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.
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References
Woolbridge M, An introduction to multi agent systems. Wiley (Book)
Russel S (2014) Norvig P artificial intelligence—a modern approach, 3rd edn. Pearson Education Limited, England, p 1079
Macal CM (2016) Everything you need to know about agent-based modelling and simulation. J Simul 10(2):144–156
Rocha J, Boavida-Portugal I, Gomes E (2017) Introductory chapter: multi-agent systems. https://doi.org/10.5772/intechopen.70241
Stone P, Veloso M (2000). Multiagent systems: a survey from a machine learning perspective. Auton Robot 8(3):345–383
Davidsson P (2002) Agent-based social simulation: a computer science view. J Artif Soc Soc Simul 5(1):1–7
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
Hayes-Roth B, Brownston L, Gent Rv (1995) Multi-agent collaboration in direct improvisation. In: Proceedings of the first international conference on multiagent systems
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
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
Laengle T, Woern H (2001) Human-robot-cooperation using multi-agent-systems. https://doi.org/10.1023/A:1013901228979
Raibert MH, Craig JJ (1981) Hybrid position/force control of manipulators. ASME J Dynam Syst Meas Control 102:126–133
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
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
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
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
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
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
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
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
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
Liu CZ, Kavakli M (2017) A data-aware confidential tunnel for wireless sensor media networks. Multimedia tools and applications, pp 1–23
Çelikok M, Peltola T, Daee P, Kaksi S (2019) Interactive AI with a theory of mind. https://arxiv.org/abs/1912.05284
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
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
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
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
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
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
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
Bordini RH, Hübner JF, Wooldridge M (2007) Programming multi-agent systems in AgentSpeak using Jason (Wiley Series in Agent Technology). Wiley, USA
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
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
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
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
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
Zhu Z, Hu H (2018) Robot learning from demonstration in robotic assembly: a survey. Robotics 7(2):17
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
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
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
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
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
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
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
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
Hodges DA (1996) Human musicality. In: Hodges DA (ed) Handbook of music psychology, 2nd edn. IMR Press, San Antonio, pp 29–68
Aiello R, Sloboda JA (1994) Musical perceptions. In: Aiello R, Sloboda JA (eds) Oxford University Press
Jones MR, Holleran S (1992) Cognitive bases of musical communication. American Psychological Association, Washinton, DC
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
Bharucha JJ (1994) Tonality and expectation. In: Aiello R, Sloboda JA (eds) Musical perceptions. Oxford University Press, New York, pp 213–239
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
Slodaba JA (1985) The musical mind: a cognitive psychology of music. Oxford University Press, New York
Fischer G (2001) User modeling in human–computer interaction. User Model User-Adap Inter 11(1–2):65–86
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
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
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
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
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
Oliehoek FA, Amato C (2016) A concise introduction to decentralized POMDPs. Springer
Pynadath DV, Tambe M (2002) The communicative multiagent team decision problem: analyzing teamwork theories and models. J Artif Intell Res 16(2002):389–423
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
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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
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