlachou, A., Doulkeridis, C., Artikis, A., Kontopoulos, I., Vouros, G.A., 2018. A Stream Reasoning System for Maritime Monitoring, in: 25th International Symposium on Temporal Representation and Reasoning (TIME 2018), Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik, Dagstuhl, Germany. pp. 20:1-20:17.
  36. Ereteo, G., Buffa, M., Gandon, F., Corby, O., 2009. Analysis of a real online social network using semantic web frameworks, in: Lecture Notes in Computer Science.
  37. Mika, P., 2005. Flink: Semantic Web technology for the extraction and analysis of social networks. Journal of Web Semantics 3, 211 -223. otwiera się w nowej karcie
  38. Barbieri, D.F., Braga, D., Ceri, S., Della Valle, E., Grossniklaus, M., 2010. C-SPARQL: a continuous query language for RDF data streams. International Journal of Semantic Computing 4, 3-25. otwiera się w nowej karcie
  39. Department of the Environment and Heritag, 2005. National standards for criteria air pollutants 1 in Australia. otwiera się w nowej karcie
  40. Giustozzi, F., Saunier, J., & Zanni-Merk, C. (2019). Abnormal Situations Interpretation in Industry 4.0 using Stream Reasoning. Procedia Computer Science, 159, 620- 629. otwiera się w nowej karcie
Weryfikacja:
Politechnika Gdańska

wyświetlono 14 razy

Publikacje, które mogą cię zainteresować

Meta Tagi