Neuro-Symbolic Hybrid Systems for Industry 4.0: A Systematic Mapping Study

  • Inés SittónEmail author
  • Ricardo S. Alonso
  • Elena Hernández-Nieves
  • Sara Rodríguez-Gonzalez
  • Alberto Rivas
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1027)


Neuro-symbolic hybrid systems (NSHS) have been used in several research areas to obtain powerful intelligent systems. A systematic mapping study was conducted, searching studies published from January 2011 to May 2018 in three author databases defining four research questions and three search strings. With the results a literature review was made to generate a map with main trends and contributions about the use of NSHS in Industry 4.0. An evaluation rubric based on the work of Petersen et al. (2015) was applied too. In a first exploratory search 544 papers was found, but only 330 had relation with research theme. After this first classification a second filter was applied to identify repeated articles or which had not relevance for solve the research questions, obtaining 118. Finally, 50 primary studies was selected. This paper is a guide aimed at researching and obtaining evidence on the shortage of publications and contributions about the use of neuro symbolic hybrid systems applied in Industry 4.0 environment.


Neuro-symbolic hybrid system (NSHS) Industry 4.0 Artificial intelligence Systematic mapping study 



This work has been supported by project IOTEC: “Development of Technological Capacities around the Industrial Application of Internet of Things (IoT)”. 0123-IOTEC-3-E. Project financed with FEDER funds, Interreg Spain-Portugal (PocTep). Inés Sittón-Candanedo has been supported by IFARHU – SENACYT scholarship program (Government of Panama).


  1. 1.
    Ramezani, J., Jassbi, J.: A hybrid expert decision support system based on artificial neural networks in process control of plaster production – an industry 4.0 perspective. In: Camarinha-Matos, Luis M., Parreira-Rocha, M., Ramezani, J. (eds.) DoCEIS 2017. IAICT, vol. 499, pp. 55–71. Springer, Cham (2017). Scholar
  2. 2.
    Fdez-Riverola, F., Corchado, J.M.: Sistemas híbridos neuro-simbólicos: una revisión. Rev. Iberoam. Intel. Artif. 4, 12–26 (2000)Google Scholar
  3. 3.
    Sahin, S., Tolun, M.R., Hassanpour, R.: Hybrid expert systems: a survey of current approaches and applications. Expert Syst. Appl. 39, 4609–4617 (2012)CrossRefGoogle Scholar
  4. 4.
    Wortmann, A., Combemale, B., Barais, O.: A systematic mapping study on modeling for industry 4.0. In: 2017 ACM/IEEE 20th International Conference on Model Driven Engineering Languages and Systems, pp. 281–291 (2017)Google Scholar
  5. 5.
    Petersen, K., Feldt, R., Mujtaba, S., Mattsson, M.: Systematic mapping studies in software engineering. In: 12th International Conference on Evaluation and Assessment in Software Engineering, vol. 17, p. 10 (2008)Google Scholar
  6. 6.
    Petersen, K., Vakkalanka, S., Kuzniarz, L.: Guidelines for conducting systematic mapping studies in software engineering: an update. Inf. Softw. Technol. 64, 1–18 (2015)CrossRefGoogle Scholar
  7. 7.
    Salehi, S., Selamat, A., Fujita, H.: Systematic mapping study on granular computing. Knowl. Based Syst. 80, 78–97 (2015)CrossRefGoogle Scholar
  8. 8.
    Kosar, T., Bohra, S., Mernik, M.: Domain-specific languages: a systematic mapping study. Inf. Softw. Technol. 71, 77–91 (2015)CrossRefGoogle Scholar
  9. 9.
    Kitchenham, B.A., Budgen, D., Pearl Brereton, O.: Using mapping studies as the basis for further research - a participant-observer case study. Inf. Softw. Technol. 53, 638–651 (2011)CrossRefGoogle Scholar
  10. 10.
    Macchi, D., Solari, M.: Mapeo Sistemático de la Literatura sobre la Adopción de Inspecciones de Software. In: Conf. Latinoam. Informática (CLEI 2012), pp. 1–8 (2012)Google Scholar
  11. 11.
    Fdez-Riverola, F., Corchado, J.M.: Forecasting red tides using an hybrid neuro-symbolic system. AI Commun. 16, 221–233 (2003)MathSciNetGoogle Scholar
  12. 12.
    Osório, F.S., Amy, B.: INSS: a hybrid system for constructive machine learning. Neurocomputing 28, 191–205 (1999)CrossRefGoogle Scholar
  13. 13.
    Medsker, L.R.: Hybrid Intelligent Systems. Springer, Boston (2012). Scholar
  14. 14.
    Hatzilygeroudis, I., Prentzas, J.: Symbolic-neural rule based reasoning and explanation. Expert Syst. Appl. 42, 4595–4609 (2015)CrossRefGoogle Scholar
  15. 15.
    Fdez-Riverola, F., Corchado, J.M.: CBR based system for forecasting red tides. Knowl. Based Syst. 16, 321–328 (2003)CrossRefGoogle Scholar
  16. 16.
    González-Briones, A., Chamoso, P., Yoe, H., Corchado, J.M.: GreenVMAS: virtual organization based platform for heating greenhouses using waste energy from power plants. Sensors 18(3), 861 (2018)CrossRefGoogle Scholar
  17. 17.
    Vaidya, S., Ambad, P., Bhosle, S.: Industry 4.0 - a glimpse. Procedia Manuf. 20, 233–238 (2018)CrossRefGoogle Scholar
  18. 18.
    Bahrin, M.A.K., Othman, M.F., Azli, N.H.N., Talib, M.F.: Industry 4.0: a review on industrial automation and robotic. J. Teknol. 78, 137–143 (2016)Google Scholar
  19. 19.
    Chamoso, P., González-Briones, A., Rodríguez, S., Corchado, J.M.: Tendencies of technologies and platforms in smart cities: a state-of-the-art review. Wireless Commun. Mob. Comput. 2018, 17 (2018)CrossRefGoogle Scholar
  20. 20.
    Gonzalez-Briones, A., Prieto, J., De La Prieta, F., Herrera-Viedma, E., Corchado, J.M.: Energy optimization using a case-based reasoning strategy. Sensors (Basel) 18(3), 865 (2018). Scholar
  21. 21.
    Lorenz, M., Rüßmann, M., Strack, R., Lueth, K.L., Bolle, M.: Man and Machine in Industry 4.0 (2015)Google Scholar
  22. 22.
    Montalvillo, L., Díaz, O.: Requirement-driven evolution in software product lines: a systematic mapping study. J. Syst. Softw. 122, 110–143 (2016)CrossRefGoogle Scholar
  23. 23.
    Budgen, D., Turner, M., Brereton, O.P., Kitchenham, B.A.: Using mapping studies in software engineering. In: XX Annual Meeting of the Psychology of Programming Interest Group (PPIG 2008), pp. 195–204 (2008)Google Scholar
  24. 24.
    Tofan, D., Galster, M., Avgeriou, P., Schuitema, W.: Past and future of software architectural decisions – a systematic mapping study. Inf. Softw. Technol. 56, 850–872 (2014)CrossRefGoogle Scholar
  25. 25.
    Dallasega, P., Rauch, E., Linder, C.: Industry 4.0 as an enabler of proximity for construction supply chains: a systematic literature review (2018).
  26. 26.
    Liao, Y., Deschamps, F., de Loures, E.F.R., Ramos, L.F.P.: Past, present and future of Industry 4.0-a systematic literature review and research agenda proposal. Int. J. Prod. Res. 55, 3609–3629 (2017)CrossRefGoogle Scholar
  27. 27.
    Sittón, I., Rodríguez, S.: Pattern extraction for the design of predictive models in industry 4.0. In: De la Prieta, F., Vale, Z., Antunes, L., Pinto, T., Campbell, Andrew T., Julián, V., Neves, Antonio J.R., Moreno, María N. (eds.) PAAMS 2017. AISC, vol. 619, pp. 258–261. Springer, Cham (2018). Scholar
  28. 28.
    Kang, H.S., et al.: Do: smart manufacturing: past research, present findings, and future directions. Int. J. Precis. Eng. Manuf. Technol. 3, 111–128 (2016)CrossRefGoogle Scholar
  29. 29.
    Rojko, A.: Industry 4.0 concept: background and overview. Int. J. Interact. Mob. Technol. 11, 77 (2017)CrossRefGoogle Scholar
  30. 30.
    Zhong, R.Y., Xu, X., Klotz, E., Newman, S.T.: Intelligent manufacturing in the context of industry 4.0: a review. Engineering 3, 616–630 (2017)CrossRefGoogle Scholar
  31. 31.
    Hozdić, E.: Smart factory for industry 4.0: a review. Int. J. Mod. Manuf. Technol. 7, 28–35 (2015)Google Scholar
  32. 32.
    Strozzi, F., Colicchia, C., Creazza, A., Noè, C.: Literature review on the ‘Smart Factory’ concept using bibliometric tools. Int. J. Prod. Res. 55, 6572–6591 (2017)CrossRefGoogle Scholar
  33. 33.
    Buer, S.-V., Strandhagen, J.O., Chan, F.T.S.: The link between Industry 4.0 and lean manufacturing: mapping current research and establishing a research agenda. Int. J. Prod. Res. 56, 2924–2940 (2018)CrossRefGoogle Scholar
  34. 34.
    Zheng, P., et al.: Smart manufacturing systems for Industry 4.0: conceptual framework, scenarios, and future perspectives. Front. Mech. Eng. 13(2), 137–150 (2018)CrossRefGoogle Scholar
  35. 35.
    Bullón, J., Arrieta, A.G., Encinas, A.H., Dios, A.Q.: Manufacturing processes in the textile industry. Expert systems for fabrics production. Adv. Distrib. Comput. Artif. Intell. J. 6(1), 41–50 (2017). (ISSN: 2255-2863), SalamancaGoogle Scholar
  36. 36.
    Thames, L., Schaefer, D.: Industry 4.0: an overview of key benefits, technologies, and challenges. In: Thames, L., Schaefer, D. (eds.) Cybersecurity for Industry 4.0. SSAM, pp. 1–33. Springer, Cham (2017). Scholar
  37. 37.
    Oesterreich, T.D., Teuteberg, F.: Understanding the implications of digitisation and automation in the context of Industry 4.0: a triangulation approach and elements of a research agenda for the construction industry. Comput. Ind. 83, 121–139 (2016)CrossRefGoogle Scholar
  38. 38.
  39. 39.
    Liu, Y., Xu, X.: Industry 4.0 and cloud manufacturing: a comparative analysis. J. Manuf. Sci. Eng. 139, 34701 (2017)CrossRefGoogle Scholar
  40. 40.
    Yang, S., Bian, C., Li, X., Tan, L., Tang, D.: Optimized fault diagnosis based on FMEA-style CBR and BN for embedded software system. Int. J. Adv. Manuf. Technol. 94, 3441–3453 (2018)CrossRefGoogle Scholar
  41. 41.
    Kim, D., et al.: A hybrid failure diagnosis and prediction using natural language-based process map and rule-based expert system. Int. J. Comput. Commun. Control 5, 1841–9836 (2017)Google Scholar
  42. 42.
    Chang, P.-C., Lin, J.-J., Dzan, W.-Y., Chang, P.-C., Lin, J.-J., Dzan, W.-Y.: Forecasting of manufacturing cost in mobile phone products by case-based reasoning and artificial neural network models. J Intell. Manuf. 23, 517–531 (2012)CrossRefGoogle Scholar
  43. 43.
    Piltan, M., Mehmanchi, E., Ghaderi, S.F.: Proposing a decision-making model using analytical hierarchy process and fuzzy expert system for prioritizing industries in installation of combined heat and power systems. Expert Syst. Appl. 39, 1124–1133 (2012)CrossRefGoogle Scholar
  44. 44.
    Zarandi, M.H.F., Mansour, S., Hosseinijou, S.A., Avazbeigi, M.: A material selection methodology and expert system for sustainable product design. Int. J. Adv. Manuf. Technol. 57, 885–903 (2011)CrossRefGoogle Scholar
  45. 45.
    Bahrammirzaee, A., et al.: Hybrid credit ranking intelligent system using expert system and artificial neural networks. Appl. Intell. 34, 28–46 (2011)CrossRefGoogle Scholar
  46. 46.
    Yazdi, M.: Hybrid probabilistic risk assessment using Fuzzy FTA and Fuzzy AHP in a process industry. J. Fail. Anal. Prev. 17, 756–764 (2017)CrossRefGoogle Scholar
  47. 47.
    Pask, F., Lake, P., Yang, A., Tokos, H., Sadhukhan, J.: Sustainability indicators for industrial ovens and assessment using Fuzzy set theory and Monte Carlo simulation. J. Clean. Prod. 140, 1217–1225 (2017)CrossRefGoogle Scholar
  48. 48.
    Karelovic, P., Putz, E., Cipriano, A.: A framework for hybrid model predictive control in mineral processing. Control Eng. Pract. 40, 1–12 (2015)CrossRefGoogle Scholar
  49. 49.
    Sáiz-Bárcena, L., Herrero, A., Del Campo, M.A.M., Del Olmo Martínez, R.: Easing knowledge management in the power sector by means of a neuro-genetic system. Int. J. Bio-Inspired Comput. 7, 170–175 (2015)CrossRefGoogle Scholar
  50. 50.
    Fazel Zarandi, M.H., Gamasaee, R., Turksen, I.B.: A type-2 fuzzy expert system based on a hybrid inference method for steel industry. Int. J. Adv. Manuf. Technol. 71(5–8), 857–885 (2013)Google Scholar
  51. 51.
    Van Pham, H., Tran, K.D., Kamei, K.: Applications using hybrid intelligent decision support systems for selection of alternatives under uncertainty and risk. Int. J. Innov. Comput. Inf. Control 10, 39–56 (2014)Google Scholar
  52. 52.
    Shahrabi, J., Hadavandi, E., Asadi, S.: Developing a hybrid intelligent model for forecasting problems: case study of tourism demand time series. Knowl. Based Syst. 43, 112–122 (2013)CrossRefGoogle Scholar
  53. 53.
    Vogel-Heuser, B., Legat, C., Folmer, J., Schütz, D.: An assessment of the potentials and challenges in future approaches for automation software. In: Leitao, P., Karsnouskos, S. (eds.) Industrial Agents: Emerging Applications of Software Agents in Industry. p. 476. Elsevier Inc. (2015).
  54. 54.
    Prentzas, J., Hatzilygeroudis, I.: Assessment of life insurance applications: an approach integrating neuro-symbolic rule-based with case-based reasoning. Expert Syst. 33, 145–160 (2016)CrossRefGoogle Scholar
  55. 55.
    Prentzas, J., Hatzilygeroudis, I.: Using clustering algorithms to improve the production of symbolic-neural rule bases from empirical data. Int. J. Artif. Intell. Tools 27, 1850002 (2018)CrossRefGoogle Scholar
  56. 56.
    Hatzilygeroudis, I., Prentzas, J.: Symbolic-neural rule based reasoning and explanation. Expert Syst. Appl. Int. J. 42, 4595–4609 (2015)CrossRefGoogle Scholar
  57. 57.
    Kasabov, N.K.: Evolving connectionist systems for adaptive learning and knowledge discovery: trends and directions. Knowl. Based Syst. 80, 24–33 (2015)CrossRefGoogle Scholar
  58. 58.
    Prentzas, J., Hatzilygeroudis, I.: Improving efficiency of merging symbolic rules into integrated rules: splitting methods and mergability criteria. Expert Syst. Appl. 32, 244–260 (2015)CrossRefGoogle Scholar
  59. 59.
    Elhoseny, M., Abdelaziz, A., Salama, A.S., Riad, A.M., Muhammad, K., Sangaiah, A.K.: A hybrid model of Internet of Things and cloud computing to manage big data in health services applications. Future Gener. Comput. Syst. 86, 1383–1394 (2018)CrossRefGoogle Scholar
  60. 60.
    Shihabudheen, K.V., Pillai, G.N.: Recent advances in neuro-fuzzy system: a survey. Knowl. Based Syst. 152, 136–162 (2018)CrossRefGoogle Scholar
  61. 61.
    Liao, Y., Felipe Pierin Ramos, L., Saturno, M., Deschamps, F., de Freitas Rocha Loures, E., Luis Szejka, A.: The role of interoperability in the fourth industrial revolution era. IFAC PapersOnline 50, 12434–12439 (2017)CrossRefGoogle Scholar
  62. 62.
    Hermann, M., Pentek, T., Otto, B.: Design principles for industrie 4.0 scenarios (2016)Google Scholar
  63. 63.
    Trotta, D., Garengo, P.: Industry 4.0 key research topics: a bibliometric review. In: 2018 7th International Conference on Industrial Technology and Management (ICITM), pp. 113–117 (2018)Google Scholar
  64. 64.
    Simas, O., Rodrigues, J.C.: The implementation of industry 4.0: a literature review. In: Proceedings of International Conference on Computers and Industrial Engineering, CIE (2017)Google Scholar
  65. 65.
    Pereira, A.C., Romero, F.: A review of the meanings and the implications of the Industry 4.0 concept. Procedia Manuf. 13, 1206–1214 (2017)CrossRefGoogle Scholar
  66. 66.
    Martín, A.M., Marcos, M., Aguayo, F., Lama, J.R.: Smart industrial metabolism: a literature review and future directions. Procedia Manuf. 13, 1223–1228 (2017)CrossRefGoogle Scholar
  67. 67.
    Dequeant, K., Vialletelle, P., Lemaire, P., Espinouse, M.-L.: A literature review on variability in semiconductor manufacturing: the next forward leap to Industry 4.0. In: Proceedings of the 2016 Winter Simulation Conference, pp. 2598–2609 (2016)Google Scholar
  68. 68.
    Santos, C., Mehrsai, A., Barros, A.C., Araújo, M., Ares, E.: Towards industry 4.0: an overview of European strategic roadmaps. Procedia Manuf. 13, 972–979 (2017)CrossRefGoogle Scholar
  69. 69.
    Barreto, L., Amaral, A., Pereira, T.: Industry 4.0 implications in logistics: an overview. Procedia Manuf. 13, 1245–1252 (2017)CrossRefGoogle Scholar
  70. 70.
    Casado-Vara, R., Novais, P., Gil, A.B., Prieto, J., Corchado, J.M.: Distributed continuous-time fault estimation control for multiple devices in IoT networks. IEEE Access 7, 11972–11984 (2019). Scholar
  71. 71.
    Bassi, L.: Industry 4.0: Hope, hype or revolution? In: 2017 IEEE 3rd International Forum on Research and Technologies for Society and Industry (RTSI), pp. 1–6 (2017)Google Scholar
  72. 72.
    Ben Said, A., Shahzad, M.K., Zamai, E., Hubac, S., Tollenaere, M.: Towards proactive maintenance actions scheduling in the Semiconductor Industry (SI) using Bayesian approach. IFAC-PapersOnLine 49, 544–549 (2016)CrossRefGoogle Scholar
  73. 73.
    Morente-Molinera, J.A., Kou, G., González-Crespo, R., Corchado, J.M., Herrera-Viedma, E.: Solving multi-criteria group decision making problems under environments with a high number of alternatives using fuzzy ontologies and multi-granular linguistic modelling methods. Knowl. Based Syst. 137, 54–64 (2017)CrossRefGoogle Scholar
  74. 74.
    Casado-Vara, R., Chamoso, P., De la Prieta, F., Prieto, J., Corchado, J.M.: Non-linear adaptive closed-loop control system for improved efficiency in IoT-blockchain management. Inf. Fusion 49, 227–239 (2019)CrossRefGoogle Scholar
  75. 75.
    Pham, H.V., Tran, K.D., Kamei, K.: Applications using hybrid intelligent decision support systems for selection of alternatives under uncertainty and risk. Int. J. Innov. Comput. Inf. Control ICIC 10, 39–56 (2014)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Inés Sittón
    • 1
    Email author
  • Ricardo S. Alonso
    • 1
  • Elena Hernández-Nieves
    • 1
  • Sara Rodríguez-Gonzalez
    • 1
  • Alberto Rivas
    • 1
  1. 1.IoT Digital Innovation HubUniversity of SalamancaSalamancaSpain

Personalised recommendations