Skip to main content

Human–Computer Interactions Through Multi-agent Systems: Design and Implementations

  • Chapter
  • First Online:
Multi Agent Systems

Abstract

In computing, multi-agent systems are a relatively new subfield made up of several interacting computer elements known as agents. This interface is part of a global system representing software architecture for robot control to help the human operator. The implementation of human–computer interaction (HCI) using a multi-agent system is evolving day by day. We examined different past research works and attempted to cover various works step by step and furthermore as per different various advancements and applications. Various technologies, which are covered here, are some recent applications in this domain like tele-health, air traffic control, mixed reality, learning from demonstration and psychological aspects of agents with HCI. Later, we introduced the musical scale recommendation and explained its importance in the musical world. Music is evergreen and popular worldwide. And in music, musical scale determination is one of the most complex things. We implement it in respect of this proposed topic. Here, we discussed how HCI using multi-agent systems can help the singers get the scale recommendation.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Woolbridge M, An introduction to multi agent systems. Wiley (Book)

    Google Scholar 

  2. Russel S (2014) Norvig P artificial intelligence—a modern approach, 3rd edn. Pearson Education Limited, England, p 1079

    Google Scholar 

  3. Macal CM (2016) Everything you need to know about agent-based modelling and simulation. J Simul 10(2):144–156

    Google Scholar 

  4. Rocha J, Boavida-Portugal I, Gomes E (2017) Introductory chapter: multi-agent systems. https://doi.org/10.5772/intechopen.70241

  5. Stone P, Veloso M (2000). Multiagent systems: a survey from a machine learning perspective. Auton Robot 8(3):345–383

    Google Scholar 

  6. Davidsson P (2002) Agent-based social simulation: a computer science view. J Artif Soc Soc Simul 5(1):1–7

    Google Scholar 

  7. Thomas MC, Gallesio S, Sanchez B, Tigli J (1994) Human computer interaction based on a multi-agent system. In: Proceedings of IEEE international conference on systems, man and cybernetics, vol 2, pp 1440–1445, https://doi.org/10.1109/ICSMC.1994.400048

  8. Hayes-Roth B, Brownston L, Gent Rv (1995) Multi-agent collaboration in direct improvisation. In: Proceedings of the first international conference on multiagent systems

    Google Scholar 

  9. Nakauchi Y, Okada T, Yamasaki N, Anzai Y (1992) Multi-agent interface architecture for human-robot cooperation. In: Proceedings 1992 IEEE international conference on robotics and automation, vol 3, pp 2786–2788, https://doi.org/10.1109/ROBOT.1992.220012

  10. Suzuki T, Yokota T, Asama T, Kaetsu H, Endo I (1995) Cooperation between the human operator and the multi-agent robotic system: evaluation of agent monitoring methods for the human interface system. In: Proceedings 1995 IEEE/RSJ international conference on intelligent robots and systems. Human robot interaction and cooperative robots, vol 1, pp 206–211. https://doi.org/10.1109/IROS.1995.525798

  11. Laengle T, Woern H (2001) Human-robot-cooperation using multi-agent-systems. https://doi.org/10.1023/A:1013901228979

  12. Raibert MH, Craig JJ (1981) Hybrid position/force control of manipulators. ASME J Dynam Syst Meas Control 102:126–133

    Google Scholar 

  13. Canino-Rodríguez JM, García-Herrero J, Besada-Portas J, Ravelo-García AG, Travieso-González C, Alonso-Hernández JB (2015) Human computer interactions in next-generation of aircraft smart navigation management systems: task analysis and architecture under an agent-oriented methodological approach. Sensors 15:5228–5250. https://doi.org/10.3390/s150305228

    Article  Google Scholar 

  14. Zheng S, Zhang Q, Zheng R, Huang BQ, Song YL, Chen XC (2017) (2017) Combining a multi-agent system and communication middleware for smart home control: a universal control platform architecture. Sensors 17:2135. https://doi.org/10.3390/s17092135

    Article  Google Scholar 

  15. Alqahtani H, Liu C, Kavakli-Thorne M, Kang Y (2019) An Agent-based intelligent hcl information system in mixed reality. In: 28th international conference on information systems development, Toulon, France

    Google Scholar 

  16. Li J (2015) The benefit of being physically present: a survey of experimental works comparing copresent robots, telepresent robots and virtual agents. Int J Hum Comput Stud 77:23–37. https://doi.org/10.1016/j.ijhcs.2015.01.001

    Article  Google Scholar 

  17. Liu CZ, Kavakli M (2016) Fuzzy knowledge-based enhanced matting. In: 2016 IEEE 11th Conference on industrial electronics and applications (ICIEA). IEEE, pp 934–939. https://doi.org/10.1109/ICIEA.2016.7603716

  18. Liu CZ, Kavakli M (2016) Data-aware QoE-QoS management. In: 2016 IEEE 11th conference on industrial electronics and applications (ICIEA). IEEE, pp 1818–1823. https://doi.org/10.1109/ICIEA.2016.7603882

  19. Liu CZ, Kavakli M (2016) Knowledge-based pattern-context-aware stereo analysis and its applications. In: 2016 international conference on digital image computing: techniques and applications (DICTA). IEEE, pp 1–8

    Google Scholar 

  20. Liu CZ, Kavakli M (2016) Mixed reality with a collaborative information system. In: Advances in services computing: 10th Asia-Pacific services computing conference, APSCC 2016, Zhangjiajie, China, November 16–18, Proceedings 10. Springer, pp 205–219

    Google Scholar 

  21. Liu CZ, Kavakli M (2018) An agent-based collaborative information processing system for mixed reality applications—part b: Agent-based collaborative information processing and coordination. In: 2018 IEEE conference on industrial electronics and applications (ICIEA), page submitted

    Google Scholar 

  22. Liu CZ, Kavakli M (2017) A data-aware confidential tunnel for wireless sensor media networks. Multimedia tools and applications, pp 1–23

    Google Scholar 

  23. Çelikok M, Peltola T, Daee P, Kaksi S (2019) Interactive AI with a theory of mind. https://arxiv.org/abs/1912.05284

  24. Kopp S, Krämer N (2021) Revisiting human-agent communication: the importance of joint co-construction and understanding mental states. Front Psychol 23(12):580955. https://doi.org/10.3389/fpsyg.2021.580955.PMID:33833705;PMCID:PMC8021865

    Article  Google Scholar 

  25. Allwood J, Nivre J, Ahlsén E (1992) On the semantics and pragmatics of linguistic feedback. J Semant 9:1–26. https://doi.org/10.1093/jos/9.1.1

    Article  Google Scholar 

  26. Buschmeier H, Kopp S (2018) Communicative listener feedback in human-agent interaction: artificial speakers need to be attentive and adaptive. In: Proceedings of the 17th international conference on autonomous agents and multiagent systems; July 10–15; Stockholm, Sweden

    Google Scholar 

  27. Huang L, Morency LP, Gratch J (2011) “Virtual Rapport 2.0” in intelligent virtual agents. IVA 2011. In: Vilhjálmsson HH, Kopp S, Marsella KR (eds) Lecture notes in computer science, vol 6895. Springer, Berlin. https://doi.org/10.1007/978-3-642-23974-8_8

  28. Kopp S (2010) Social resonance and embodied coordination in face-to-face conversation with artificial interlocutors. Speech Comm 52:587–597. https://doi.org/10.1016/j.specom.2010.02.007

    Article  Google Scholar 

  29. Lanza F, Seidita V, Chella A (2020) Agents and robots for collaborating and supporting physicians in healthcare scenarios. J Biomed Inform 108:103483. https://doi.org/10.1016/j.jbi.2020.103483

    Article  Google Scholar 

  30. Bordini RH, Hübner JF (2005) BDI agent programming in AgentSpeak using Jason. In: International workshop on computational logic in multi-agent systems, Springer, pp 143–164. https://doi.org/10.1007/11750734_9

  31. Bordini RH, Hübner JF, Wooldridge M (2007) Programming multi-agent systems in AgentSpeak using Jason (Wiley Series in Agent Technology). Wiley, USA

    Book  Google Scholar 

  32. Chella A, Lanza F, Seidita V (2018) Human-agent interaction, the system level using JASON. In: Proceedings of the 6th international workshop on engineering multi-agent systems, Stockholm

    Google Scholar 

  33. Chella A, Lanza F, Seidita V (2019) Decision process in human-agent interaction: extending JASON reasoning cycle. Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics) 11375 LNAI, pp 320–339. https://doi.org/10.1007/978-3-030-25693-7_17

  34. Papadopoulos GT, Antona M, Stephanidis C (2021) Towards open and expandable cognitive AI architectures for large-scale multi-agent human-robot collaborative learning. In: IEEE Access, vol 9, pp 73890–73909. https://doi.org/10.1109/ACCESS.2021.3080517

  35. Billard AG, Calinon S, Dillmann R (2016) Learning from humans. In: Siciliano B, Khatib O (eds) Springer handbook of robotics, Springer handbooks. Springer, Cham, pp 1995–2014. https://doi.org/10.1007/978-3-319-32552-1_74

  36. Caccavale R, Saveriano M, Finzi A, Lee D (2019) Kinesthetic teaching and attentional supervision of structured tasks in human-robot interaction. Auton Robot 43(6):1291–1307. https://doi.org/10.1007/s10514-018-9706-9

    Article  Google Scholar 

  37. Zhu Z, Hu H (2018) Robot learning from demonstration in robotic assembly: a survey. Robotics 7(2):17

    Article  Google Scholar 

  38. Capurso M, Ardakani MMG, Johansson R, Robertsson A, Rocco P (2017) Sensorless kinesthetic teaching of robotic manipulators assisted by observer-based force control. In: Proceedings of IEEE International Conference on Robotics and Automation (ICRA), pp 945–950. https://doi.org/10.1109/ICRA.2017.7989115

  39. Liu Y, Gupta A, Abbeel P, Levine S (2018) Imitation from observation: learning to imitate behaviors from raw video via context translation. In: Proceedings of IEEE international conference on robotics and automation (ICRA), pp 1118–1125

    Google Scholar 

  40. Xu Y, Yang C, Zhong J, Wang N, Zhao L (2018) Robot teaching by teleoperation based on visual interaction and extreme learning machine. Neurocomputing 275:2093–2103

    Google Scholar 

  41. Zhang T, McCarthy Z, Jow O, Lee D, Chen X, Goldberg K, Abbeel P (2018) Deep imitation learning for complex manipulation tasks from virtual reality teleoperation. In: Proceedings of IEEE international conference on robotics and automation (ICRA), May 2018, pp 1–8

    Google Scholar 

  42. Finn C, Yu T, Zhang T, Abbeel P, Levine S (2017) One-shot visual imitation learning via meta-learning. arXiv:1709.04905. [Online]. Available: http://arxiv.org/abs/1709.04905

  43. Hussein A, Gaber MM, Elyan E, Jayne C (2017) Imitation learning: a survey of learning methods. ACM Comput Surv 50(2):1–35. https://doi.org/10.1145/3054912

    Article  Google Scholar 

  44. Silver D, Huang A, Maddison CJ, Guez A, Sifre L, Driessche G, Schrittwieser J, Antonoglou I, Panneershelvam V, Lanctot M, Dieleman S, Grewe D, Nham J, Kalchbrenner N, Sutskever I, Lillicrap T, Leach M, Kavukcuoglu K, Graepel T, Hassabis D (2016) Mastering the game of go with deep neural networksand tree search. Nature 529(7587):484–489

    Google Scholar 

  45. Zhang M, McCarthy Z, Finn C, Levine S, Abbeel P (2016) Learning deep neural network policies with continuous memory states. In: Proceedings of IEEE international conference on robotics and automation (ICRA), May 2016, pp 520–527

    Google Scholar 

  46. Hodges DA (1996) Human musicality. In: Hodges DA (ed) Handbook of music psychology, 2nd edn. IMR Press, San Antonio, pp 29–68

    Google Scholar 

  47. Aiello R, Sloboda JA (1994) Musical perceptions. In: Aiello R, Sloboda JA (eds) Oxford University Press

    Google Scholar 

  48. Jones MR, Holleran S (1992) Cognitive bases of musical communication. American Psychological Association, Washinton, DC

    Book  Google Scholar 

  49. Bharucha JJ (1984) Anchoring effects in music: the resolution of dissonance. Cogn Psychol 16:485–518. https://doi.org/10.1016/0010-0285(84)90018-5

    Article  Google Scholar 

  50. Bharucha JJ (1994) Tonality and expectation. In: Aiello R, Sloboda JA (eds) Musical perceptions. Oxford University Press, New York, pp 213–239

    Google Scholar 

  51. Dowling WJ (1978) Scale and contour: two components of a theory of memory for melodies. Psychol Rev 85:341–354. https://doi.org/10.1037/0033-295X.85.4.341

  52. Slodaba JA (1985) The musical mind: a cognitive psychology of music. Oxford University Press, New York

    Google Scholar 

  53. Fischer G (2001) User modeling in human–computer interaction. User Model User-Adap Inter 11(1–2):65–86

    Article  Google Scholar 

  54. Baker CL, Jara-Ettinger J, Saxe R, Tenenbaum JB (2017) Rational quantitative attribution of beliefs, desires, and percepts in human mentalizing. Nat Hum Behav 1:1–10. https://doi.org/10.1038/s41562-017-0064

    Article  Google Scholar 

  55. Rabinowitz NC, Perbet F, Song HF, Zhang C, Eslami SM A, Botvinick M (2018) Machine theory of mind. In: Proceedings of the 35th international conference on machine learning, ICML, pp 4218–4227

    Google Scholar 

  56. Hernandez-Leal P, Kaisers M, Baarslag T, de Cote EM (2017) A survey of learning in multiagent environments: dealing with non-stationarity. arXiv:1707.09183

  57. Albrecht SV, Stone P (2018) Autonomous agents modelling other agents: a comprehensive survey and open problems. Artif Intell 258:66–95. https://doi.org/10.1016/j.artint.2018.01.002

    Article  MathSciNet  MATH  Google Scholar 

  58. Gmytrasiewicz PJ, Doshi P (2005) A framework for sequential planning in multi-agent settings. J Artif Intell Res 24:49–79. https://doi.org/10.1613/jair.1579

    Article  MATH  Google Scholar 

  59. Oliehoek FA, Amato C (2016) A concise introduction to decentralized POMDPs. Springer

    Book  Google Scholar 

  60. Pynadath DV, Tambe M (2002) The communicative multiagent team decision problem: analyzing teamwork theories and models. J Artif Intell Res 16(2002):389–423

    Article  MathSciNet  Google Scholar 

  61. Camerer CF, Ho T-H, Chong J-K (2004) A Cognitive hierarchy model of games. Q J Econ 119(3):861–898. https://doi.org/10.1162/0033553041502225

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Mondal, S., Bhattacharya, I., Gupta, S. (2022). Human–Computer Interactions Through Multi-agent Systems: Design and Implementations. In: Gupta, S., Banerjee, I., Bhattacharyya, S. (eds) Multi Agent Systems. Springer Tracts in Human-Centered Computing. Springer, Singapore. https://doi.org/10.1007/978-981-19-0493-6_2

Download citation

Publish with us

Policies and ethics