046e6191-8373-44e0-93ec-e2a1f8f2c6c320240328045412320wseas:wseasmdt@crossref.orgMDT DepositWSEAS TRANSACTIONS ON BIOLOGY AND BIOMEDICINE2224-29021109-951810.37394/23208http://wseas.org/wseas/cms.action?id=40111820241820242110.37394/23208.2024.21https://wseas.com/journals/bab/2024.phpIoannisGiachosDepartment of Industrial Design & Production Engineering, University of West Attica, Egaleo, Athens 12241, GREECEEleniBatzakiDepartment of Industrial Design & Production Engineering, University of West Attica, Egaleo, Athens 12241, GREECEEvangelos C.PapakitsosDepartment of Industrial Design & Production Engineering, University of West Attica, Egaleo, Athens 12241, GREECEMichailPapoutsidakisDepartment of Industrial Design & Production Engineering, University of West Attica, Egaleo, Athens 12241, GREECENikolaosLaskarisDepartment of Industrial Design & Production Engineering, University of West Attica, Egaleo, Athens 12241, GREECEThis paper is an attempt to show how a Human-Robot Interface (HRI) system in the Greek language can help people with brain damage in speech and its related perception issues. This proposal is not the product of research conducted on how to treat brain injuries. It is a conclusion stemming from research on intelligent Human-Robot interfaces, as a part of Artificial Intelligence and Natural Language Processing, which approaches the processing and understanding of natural language with specific methods. For the same reason, experiments on real patients have not been conducted. Thus, this paper does not propose a competing method, but a method for further study. Since it is referring to a very general and quite complex issue, an approach is presented here for the Sequencing problem. A person with such a problem cannot hierarchically organize the tasks needed to be performed. This Hierarchy has to do with both time and practicality. The particular problem here, as much as the innovation of our approach, lies not when there are explicit temporally defined instructions, but in the ability to derive these temporal values through the person’s perception from more vague temporal references. The present approach is developed based on our related previous works for deploying a robotic system that relies on Hole Semantics and the OMAS-III computational model as a grammatical formalism for its communication with humans.3282024328202413814714https://wseas.com/journals/bab/2024/a285108-011(2024).pdf10.37394/23208.2024.21.14https://wseas.com/journals/bab/2024/a285108-011(2024).pdf10.26555/ijain.v3i1.81Giachos I., Papakitsos C.E. and Chorozoglou G., Exploring natural language understanding in robotic interfaces, International Journal of Advances in Intelligent Informatics, Vol. 3, No. 1, 2017, pp. 10-19. 10.6007/ijarbss/v3-i9/200Papakitsos E., The Systemic Modeling via Military Practice at the Service of any Operational Planning, International Journal of Academic Research in Business and Social Science, Vol. 3, No. 9, 2013, pp. 176-190. 10.1109/tse.1977.229900Ross D.T., Structured Analysis (SA): A Language for Communicating Ideas, IEEE Transactions on Software Engineering, Vol. SE-3, No. 1, 1977, pp. 16-34. 10.4018/978-1-878289-68-1Grover V. and Kettinger W.J., Process Think: Winning Perspectives for Business Change in the Information Age, IDEA Group Publishing Inc, 2000. 10.3115/1067807.1067834Koller A., Niehren J. and Thater, S., Bridging the gap between underspecification formalisms: hole semantics as dominance constraints, Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics (EACL '03), Vol. 1, Budapest, Hungary, 2003, pp. 195–202. Giachos I., Piromalis D., Papoutsidakis M., Kaminaris S. and Papakitsos E.C., A Contemporary Survey on Intelligent HumanRobot Interfaces Focused on Natural Language Processing, International Journal of Research in Computer Applications and Robotics, Vol. 8, No. 7, 2020, pp. 1-20. 10.30564/jcsr.v5i4.6067Giachos I., Batzaki E., Papakitsos C.E., Kaminaris S. and Laskaris N., A Natural Language Generation Algorithm for Greek by using Hole Semantics and a systemic Grammatical Formalism, Journal of Computer Science Research, Vol. 5, No. 4, 2023, pp. 27-37. https://doi.org/10.30564/jcsr.v5i4.6067. Bos J., Predicate logic unplugged, Proceedings of the 10th Amsterdam Colloquium, Amsterdam, The Netherlands, 1996. Bos J., Underspecification and resolution in discourse semantics, Ph.D. thesis, Saarland University, 2002. 10.1109/isemantic55962.2022.9920394Jumanto J., Rizal S.S., Asmarani R. and Sulistyorini H., The Discrepancies of Online Translation-Machine Performances: A MiniTest on Object Language and Metalanguage, International Seminar on Application for Technology of Information and Communication (iSemantic), 2022, pp. 27-35. https://doi.org/10.1109/iSemantic55962.2022. 10.1007/978-3-030-34485-6_8Georgi G., Demonstratives in First-Order Logic, The Architecture of Context and Context-Sensitivity, Studies in Linguistics and Philosophy, Springer, Cham, Vol. 103, 2020, pp. 125–148. 10.18778/0138-0680.2022.07Michalczenia P., First-Order Modal Semantics and Existence Predicate, Bulletin of the Section of Logic, Vol. 51, No. 3, 2022, pp. 317-327. Bos, J., Variable-free discourse representation structures, Semantics Archive, 2021. 10.1007/978-3-030-58323-1_28Pereira J., Franco N. and Fidalgo R., A Semantic Grammar for Augmentative and Alternative Communication Systems, 23rd International Conference on Text, Speech, and Dialogue (TSD 2020), Brno, Czech Republic, 2020, pp. 257–264. 10.1075/li.00038.bilBîlbîie G., A constraint-based approach to linguistic interfaces, Lingvisticæ Investigationes, Vol. 43, No. 1, 2020, pp. 1- 22. 10.1126/science.170.3961.940Geschwind N., The Organization of Language and the Brain: Language disorders after brain damage help in elucidating the neural basis of verbal behavior, Vol. 170, No. 3961, 1970, pp. 940-944. 10.1097/wnr.0b013e3283329be8Clerget E., Winderickx A., Fadiga L., and Olivier E., Role of Broca's area in encoding sequential human actions: a virtual lesion study, Neuroreport, Vol. 20, No. 16, 2009, pp. 1496-1499. 10.1103/physrevlett.117.138001Goriely A., Weickenmeier J. and Kuhl E., Stress Singularities in Swelling Soft Solids, Physical Review Letters, Vol. 117, 2016, 138001. 10.1016/j.actbio.2023.01.055Linka K., St. Pierre S. R. and Kuhl E., Automated model discovery for human brain using Constitutive Artificial Neural Networks, Acta Biomaterialia, Vol. 160, 2023, pp. 134- 151. 10.2172/1739905Daphalapurkar N. P., Biofidelic Digital Head Model Software, U.S. Department of Energy, Office of Scientific and Technical Information, Technical Report LA-UR-20- 30334 (TRN: US2214783), 2020. Moss W. and Heller A., Computer Modeling Provides New Insights into Traumatic Brain Injury, Science & Technology Review, Vol. 2018-09, 2018, pp. 21-23. https://doi.org/10.2172/1489455. 10.3389/fbioe.2021.587082Schroder A., Lawrence T., Voets N., GarciaGonzalez D., Jones M., Peña J.-M. and Jerusalem A., A Machine Learning Enhanced Mechanistic Simulation Framework for Functional Deficit Prediction in TBI, Frontiers in Bioengineering and Biotechnology, Vol. 9, 2021, pp. 1-19. https://doi.org/10.3389/fbioe.2021.587082. 10.1016/j.neuron.2020.12.010Armand E. J., Li J., Xie F., Luo C. and Mukamel E. A., Single-Cell Sequencing of Brain Cell Transcriptomes and Epigenomes, Neuron, Vol. 109, No. 1, 2021, pp. 11-26. 10.3390/ijms24032943Wang S., Sun S.-T., Zhang X.-Y., Ding H.-R., Yuan. Y., He J.-J., Wang M.-S., Yang B. and Li Y.-B., The Evolution of Single-Cell RNA Sequencing Technology and Application: Progress and Perspectives, International Journal of Molecular Sciences, Vol. 24, No. 3, 2023, 2943. 10.3390/genes14030756Naydenov D.D., Vashukova E.S., Barbitoff Y.A., Nasykhova Y.A., Glotov A.S., Current Status and Prospects of the Single-Cell Sequencing Technologies for Revealing the Pathogenesis of Pregnancy-Associated Disorders, Genes, Vol. 14, No. 3, 2023, 756. 10.1007/s10846-015-0190-6Alatartsev S., Stellmacher S. and Ortmeier F., Robotic Task Sequencing Problem: A Survey, Journal of Intelligent & Robotic Systems, Vol. 80, 2015, pp. 279–298. 10.1109/lra.2022.3142919Touzani H., Séguy N., Hadj-Abdelkader H., Suárez R., Rosell J., Palomo-Avellaneda L. and Bouchafa S., Efficient Industrial Solution for Robotic Task Sequencing Problem With Mutual Collision Avoidance & Cycle Time Optimization, IEEE Robotics and Automation Letters, Vol. 7, No. 2, 2022, pp. 2597-2604. 10.1109/case49997.2022.9926498Donghui Li, Qingbin Wang, Wei Zou, Hu Su, Xingang Wang, Xinyi Xu, An Efficient Approach for Solving Robotic Task Sequencing Problems Considering Spatial Constraint, 2022 IEEE 18th International Conference on Automation Science and Engineering (CASE), Mexico City, Mexico, 2022, pp. 60-66. 10.1177/1362168818813668Malicka A., The role of task sequencing in fluency, accuracy, and complexity: Investigating the SSARC model of pedagogic task sequencing, Language Teaching Research, Vol. 24, No. 5, 2020, pp. 642-665. 10.1016/j.neuroimage.2019.116387Janacsek K., Shattuck K. F., Tagarelli K. M., Lum J.A.G., Turkeltaub P.E. and Ullman M. T., Sequence learning in the human brain: A functional neuroanatomical meta-analysis of serial reaction time studies, NeuroImage, Vol. 207, 2020, 116387. 10.1109/tii.2021.3112504Yidan Hu, Ruonan Liu, Xianling Li, Dongyue Chen, Qinghua Hu, Task-Sequencing Meta Learning for Intelligent Few-Shot Fault Diagnosis With Limited Data, IEEE Transactions on Industrial Informatics, Vol. 18, No. 6, 2022, pp. 3894-3904.