A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Advanced Search
Volume 6 Issue 3
May  2019

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 15.3, Top 1 (SCI Q1)
    CiteScore: 23.5, Top 2% (Q1)
    Google Scholar h5-index: 77， TOP 5
Turn off MathJax
Article Contents
Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2019  >  6(3): 610-622
Jonathan M. Garibaldi, "The Need for Fuzzy AI," IEEE/CAA J. Autom. Sinica, vol. 6, no. 3, pp. 610-622, May 2019. doi: 10.1109/JAS.2019.1911465
Citation: Jonathan M. Garibaldi, "The Need for Fuzzy AI," IEEE/CAA J. Autom. Sinica, vol. 6, no. 3, pp. 610-622, May 2019. doi: 10.1109/JAS.2019.1911465
shu

The Need for Fuzzy AI

doi: 10.1109/JAS.2019.1911465
Funds:  This work was supported by University of Nottingham
More Information
    Abstract
  • Artificial intelligence (AI) is once again a topic of huge interest for computer scientists around the world. Whilst advances in the capability of machines are being made all around the world at an incredible rate, there is also increasing focus on the need for computerised systems to be able to explain their decisions, at least to some degree. It is also clear that data and knowledge in the real world are characterised by uncertainty. Fuzzy systems can provide decision support, which both handle uncertainty and have explicit representations of uncertain knowledge and inference processes. However, it is not yet clear how any decision support systems, including those featuring fuzzy methods, should be evaluated as to whether their use is permitted. This paper presents a conceptual framework of indistinguishability as the key component of the evaluation of computerised decision support systems. Case studies are presented in which it has been clearly demonstrated that human expert performance is less than perfect, together with techniques that may enable fuzzy systems to emulate human-level performance including variability. In conclusion, this paper argues for the need for " fuzzy AI” in two senses: (i) the need for fuzzy methodologies (in the technical sense of Zadeh’s fuzzy sets and systems) as knowledge-based systems to represent and reason with uncertainty; and (ii) the need for fuzziness (in the non-technical sense) with an acceptance of imperfect performance in evaluating AI systems.

     

    • FullText(HTML)
  • loading
    • References(34)
  • [1]
    F.-H. Hsu, Behind Deep Blue: Building the Computer that Defeated the World Chess Champion. Princeton University Press, 2004.
    [2]
    M. Minsky and S. Papert, Perceptrons: An Introduction to Computational Geometry. MIT Press, 1969.
    [3]
    D. C. Cireşan, U. Meier, J. Masci, L. M. Gambardella, and J. Schmid huber, " Flexible, high performance convolutional neural networks for image classification,” in Proceedings of the 22nd International Joint Conference on Artificial Intelligence, 2011, pp. 1237–1242.
    [4]
    D. C. Cireşan, U. Meier, and J. Schmidhuber, " Multi-column deep neural networks for image classification,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, June 2012, pp. 3642–3649.
    [5]
    A. M. Turing, " Intelligent machinery,” National Physical Laboratory, Tech. Rep., 1948.
    [6]
    Information Innovation Office, " Explainable artificial intelligence (xai),” Defense Advanced Research Projects Agency, Tech. Rep., 2016.
    [7]
    The European Parliament and The Council of the European Union, " General data protection regulation,” Official Journal of the European Union, Tech. Rep., 2016.
    [8]
    W. Heisenberg, " Über den anschaulichen inhalt der quantentheoretischen kinematik und mechanik,” Zeitschrift fur Physik, vol. 43, pp. 172–198, Mar. 1927. doi: 10.1007/BF01397280
    [9]
    L. A. Zadeh, " Fuzzy sets,” Information and Control, vol. 8, pp. 338–353, 1965. doi: 10.1016/S0019-9958(65)90241-X
    [10]
    L. A. Zadeh, " Outline of a new approach to the analysis of complex systems and decision processes,” IEEE Transactions on Systems, Man, and Cybernetics, vol. SMC-3, no. 1, pp. 28–44, 1973. doi: 10.1109/TSMC.1973.5408575
    [11]
    L. A. Zadeh, " The concept of a linguistic variable and its application to approximate reasoning–I,” Information Sciences, vol. 8, pp. 199–249, 1975. doi: 10.1016/0020-0255(75)90036-5
    [12]
    L. A. Zadeh, " The concept of a linguistic variable and its application to approximate reasoning–II,” Information Sciences, vol. 8, pp. 301–357, 1975. doi: 10.1016/0020-0255(75)90046-8
    [13]
    L. A. Zadeh, " The concept of a linguistic variable and its application to approximate reasoning–III,” Information Sciences, vol. 9, pp. 43–80, 1975. doi: 10.1016/0020-0255(75)90017-1
    [14]
    E. H. Mamdani, " Application of fuzzy algorithms for control of simple dynamic plant,” Proceedings of the Institution of Electrical Engineers, vol. 121, no. 12, pp. 1585–1588, Dec. 1974. doi: 10.1049/piee.1974.0328
    [15]
    E. H. Mamdani and S. Assilian, " An experiment in linguistic synthesis with a fuzzy logic controller,” International Journal of Man-Machine Studies, vol. 7, no. 1, pp. 1–13, 1975. doi: 10.1016/S0020-7373(75)80002-2
    [16]
    L. Magdalena, Fuzzy Rule-Based Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015, pp. 203–218.
    [17]
    H. Liu, P. Burnap, W. Alorainy, and M. L. Williams, " A fuzzy approach to text classification with two-stage training for ambiguous instances,” IEEE Transactions on Computational Social Systems, vol. 6, no. 2, pp. 227–240, April. 2019. doi: 10.1109/TCSS.2019.2892037
    [18]
    H. Mo, K. Yan, X. Zhao, Y. Zeng, X. Wang, and W. Fei-yue, " Type-2 fuzzy comprehension evaluation for tourist attractive competency,” IEEE Transactions on Computational Social Systems, vol. 6, no. 1, pp. 96–102, 2019. doi: 10.1109/TCSS.2019.2891306
    [19]
    J. Wang and T. Kumbasar, " Parameter optimization of interval type-2 fuzzy neural networks based on PSO and BBBC methods,” IEEE/CAA Journal of Automatica Sinica, vol. 6, no. 1, pp. 247–257, January. 2019. doi: 10.1109/JAS.2019.1911348
    [20]
    A. Rubio-Solis, P. Melin, U. Martinez-Hernandez, and G. Panoutsos, " General type-2 radial basis function neural network: A data-driven fuzzy model,” IEEE Transactions on Fuzzy Systems, vol. 27, no. 2, pp. 333–347, 2019. doi: 10.1109/TFUZZ.2018.2858740
    [21]
    J. M. Garibaldi, J. A. Westgate, E. C. Ifeachor, and K. R. Greene, " The development and implementation of an expert system for the analysis of umbilical cord blood,” Artificial Intelligence in Medicine, vol. 10, no. 2, pp. 129–144, 1997. doi: 10.1016/S0933-3657(97)00390-4
    [22]
    J. A. Westgate, J. M. Garibaldi, and K. R. Greene, " Umbilical cord blood gas analysis at delivery: A time for quality data,” BJOG: An International Journal of Obstetrics & Gynaecology, vol. 101, no. 12, pp. 1054–1063, 1994.
    [23]
    J. M. Garibaldi, " Intelligent techniques for handling uncertainty in the assessment of neonatal outcome,” Ph.D. dissertation, University of Plymouth, 1997.
    [24]
    J. Garibaldi and E. Ifeachor, The Development of a Fuzzy Expert System for the Analysis of Umbilical Cord Blood, ser. Fuzzy Systems in Medicine. Studies in Fuzziness and Soft Computing. Physica, Heidelberg, 2000, vol. 41.
    [25]
    A. M. Turing, " Computing machinery and intelligence,” Mind, vol. LIX, no. 236, pp. 433–460, October. 1950. doi: 10.1093/mind/LIX.236.433
    [26]
    R. D. F. Keith, S. Beckley, J. M. Garibaldi, J. A. Westgate, E. C. Ifeachor, and K. R. Greene, " A multicentre comparative study of 17 experts and an intelligent computer system for managing labour using the cardiotocogram,” BJOG: An International Journal of Obstetrics & Gynaecology, vol. 102, no. 9, pp. 688–700, 1995.
    [27]
    J. M. Garibaldi and T. Ozen, " Uncertain fuzzy reasoning: A case study in modelling expert decision making,” IEEE Transactions on Fuzzy Systems, vol. 15, no. 1, pp. 16–30, 2007. doi: 10.1109/TFUZZ.2006.889755
    [28]
    J. M. Garibaldi, M. Jaroszewski, and S. Musikasuwan, " Nonstationary fuzzy sets,” IEEE Transactions on Fuzzy Systems, vol. 16, no. 4, pp. 1072–1086, 2008. doi: 10.1109/TFUZZ.2008.917308
    [29]
    J. M. Garibaldi and S. Guadarrama, " Constrained type-2 fuzzy sets,” in Proceedings of the IEEE Symposium on Advances in Type-2 Fuzzy Logic Systems (T2FUZZ). IEEE, 2011, pp. 66–73.
    [30]
    J. M. Garibaldi, S.-M. Zhou, X.-Y. Wang, R. I. John, and I. O. Ellis, " Incorporation of expert variability into breast cancer treatment recommendation in designing clinical protocol guided fuzzy rule system models,” Journal of Biomedical Informatics, vol. 45, no. 3, pp. 447–459, 2012. doi: 10.1016/j.jbi.2011.12.007
    [31]
    H. Zuo, J. Lu, G. Zhang, and F. Liu, " Fuzzy transfer learning using an infinite gaussian mixture model and active learning,” IEEE Transactions on Fuzzy Systems, vol. 27, no. 2, pp. 291–303, 2019. doi: 10.1109/TFUZZ.2018.2857725
    [32]
    C. Wagner and H. Hagras, " Toward general type-2 fuzzy logic systems based on zSlices,” IEEE Transactions on Fuzzy Systems, vol. 18, no. 4, pp. 637–660, 2010. doi: 10.1109/TFUZZ.2010.2045386
    [33]
    P. D’Alterio, J. M. Garibaldi, and A. Pourabdollah, " Exploring constrained type-2 fuzzy sets,” in Proceedings of the IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2018, pp. 1–7.
    [34]
    W HO, " Global status report on road safety,” World Health Organisation. Geneva, Tech. Rep., 2015.
    • Related Articles
    • Supplements(0)

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(11)

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (4044) PDF downloads(318) Cited by()

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return