Evaluation of Vocal Communication in a Robot Collective

Year 2020, Volume: 8 Issue: 3, 2029 - 2040, 31.07.2020

Mehmet Dinçer Erbaş , İsmail Hakkı Parlak

https://doi.org/10.29130/dubited.688255

Abstract

In this research, we attempt to design a model in which multiple robots communicate with an artificial proto-language whose symbols are vocally encoded letters of the Morse alphabet. We have shown that, as the robots have limited sensing and acting abilities, the communicated symbols of the proto-language differentiates from their original versions due to copying errors. We check the effects of two distinct environmental factors, namely the positional distance between the robots and the amount of noise in the environment. It is shown that both of these factors affect, in different ways, how accurately the presented proto-language can be accurately transmitted by the robots.

Keywords

Vocal communication, Robot learning, Multi-robot systems

Bir Robot Kolektifinde Ses ile Haberleşmenin Değerlendirilmesi

Abstract

Bu araştırmada, birden fazla robotun, sembolleri Mors alfabesinin sesli olarak kodlanmış harfleri olan yapay bir öncül-dil vasıtasıyla iletişim kurduğu bir model tasarlanmıştır. Robotların sınırlı algılama ve eyleyici yeteneklerine sahip olduklarından, kullanılan öncül-dilin iletilen sembollerinin kopyalama hataları nedeniyle orijinal sürümlerinden farklılaştıkları gösterilmiştir. İki ayrı çevresel faktörün, robotlar arasındaki konumsal mesafe ve ortamdaki gürültü miktarının, kopyalama üzerine etkileri incelenmiştir. Bu faktörlerin her ikisinin sunulan öncül-dilin robotlar tarafından ne kadar doğru bir şekilde iletilebileceğini etkilediği gösterilmiştir.

Keywords

Ses ile haberleşme, Robot öğrenmesi, Çok-robotlu sistemler

References

• [1] C. Hockett, “The origin of speech,” Scientific American, vol. 203, pp. 88-111, 1960.
• [2] L. Steels, “The synthetic modeling of language origins,” Evolution of Communication, vol. 1, no. 1 pp. 1-34, 1997.
• [3] L. Steels, “Human language is a culturally evolving system,” Psychonomic Bulletin & Review, vol. 24, no. 1, pp. 190-193, 2017.
• [4] B. de Boer, “Self-organization in vowel systems,” Journal of Phonetics, vol. 28, no. 4, pp. 441-465, 2000.
• [5] W. Zuidema, and B. de Boer, “The evolution of combinatorial phonology,” Journal of Phonetics, vol. 37, no. 2, pp. 125-144, 2009.
• [6] B. Galantucci, “An experimental study of the emergence of human communication systems,” Cognitive science, vol. 29, no. 5, pp. 737-767, 2005.
• [7] H. Cornish, R. Dale, S. Kirby, and M. H. Christiansen, “Sequence memory constraints give rise to language-like structure through iterated learning,” PloS one, vol. 12, no.1, pp. 1-18, 2017.
• [8] B. Chazelle, and C. Wang, “Iterated learning in dynamic social networks,” The Journal of Machine Learning Research, vol. 20, no. 1, pp. 979-1006, 2019.
• [9] V. DeCastro-Arrazola, and S. Kirby, “The emergence of verse templates through iterated learning,” Journal of Language Evolution, vol. 4, no. 1, pp. 28-43, 2019.
• [10] S. Kirby, T. Griffiths, and K. Smith, “Iterated learning and the evolution of language,” Current Opinion in Neurobiology, vol. 28, pp. 108-114, 2014.
• [11] T. Verhoef, “The origins of duality of patterning in artificial whistled languages,” Language and Cognition, vol. 4, no. 4, pp. 357-380, 2012.
• [12] L. Steels, “Modeling the cultural evolution of language,” Physics of Life Reviews, vol. 8, no. 4, pp. 339-356, 2011.
• [13] C. M. Cianci, X. Raemy, J. Pugh, and A. Martinoli, “Communication in a swarm of miniature robots: The e-puck as an educational tool for swarm robotics,” in International Workshop on Swarm Robotics, 1st ed., Berlin, Germany: Springer, 2006, pp. 103-115.
• [14] J. E. Peelle, “Speech comprehension: Stimulating discussions at a cocktail party,” Current Biology, vol. 28, no.2, pp. 68-70, 2018.
• [15] S. Getzmann, J. Jasny, and M. Falkenstein, “Switching of auditory attention in cocktail-party listening: ERP evidence of cueing effects in younger and older adults,” Brain and Cognition, vol. 111, pp. 1-12, 2017.