Inquiring Natural Language Processing Capabilities on Robotic Systems through Virtual Assistants: A Systemic Approach

Authors

  • Ioannis Giachos

    Department of Industrial Design & Production Engineering, University of West Attica, Egaleo, Athens, 12241, Greece
  • Evangelos C. Papakitsos

    Department of Industrial Design & Production Engineering, University of West Attica, Egaleo, Athens, 12241, Greece
  • Petros Savvidis

    Department of Industrial Design & Production Engineering, University of West Attica, Egaleo, Athens, 12241, Greece

    Research Laboratory of Electronic Automation, Telematics and Cyber-Physical Systems, University of West Attica, Egaleo, Athens, 12241, Greece
  • Nikolaos Laskaris

    Department of Industrial Design & Production Engineering, University of West Attica, Egaleo, Athens, 12241, Greece

DOI:

https://doi.org/10.30564/jcsr.v5i2.5537
Received: 9 March 2023 | Revised: 4 April 2023 | Accepted: 7 April 2023 | Published Online: 18 April 2023

Abstract

This paper attempts to approach the interface of a robot from the perspective of virtual assistants. Virtual assistants can also be characterized as the mind of a robot, since they manage communication and action with the rest of the world they exist in. Therefore, virtual assistants can also be described as the brain of a robot and they include a Natural Language Processing (NLP) module for conducting communication in their human-robot interface. This work is focused on inquiring and enhancing the capabilities of this module. The problem is that nothing much is revealed about the nature of the human-robot interface of commercial virtual assistants. Therefore, any new attempt of developing such a capability has to start from scratch. Accordingly, to include corresponding capabilities to a developing NLP system of a virtual assistant, a method of systemic semantic modelling is proposed and applied. For this purpose, the paper briefly reviews the evolution of virtual assistants from the first assistant, in the form of a game, to the latest assistant that has significantly elevated their standards. Then there is a reference to the evolution of their services and their continued offerings, as well as future expectations. The paper presents their structure and the technologies used, according to the data provided by the development companies to the public, while an attempt is made to classify virtual assistants, based on their characteristics and capabilities. Consequently, a robotic NLP interface is being developed, based on the communicative power of a proposed systemic conceptual model that may enhance the NLP capabilities of virtual assistants, being tested through a small natural language dictionary in Greek.

Keywords:

Natural language processing, Robotic systems, Virtual assistant, Human-robot interface

References

[1] Giachos, I., Piromalis, D., Papoutsidakis, M., et al., 2020. A contemporary survey on intelligent human-robot interfaces focused on natural language processing. International Journal of Research in Computer Applications and Robotics. 8(7), 1-20.

[2] Markowitz, J., 2003. Toys that Have a Voice. Speech Technology Magazine [Internet]. Available from: https://www.speechtechmag.com/Articles/ReadArticle.aspx?ArticleID=30031

[3] Juang, B.H., Rabiner, L.R., 2005. Automatic speech recognition—a brief history of the technology development. Rutgers University and the University of California, Santa Barbara. 1, 67.

[4] Gold, B., 1990. A history of Vocoder research at Lincoln Laboratory. The Lincoln Laboratory Journal. 3(2), 163-202.

[5] Lindsay, D., 1997. Talking head. American Heritage of Invention & Technology. 13(1).

[6] Suryadi, S., 2006. The “talking machine” comes to the Dutch East Indies: The arrival of Western media technology in Southeast Asia. Bijdragen Tot de Taal-,Land-En Volkenkunde/Journal of the Humanities and Social Sciences of Southeast Asia and Oceania. 162(2/3), 269-305. DOI: https://doi.org/10.1163/22134379-90003668

[7] Deryugina, O.V., 2010. Chatterbots. Scientific and Technical Information Processing. 37(2), 143-147. DOI: https://doi.org/10.3103/S0147688210020097

[8] Ahirwar, G.K., 2020. Chatterbot: Technologies, tools and applications. High Performance Vision Intelligence: Recent Advances. 913, 203-213. DOI: https://doi.org/10.1007/978-981-15-6844-2_1

[9] Papakitsos, E., 2013. The systemic modeling via military practice at the service of any operational planning. International Journal of Academic Research in Business and Social Science. 3(9), 176-190.

[10] Sanders, M., 1991. Communication technology today and tomorrow. Glencoe/McGraw-Hill: New York.

[11] Παπακίτσος Ε.Χ., 2008. Θέματα Σεμιναρίων Σχολικού Επαγγελματικού Προσανατολισμού (Greek) [Seminar Topics in School Vocational Guidance]. Αθήνα: Μ.-Χ.Χ. Χριστοδουλάτου.

[12] Saunders, M., Lewis, P., Thornhill, A., 2015. Research methods for business students (7th edition). Pearson Publication: Dallas.

[13] Wald, R., Piotrowski, J. T., Araujo, T., et al., 2023. Virtual assistants in the family home. Understanding parents' motivations to use virtual assistants with their Child(dren). Computers in Human Behavior. 139, 107526. DOI: https://doi.org/10.1016/j.chb.2022.107526

[14] Hilal, A.M., Alrowais, F., Al-Wesabi, F.N., et al., 2023. Red deer optimization with artificial intelligence enabled image captioning system for visually impaired people. Computer Systems Science and Engineering. 46(2), 1929-1945. DOI: https://doi.org/10.32604/csse.2023.035529

[15] Daoud, M., 2022. Topical and non-topical approaches to measure similarity between arabic questions. Big Data and Cognitive Computing. 6(3), 87. DOI: https://doi.org/10.3390/bdcc6030087

[16] Brabra, H., Báez, M., Benatallah, B., et al., 2021. Dialogue management in conversational systems: A review of approaches, challenges, and opportunities. IEEE Transactions on Cognitive and Developmental Systems. 14(3), 783-798. DOI: https://doi.org/10.1109/TCDS.2021.3086565

[17] Tulshan, A.S., Dhage, S.N., 2019. Survey on virtual assistant: Google Assistant, Siri, Cortana, Alexa. Advances in Signal Processing and Intelligent Recognition Systems, SIRS 2018. Communications in Computer and Information Science. 968, 190-201. DOI: https://doi.org/10.1007/978-981-13-5758-9_17

[18] Sermet, Y., Demir, I., 2021. A semantic web framework for automated smart assistants: A case study for public health. Big Data and Cognitive Computing. 5(4), 57. DOI: https://doi.org/10.3390/bdcc5040057

[19] Stoica, A., Kadar, T., Lemnaru, C., et al., 2021. Intent detection and slot filling with capsule net architectures for a romanian home assistant. Sensors (Switzerland). 21(4), 1-28. DOI: https://doi.org/10.3390/s21041230

[20] Mirbabaie, M., Stieglitz, S., Brünker, F., et al., 2021. Understanding collaboration with virtual assistants—The role of social identity and the extended self. Business and Information Systems Engineering. 63(1), 21-37. DOI: https://doi.org/10.1007/s12599-020-00672-x

[21] Pal, D., Babakerkhell, M.D., Zhang, X., 2021. Exploring the determinants of users' continuance usage intention of smart voice assistants. IEEE Access. 9, 162259-162275. DOI: https://doi.org/10.1109/ACCESS.2021.3132399

[22] Arora, S., Athavale, V.A., Maggu, H., et al., 2020. Artificial intelligence and virtual assistant—working model. Mobile radio communications and 5G networks: Proceedings of MRCN 2020. Springer Singapore: Singapore. pp. 163-171. DOI: https://doi.org/10.1007/978-981-15-7130-5_12

[23] Wellsandt, S., Hribernik, K., Thoben, K.D., 2021. Anatomy of a digital assistant. Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems. IFIP Advances in Information and Communication Technology. 633, 321-330. DOI: https://doi.org/10.1007/978-3-030-85910-7_34

[24] Rastogi, A., Zang, X., Sunkara, S., et al., 2020. Towards scalable multi-domain conversational agents: The schema-guided dialogue dataset. Proceedings of the AAAI Conference on Artificial Intelligence. 34(5), 8689-8696.

[25] Papakitsos, E.C., Giachos, I., 2016. The study of machine translation aspects through constructed languages. International Journal of Electronic Engineering and Computer Science. 1(1), 28-34.

[26] Γιάχος Ι., Παπακίτσος Ε.Χ., Μακρυγιάννης Π.Σ., 2017. Ένα Πείραμα Εκμάθησης Γλωσσικής Επικοινωνίας σε ένα Ρομποτικό Σύστημα (Greek) [An Experiment in Learning Language Communication in a Robotic System]. In the 9th Conference on Informatics in Education—Piraeus. pp. 46-56. http://events.di.ionio.gr/cie/images/documents17/cie2017_Proc_OnLine/new/custom/pdf/CIE2017_proceedings_all.pdf

[27] Giachos, I., Papakitsos, E.C., Chorozoglou, G., 2017. Exploring natural language understanding in robotic interfaces. International Journal of Advances in Intelligent Informatics. 3(1), 10-19. DOI: https://doi.org/10.26555/ijain.v3i1.81

[28] Bunt, H., 2008. Semantic underspecification: Which Technique for what purpose? Computing Meaning. 3, 55-85.

[29] Bos, J., 2002. Underspecification and resolution in discourse semantics [PhD thesis]. Saarland: Saarland University.

Downloads

How to Cite

Giachos, I., Papakitsos, E. C., Savvidis, P., & Laskaris, N. (2023). Inquiring Natural Language Processing Capabilities on Robotic Systems through Virtual Assistants: A Systemic Approach. Journal of Computer Science Research, 5(2), 28–36. https://doi.org/10.30564/jcsr.v5i2.5537

Issue

Vol. 5 , Iss. 2 (April 2023)

Article Type

Article

License

Copyright © 2023 Author(s)

Creative Commons License

This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.

Crossref
Scopus
Google Scholar
Europe PMC