Abstract
Within what is called the Fourth Industrial Revolution, one of the problems that companies frequently are experiencing, in order to ensure themselves, their products and services over time, is the need for continuous employee training. If continuing training is a problem, e-learning could represent a natural solution. However, to be effective, e-learning should be both a company’s instrument, which allows monitoring and provides reliable results and an easily accessible tool to the end-user. It should provide an agile, simplified and meaningful path on an educational, cognitive and relational level. Chatbots are a tool that is used both in the e-learning sector and in the industry 4.0 paradigm. The chatbots could allow to build individual learning paths and monitoring the learning phase. This paper aims to propose a system capable of providing a constant, reliable and friendly help through a practical and helpful bot, which takes advantage of NLP techniques. In particular, the proposed chatbot acts as a reminder following the user during his company training, ready to provide, when needed, useful teaching material to complete the tailored educational path. A prototype has been developed and tested in the real scenario with promising results.
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References
Y. Lu, Industry 4.0: A survey on technologies, applications and open research issues. J. Ind. Inf. Integr. (2017). https://doi.org/10.1016/j.jii.2017.04.005
L. Da Xu, L. Duan, Big data for cyber physical systems in industry 4.0: a survey. Enterp. Inf. Syst. (2019). https://doi.org/10.1080/17517575.2018.1442934
M. Gaeta, V. Loia, S. Tomasiello, A generalized functional network for a classifier-quantifiers scheme in a gas-sensing system. Int. J. Intell. Syst. (2013). https://doi.org/10.1002/int.21613
F. Colace, M. De Santo, M. Lombardi, F. Pascale, D. Santaniello, A. Tucker, A multilevel graph approach for predicting bicycle usage in London area, in Fourth International Congress on Information and Communication Technology. Advances in Intelligent Systems and Computing, vol 1027, ed. by X.S. Yang, S. Sherratt, N. Dey, A. Joshi (Springer, Singapore, 2020), pp. 353–362. https://doi.org/10.1007/978-981-32-9343-4_28
F. Colace, M. Lombardi, F. Pascale, D. Santaniello, A. Tucker, P. Villani, MuG: A multilevel graph representation for big data interpretation, in 2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS), pp. 1408–1413, June 2018. https://doi.org/10.1109/hpcc/smartcity/dss.2018.00233
F. Abate, M. Carratù, C. Liguori, M. Ferro, V. Paciello, Smart meter for the IoT, in I2MTC 2018—2018 IEEE International Instrumentation and Measurement Technology Conference: Discovering New Horizons in Instrumentation and Measurement, Proceedings (2018), pp. 1–6. https://doi.org/10.1109/i2mtc.2018.8409838
F. Amato, F. Moscato, V. Moscato, F. Colace, Improving security in cloud by formal modeling of IaaS resources. Futur. Gener. Comput. Syst. (2018). https://doi.org/10.1016/j.future.2017.08.016
D. Gorecky, M. Schmitt, M. Loskyll, D. Zühlke, Human-machine-interaction in the industry 4.0 era, in Proceedings—2014 12th IEEE International Conference on Industrial Informatics, INDIN 2014 (2014). https://doi.org/10.1109/indin.2014.6945523
S. Quarteroni, Natural language processing for industry: ELCA’s experience. Informatik-Spektrum (2018). https://doi.org/10.1007/s00287-018-1094-1
W. Rahane, S. Patil, K. Dhondkar, T. Mate, Artificial intelligence based Solarbot, in Proceedings of the International Conference on Inventive Communication and Computational Technologies, ICICCT 2018 (2018). https://doi.org/10.1109/icicct.2018.8473172
A. Chawla, A. Varshney, M.S. Umar, H. Javed, “ProBot: An online aid to procurement, in Proceedings of the 2018 International Conference on System Modeling and Advancement in Research Trends, SMART 2018 (2018). https://doi.org/10.1109/sysmart.2018.8746954
S. Mantravadi, A.D. Jansson, C. Møller, User-Friendly MES Interfaces: Recommendations for an AI-Based Chatbot Assistance in Industry 4.0 Shop Floors (2020), pp. 189–201. https://doi.org/10.1007/978-3-030-42058-1_16
F. Colace, M. De Santo, M. Lombardi, F. Pascale, A. Pietrosanto, S. Lemma, Chatbot for e-learning: A case of study. Int. J. Mech. Eng. Robot. Res. (2018). https://doi.org/10.18178/ijmerr.7.5.528-533
F. Amato, A. Mazzeo, V. Moscato, A. Picariello, Semantic Management of Multimedia Documents for E-Government Activity, in 2009 International Conference on Complex, Intelligent and Software Intensive Systems (2009), pp. 1193–1198. https://doi.org/10.1109/cisis.2009.195
A.C. Graesser, P. Chipman, B.C. Haynes, A. Olney, Auto tutor: An intelligent tutoring system with mixed-initiative dialogue. IEEE Trans. Educ. (2005). https://doi.org/10.1109/TE.2005.856149
N.T. Heffernan, E.A. Croteau, Web-based evaluations showing differential learning for tutorial strategies employed by the MS. Lindquist tutor. Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics) (2004). https://doi.org/10.1007/978-3-540-30139-4_46
F. Colace, M. De Santo, M. Lombardi, D. Santaniello, CHARS: A cultural heritage adaptive recommender system, in Proceedings of the 1st ACM International Workshop on Technology Enablers and Innovative Applications for Smart Cities and Communities—TESCA’19 (2019), pp. 58–61. https://doi.org/10.1145/3364544.3364830
M. Lombardi, F. Pascale, D. Santaniello, An application for cultural heritage using a Chatbot, in 2019 2nd International Conference on Computer Applications & Information Security (ICCAIS) (2019), pp. 1–5. https://doi.org/10.1109/cais.2019.8769525
A. Kerry, R. Ellis, S. Bull, Conversational agents in e-learning, in Applications and Innovations in Intelligent Systems XVI—Proceedings of AI 2008, the 28th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence (2009). https://doi.org/10.1007/978-1-84882-215-3-13
F. Clarizia, S. Lemma, M. Lombardi, F. Pascale, A mobile context-aware information system to support tourism events, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2017). https://doi.org/10.1007/978-3-319-57186-7_40
F. Daniel, M. Matera, E. Quintarelli, L. Tanca, V. Zaccaria, Context-Aware Access to Heterogeneous Resources Through On-the-Fly Mashups (2018), pp. 119–134. https://doi.org/10.1007/978-3-319-91563-0_8
F. Colace, M. De Santo, M. Lombardi, R. Mosca, D. Santaniello, A multilayer approach for recommending contextual learning paths. J. Internet Serv. Inf. Secur. 2 (May), 91–102 (2020). https://doi.org/10.22667/jisis.2020.05.31.091
R. Krestel, P. Fankhauser, W. Nejdl, Latent Dirichlet allocation for tag recommendation, in RecSys’09—Proceedings of the 3rd ACM Conference on Recommender Systems (2009). https://doi.org/10.1145/1639714.1639726
M. Casillo, F. Clarizia, G. D’Aniello, M. De Santo, M. Lombardi, D. Santaniello, CHAT-Bot: A cultural heritage aware teller-bot for supporting touristic experiences. Pattern Recognit. Lett. (2020). https://doi.org/10.1016/j.patrec.2020.01.003
F. Colace, M. De Santo, L. Greco, F. Amato, V. Moscato, A. Picariello, Terminological ontology learning and population using latent dirichlet allocation. J. Vis. Lang. Comput. (2014). https://doi.org/10.1016/j.jvlc.2014.11.001
F. Amato, V. Moscato, A. Picariello, F. Colace, M. De Santo, F.A. Schreiber, L. Tanca, Big data meets digital cultural heritage: Design and implementation of SCRABS, a smart context-aware browsing assistant for cultural environments. J. Comput. Cult. Herit. (2017). https://doi.org/10.1145/3012286
M. Casillo, F. Clarizia, F. Colace, M. Lombardi, F. Pascale, D. Santaniello, An approach for recommending contextualized services in e-Tourism. Information 10(5), 180 (2019). https://doi.org/10.3390/info10050180
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Casillo, M., Colace, F., De Santo, M., Lombardi, M., Santaniello, D. (2021). A Chatbot for Training Employees in Industry 4.0. In: Visvizi, A., Lytras, M.D., Aljohani, N.R. (eds) Research and Innovation Forum 2020. RIIFORUM 2020. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-030-62066-0_30
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