Designing and building a dialogue mechanism suitable for RoboPuppet with using deep inference learning

Document Type : Research Article

Authors

Department of Mechanical Engineering, University of Isfahan, Isfahan, Iran

Abstract

This research endeavors to construct a mechanism, blending text mining and natural language processing, to apply a deep learning dialogue and deep reasoning approach to "Puppet robot." Historically, tent dolls have been an ancient method of interacting with audiences, being directly managed by an operator. With breakthroughs in artificial intelligence and deep learning, it is now possible to reduce the dependence of tent dolls on operators, thereby enabling them to communicate intelligently with audiences. The robot, by identifying the audience's Persian speech, ascertains a fitting answer to their inquiries and broadcasts it in audible Persian. The dialogue mechanism, deeply ingrained in a deep learning algorithm, identifies the user's question and proffers a range of possible answers from the robot's dataset categories. Utilizing the highest probability, the category containing the user's question is identified, and responses to those questions are selected at random. Additionally, the Robo Tent Dialogue mechanism comprises several uncomplicated conditional sections that can furnish suitable responses to repetitive or inappropriate questions. Through diverse training and by altering parameters in the robot's deep learning model, using a 64-class dataset, results reveal that the application of technologically advanced, high-neuron layers outperforms multi-layers without detrimentally impacting the model's final accuracy.

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References

[1] A. Chowanda, A.D. Chowanda, Recurrent neural network to deep learn conversation in indonesian, Procedia computer science, 116 (2017) 579-586.
[2] K. Karpagam, K. Madusudanan, A. Saradha, Deep learning approaches for answer selection in question answering system for conversation agents, ICTACT Journal on Soft Computing, 10(2) (2020) 2040-2044.
[3] R. Yan, Y. Song, H. Wu, Learning to respond with deep neural networks for retrieval-based human-computer conversation system, in:  Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, 2016, pp. 55-64.
[4] A. Engineered, Ameca The future face of robotics, in, Engineered Arts, (2022).
[5] Y. Sharma, S. Gupta, Deep learning approaches for question answering system, Procedia computer science, 132 (2018) 785-794.
[6] M. Tan, C. Dos Santos, B. Xiang, B. Zhou, Improved representation learning for question answer matching, in:  Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2016, pp. 464-473.
[7] C. Xiong, S. Merity, R. Socher, Dynamic memory networks for visual and textual question answering, in:  International conference on machine learning, PMLR, 2016, pp. 2397-2406.
[8] T. Minato, K. Sakai, T. Uchida, H. Ishiguro, A study of interactive robot architecture through the practical implementation of conversational android, Frontiers in Robotics and AI, 9, (2022)
[9] A. Conneau, A. Baevski, R. Collobert, A. Mohamed, M. Auli, Unsupervised cross-lingual representation learning for speech recognition, arXiv preprint arXiv:2006.13979, (2020)
[10] S.-H. Lee, Y.-E. Lee, S.-W. Lee, Toward imagined speech based smart communication system: potential applications on metaverse conditions, in:  2022 10th International Winter Conference on Brain-Computer Interface (BCI), IEEE, 2022, pp. 1-4.
[11] P. Chang, S. Liu, K.D. Campbell, Robot Sound Interpretation: Learning Visual-Audio Representations for Voice-Controlled Robots, CoRR, (2021)
[12] S. Hamed, puppeteer robot: Designing a construction of a new interactive game based on imitative learning of a humanoid robot from a human, in:  The first national conference of computer games; Opportunities and challenges, 1394 (in Persian).
[13] fastText, fastText: Library for efficient text classification and representation learning, in, Facebook.
[14] H. Hemati, fastText Word Embedding: The Persian Approach to Text Embedding, (2018)
[15] H. Hemati, fastText-Persian, in, Github, 2019.
[16] S. Ayobi, PersianQA, in, Github, 2021.
[17] A. Akhavan, Text Classification Emojify, in, Github, 1399.
[18] J.C. Vasquez-Correa, J.C. Guerrero-Sierra, J.L. Pemberty-Tamayo, J.E. Jaramillo, A.F. Tejada-Castro, One system to rule them all: A universal intent recognition system for customer service chatbots, arXiv preprint arXiv:2112.08261, (2021)
[19] J.J. Bird, A. Ekárt, D.R. Faria, Chatbot Interaction with Artificial Intelligence: human data augmentation with T5 and language transformer ensemble for text classification, Journal of Ambient Intelligence and Humanized Computing, 14(4) (2023) 3129-3144.
[20] E.H. Almansor, F.K. Hussain, O.K. Hussain, Supervised ensemble sentiment-based framework to measure chatbot quality of services, Computing, 103 (2021) 491-507.
Amirkabir Journal of Mechanical Engineering
Volume 55, Issue 8
November 2023
Pages 919-942

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