Skip to main content

Advertisement

SpringerLink
Log in

Comparative Analytical Survey on Cognitive Agents with Emotional Intelligence

  • Published:
Cognitive Computation Aims and scope Submit manuscript

Abstract

During the past decade, social interaction in computer systems has attracted wide attention from the human-computer interaction and robotics communities. One of the essential objectives of artificial intelligence and human-machine interaction is to develop cognitive agents that can interact with others naturally in social situations. Emotional intelligence has a fundamental role in social communication. Emotional intelligence is the capacity of human beings to precisely assess their emotional states as well as emotions pertinent to others and subsequently utilize the acquired information to manage and conduct thoughts and actions and regulate their emotions for adaptation to environments. This critical role of emotional intelligence in social contexts fuels interest in developing computational models of emotions. Therefore, computational models of emotions enhance the behaviors associated with cognitive agents. The main objective of this paper is to present a more holistic view of cognitive agents with emotional intelligence for providing a confident route to the researchers in this literature. This paper offers a comprehensive study of all aspects of emotionally cognitive agents, including definitions, features, applications, and challenges. Subsequently, state-of-the-art computational models of emotions in affective computing literature utilized in cognitive agents are investigated. Also, the general framework is proposed for emotion modeling in cognitive agents. The proposed general framework leverages four main components: emotion generation, emotion experience, emotion regulation, and emotional modulation. These components occur intertwined within an emotional episode. Given our proposed framework, we investigate the components implemented in each computational model of emotions. Due to challenges in assessing agents with emotional intelligence, we present a classification of the evaluation manners leveraged in various studies. Then, we categorize and discuss challenges that necessitate being addressed for further studies. The main contribution of the paper is to provide insight and extensive resources for researchers on the cognitive agents with emotional intelligence literature.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Ghandeharioun A, McDuff D, Czerwinski M, Rowan K. Emma: an emotion-aware wellbeing chatbot. In 2019 8th International Conference on Affective Computing and Intelligent Interaction (ACII). 2019, pp. 1–7. IEEE.

  2. Barrett LF, Gross JJ. Emotional intelligence: a process model of emotion representation and regulation. 2001

  3. Jeon, M. Emotions and affect in human factors and human–computer interaction: taxonomy, theories, approaches, and methods. In Emotions and affect in human factors and human-computer interaction. Elsevier. 2017, pp. 3–26.

  4. Bechara A, Damasio H, Damasio AR. Emotion, decision making and the orbitofrontal cortex. Cerebral cortex. 2000;10(3):295–307.

    Article  Google Scholar 

  5. Lin J, Spraragen M, Blythe J, Zyda M. EmoCog: computational integration of emotion and cognitive architecture. In FLAIRS Conference. 2011, Citeseer.

  6. Larue O, Hough A, Juvina I. A core-affect model of decision making in simple and complex tasks. In CogSci. 2017.

  7. Perez J, Sanchez Y, Seron FJ, Cerezo E. Interacting with a semantic affective ECA. In International Conference on Intelligent Virtual Agents. 2017, Springer, pp. 374–384.

  8. Picard RW. Affective computing: challenges. International Journal of Human-Computer Studies. 2003;59(1–2):55–64.

    Article  Google Scholar 

  9. Albu F, Hagiescu D, Vladutu L, Puica M-A. Neural network approaches for children’s emotion recognition in intelligent learning applications. In EDULEARN15 7th Annu Int Conf Educ New Learn Technol Barcelona, Spain, 6th-8th. 2015.

  10. Ammar MB, Neji M, Alimi AM, Gouardères G. The affective tutoring system. Expert Systems with Applications. 2010;37(4):3013–23.

    Article  Google Scholar 

  11. Hudlicka, E. The case for cognitive-affective architectures as affective user models in behavioral health technologies. In International Conference on Human-Computer Interaction. 2020, Springer, pp. 191–206.

  12. de Melo, C. M., Carnevale, P., and Gratch, J. The effect of expression of anger and happiness in computer agents on negotiations with humans. In The 10th International Conference on Autonomous Agents and Multiagent Systems. 2011;3:937–944.

  13. Ye P, Wang T, Wang F-Y. A survey of cognitive architectures in the past 20 years. IEEE transactions on cybernetics. 2018;48(12):3280–90.

    Article  Google Scholar 

  14. Kotseruba I, Tsotsos JK. 40 years of cognitive architectures: core cognitive abilities and practical applications. Artif Intell Rev. 2020;53(1):17–94.

    Article  Google Scholar 

  15. Sanchez Y, Coma T, Aguelo A, Cerezo E. ABC-EBDI: an affective framework for BDI agents. Cognitive Systems Research. 2019;58:195–216.

    Article  Google Scholar 

  16. Marsella SC, Gratch J. EMA: a process model of appraisal dynamics. Cognitive Systems Research. 2009;10(1):70–90.

    Article  Google Scholar 

  17. Samsonovich AV. Socially emotional brain-inspired cognitive architecture framework for artificial intelligence. Cogn Syst Res. 2020;60:57–76.

    Article  Google Scholar 

  18. Hortensius R, Hekele F, Cross ES. The perception of emotion in artificial agents. IEEE Transactions on Cognitive and Developmental Systems. 2018;10(4):852–64.

    Article  Google Scholar 

  19. Hoorn JF, Baier T, Van Maanen JAN, Wester J. Silicon Coppelia and the formalization of the affective process. IEEE Trans Affect Comput. 2021.

  20. Rodríguez L-F, Gutierrez-Garcia JO, Ramos F. Modeling the interaction of emotion and cognition in autonomous agents. Biologically Inspired Cognitive Architectures. 2016;17:57–70.

    Article  Google Scholar 

  21. Adam C, Johal W, Pellier D, Fiorino H, Pesty S. Social human-robot interaction: a new cognitive and affective interaction-oriented architecture. In International conference on social robotics. 2016, Springer, pp. 253–263.

  22. Sander D, Grandjean D, Scherer KR. An appraisal-driven componential approach to the emotional brain. Emotion Review. 2018;10(3):219–31.

    Article  Google Scholar 

  23. Plutchik R. The nature of emotions: human emotions have deep evolutionary roots, a fact that may explain their complexity and provide tools for clinical practice. American scientist. 2001;89(4):344–50.

    Article  Google Scholar 

  24. Parrott WG. Emotions in social psychology: essential readings. psychology press. 2001.

  25. Ekman P. Basic emotions. Handbook of cognition and emotion. 1999;98(45–60):16.

    Google Scholar 

  26. Cambria E, Livingstone A, Hussain A. The hourglass of emotions. In Cognitive behavioural systems. Springer, 2012, pp. 144–157.

  27. Susanto Y, Livingstone AG, Ng BC, Cambria E. The hourglass model revisited. IEEE Intelligent Systems. 2020;35(5):96–102.

    Article  Google Scholar 

  28. Scherer KR. Emotion and emotional competence: conceptual and theoretical issues for modelling agents. Blueprint for affective computing: A sourcebook; 2010. p. 3–20.

    Google Scholar 

  29. Hudlicka E. Reasons for emotions. Integrated models of cognition systems. 2007;1:263.

    Article  Google Scholar 

  30. Wang Z, Ho S-B, Cambria E. A review of emotion sensing: categorization models and algorithms. Multimedia Tools and Applications. 2020;79(47):35553–82.

    Article  Google Scholar 

  31. Gross JJ. Handbook of emotion regulation. Guilford publications, 2013.

  32. Uusberg A, Taxer JL, Yih J, Uusberg H, Gross JJ. Reappraising reappraisal. Emotion Review. 2019;11(4):267–82.

    Article  Google Scholar 

  33. Clore GL, Ortony A. Psychological construction in the OCC model of emotion. Emotion Review. 2013;5(4):335–43.

    Article  Google Scholar 

  34. Sander D, Grandjean D, Scherer KR. A systems approach to appraisal mechanisms in emotion. Neural networks. 2005;18(4):317–52.

    Article  Google Scholar 

  35. Scherer KR. The dynamic architecture of emotion: evidence for the component process model. Cognition and emotion. 2009;23(7):1307–51.

    Article  Google Scholar 

  36. Hudlicka E. To feel or not to feel: the role of affect in human-computer interaction. Int J Hum Comput Stud. 2003;59(1–2):1–32.

    Article  Google Scholar 

  37. Mohammad SM. Word affect intensities. in Proceedings of the 11th Edition of the, Miyazaki, Japan, 2018.

  38. Majumder N, Poria S, Peng H, Chhaya N, Cambria E, Gelbukh A. Sentiment and sarcasm classification with multitask learning. IEEE Intelligent Systems. 2019;34(3):38–43.

    Article  Google Scholar 

  39. Basiri ME, Nemati S, Abdar M, Cambria E, Acharya UR. ABCDM: an attention-based bidirectional CNN-RNN deep model for sentiment analysis. Futur Gener Comput Syst. 2021;115:279–94.

    Article  Google Scholar 

  40. Akhtar MS, Ekbal A, Cambria E. How intense are you? Predicting intensities of emotions and sentiments using stacked ensemble [application notes]. IEEE Computational Intelligence Magazine. 2020;15(1):64–75.

    Article  Google Scholar 

  41. Hochreiter S, Schmidhuber J. Long short-term memory. Neural computation. 1997;9(8):1735–80.

    Article  Google Scholar 

  42. Cho K, Van Merriënboer B, Bahdanau D, Bengio Y. On the properties of neural machine translation: encoder-decoder approaches. in Proc. 8th Workshop Syntax, Semantics and Structure in Statistical Translation(SSST), Doha, Qatar. 2014.

  43. Smola AJ, Schölkopf B. A tutorial on support vector regression. Statistics and computing. 2004;14(3):199–222.

    Article  MathSciNet  Google Scholar 

  44. Cambria E, Li Y, Xing FZ, Poria S, Kwok K. SenticNet 6: ensemble application of symbolic and subsymbolic AI for sentiment analysis. In Proceedings of the 29th ACM international conference on information & knowledge management. 2020, pp. 105–114.

  45. Poria S, Peng H, Hussain A, Howard N, Cambria E. Ensemble application of convolutional neural networks and multiple kernel learning for multimodal sentiment analysis. Neurocomputing. 2017;261:217–30.

    Article  Google Scholar 

  46. Rashid M, Abu-Bakar SAR, Mokji M. Human emotion recognition from videos using spatio-temporal and audio features. Vis Comput. 2013;29(12):1269–75.

    Article  Google Scholar 

  47. Zadeh A, Chen M, Poria S, Cambria E, Morency L-P. Tensor fusion network for multimodal sentiment analysis. in Proc. of the Conf. on EmpiricalMethods on Natural Language Processing (EMNLP). 2017.

  48. Poria S, Cambria E, Hazarika D, Majumder N, Zadeh A, Morency L-P. Context-dependent sentiment analysis in user-generated videos. In Proceedings of the 55th annual meeting of the association for computational linguistics (volume 1: Long papers). 2017, pp. 873–883.

  49. Huan R-H, Shu J, Bao S-L, Liang R-H, Chen P, Chi K-K. Video multimodal emotion recognition based on Bi-GRU and attention fusion. Multimed Tools Appl. 2021;80(6):8213–40.

    Article  Google Scholar 

  50. Lin J, Mao W, Zeng DD. Personality-based refinement for sentiment classification in microblog. Knowledge-Based Systems. 2017;132:204–14.

    Article  Google Scholar 

  51. Kumar S, Gahalawat M, Roy PP, Dogra DP, Kim B-G. Exploring impact of age and gender on sentiment analysis using machine learning. Electronics. 2020;9(2):374.

    Article  Google Scholar 

  52. Díaz M, Johnson I, Lazar A, Piper AM, Gergle D. Addressing age-related bias in sentiment analysis. In Proceedings of the 2018 chi conference on human factors in computing systems. 2018, pp. 1–14.

  53. Poria S, Majumder N, Hazarika D, Cambria E, Gelbukh A, Hussain A. Multimodal sentiment analysis: addressing key issues and setting up the baselines. IEEE Intelligent Systems. 2018;33(6):17–25.

    Article  Google Scholar 

  54. Poria S, Cambria E, Bajpai R, Hussain A. A review of affective computing: from unimodal analysis to multimodal fusion. Information Fusion. 2017;37:98–125.

    Article  Google Scholar 

  55. Susanto Y, Cambria E, Ng BC, Hussain A. Ten years of sentic computing. Cogn Comput. 2022, 1–19.

  56. Young T, Hazarika D, Poria S, Cambria E. Recent trends in deep learning based natural language processing. IEEE Computational Intelligence Magazine. 2018;13(3):55–75.

    Article  Google Scholar 

  57. Churamani N, Barros P, Strahl E, Wermter S. Learning empathy-driven emotion expressions using affective modulations. In 2018 International Joint Conference on Neural Networks (IJCNN). 2018, IEEE, pp. 1–8.

  58. Lee Y, Rabiee A, Lee SY. Emotional end-to-end neural speech synthesizer. In In: Proceedings of the International Conference on Neural Information Processing Systems (NIPS), Long Beach, CA, USA. 2017.

  59. Um, Se-Yun, Sangshin Oh, Kyungguen Byun, Inseon Jang, ChungHyun Ahn, and Hong-Goo Kang. Emotional speech synthesis with rich and granularized control. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain. 2020.

  60. Masuda M, Kato S. Motion rendering system for emotion expression of human form robots based on Laban movement analysis. In 19Th international symposium in robot and human interactive communication. 2010, IEEE, pp. 324–329.

  61. Nakagawa K, Shinozawa K, Ishiguro H, Akimoto T, Hagita N. Motion modification method to control affective nuances for robots. In 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems. 2009, IEEE, pp. 5003–5008.

  62. Bozkurt E, Yemez Y, Erzin E. Affective synthesis and animation of arm gestures from speech prosody. Speech Communication. 2020;119:1–11.

    Article  Google Scholar 

  63. Hasegawa D, Kaneko N, Shirakawa S, Sakuta H, Sumi K. Evaluation of speech-to-gesture generation using bi-directional LSTM network. In In: Proceedings of the ACM International Conference on Intelligent Virtual Agents (IVA),Sydney, NSW, Australia. 2018.

  64. Yoon Y, Ko WR, Jang M, Lee J, Kim J, Lee G. Robots learn social skills: end-to-end learning of co-speech gesture generation for humanoid robots. In In: Proceedings of the International Conference on Robotics and Automation(ICRA), Montreal, QC, Canada. 2019, pp 4303–4309.

  65. Tsiourti C, Weiss A, Wac K, Vincze M. Multimodal integration of emotional signals from voice, body, and context: Effects of (in) congruence on emotion recognition and attitudes towards robots. Int J Soc Robot. 2019;11(4):555–73.

    Article  Google Scholar 

  66. Lubis N, Sakti S, Yoshino K, Nakamura S. Eliciting positive emotion through affect-sensitive dialogue response generation: a neural network approach. In Proceedings of the AAAI Conference on Artificial Intelligence. 2018;32.

  67. Eric M, Manning CD. A copy-augmented sequence-to-sequence architecture gives good performance on task-oriented dialogue. 2017. arXiv preprint arXiv: 1701.04024.

  68. Peng Y, Fang Y, Xie Z, Zhou G. Topic-enhanced emotional conversation generation with attention mechanism. Knowledge-Based Systems. 2019;163:429–37.

    Article  Google Scholar 

  69. Ma Y, Nguyen KL, Xing FZ, Cambria E. A survey on empathetic dialogue systems. Information Fusion. 2020;64:50–70.

    Article  Google Scholar 

  70. Ong D, Soh H, Zaki J, Goodman N. Applying probabilistic programming to affective computing. IEEE Trans Affect Comput. 2019.

  71. Phelps EA. Emotion and cognition: insights from studies of the human amygdala. Annu Rev Psychol. 2006;57:27–53.

    Article  Google Scholar 

  72. Smith R, Lane RD. The neural basis of one’s own conscious and unconscious emotional states. Neurosci Biobehav Rev. 2015;57:1–29.

    Article  Google Scholar 

  73. Gainotti G. Emotions and the right side of the brain. Springer; 2020.

    Book  Google Scholar 

  74. Arbib MA, Fellous J-M. Emotions: from brain to robot. Trends in cognitive sciences. 2004;8(12):554–61.

    Article  Google Scholar 

  75. Hudlicka E. Beyond cognition: modeling emotion in cognitive architectures. In ICCM. 2004, pp. 118–123.

  76. Pérez J, Cerezo E, Seron FJ. a. A cognitive-affective architecture for ECAs. Biologically Inspired Cognitive Architectures. 2016;18:33–40.

  77. Anderson JR, Bothell D, Byrne MD, Douglass S, Lebiere C, Qin Y. An integrated theory of the mind. Psychological review. 2004;111(4):1036.

    Article  Google Scholar 

  78. Belavkin RV. The role of emotion in problem solving. In Proceedings of the AISB’01 Symposium on emotion, cognition and affective computing, Heslington, York, England. 2001, Citeseer, pp. 49–57.

  79. Juvina I, Larue O, Hough A. Modeling valuation and core affect in a cognitive architecture: the impact of valence and arousal on memory and decision-making. Cogn Syst Res. 2018;48:4–24.

    Article  Google Scholar 

  80. Laird JE. The Soar cognitive architecture. MIT press. 2019.

  81. Flavián-Blanco C, Gurrea-Sarasa R, Orús-Sanclemente C. Analyzing the emotional outcomes of the online search behavior with search engines. Comput Hum Behav. 2011;27(1):540–51.

    Article  Google Scholar 

  82. McCrae RR, John OP. An introduction to the five-factor model and its applications. Journal of personality. 1992;60(2):175–215.

    Article  Google Scholar 

  83. Gratch J, Marsella S. A domain-independent framework for modeling emotion. Cognitive Systems Research. 2004;5(4):269–306.

    Article  Google Scholar 

  84. De Silva, L. C., Miyasato, T., and Nakatsu, R. Facial emotion recognition using multi-modal information. In Proceedings of ICICS, 1997 International Conference on Information, Communications and Signal Processing. Theme: Trends in Information Systems Engineering and Wireless Multimedia Communications Cat. 1997;1:397–401. IEEE.

  85. Baars BJ. A cognitive theory of consciousness. Cambridge University Press; 1993.

    MATH  Google Scholar 

  86. Franklin S, Ramamurthy U, D’Mello SK, McCauley L, Negatu A, Silva RL, Datla V. Lida: a computational model of global workspace theory and developmental learning. 2007

  87. Snaider J, McCall R, Franklin S. The LIDA framework as a general tool for AGI. In International Conference on Artificial General Intelligence. 2011, Springer, pp. 133–142.

  88. Pereira D, Oliveira E, Moreira N, Sarmento L. Towards an architecture for emotional BDI agents. In 2005 portuguese conference on artificial intelligence. 2005, IEEE, pp. 40–46.

  89. Pereira, D., Oliveira, E., and Moreira, N. Modelling emotional BDI agents. In Workshop on Formal Approaches to Multi-Agent Systems (FAMAS 2006), Riva Del Garda, Italy (August 2006). 2006, Citeseer.

  90. Jiang H, Vidal JM, Huhns MN. EBDI: an architecture for emotional agents. In Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems. 2007, pp. 1–3.

  91. Taverner J, Vivancos E, Botti V. A fuzzy appraisal model for affective agents adapted to cultural environments using the pleasure and arousal dimensions. Information Sciences. 2021;546:74–86.

    Article  Google Scholar 

  92. Sánchez Y, Coma T, Aguelo A, Cerezo E. Applying a psychotherapeutic theory to the modeling of affective intelligent agents. IEEE Transactions on Cognitive and Developmental Systems. 2019;12(2):285–99.

    Article  Google Scholar 

  93. Ellis A, Harper RA. A new guide to rational living. CA Wilshire. 1975.

  94. Becker-Asano C. WASABI: affect simulation for agents with believable interactivity, vol. 319. IOS Press; 2008.

    Google Scholar 

  95. Becker-Asano C, Wachsmuth I. Affective computing with primary and secondary emotions in a virtual human. Autonomous Agents and Multi-Agent Systems. 2010;20(1):32–49.

    Article  Google Scholar 

  96. Mehrabian A. Pleasure-arousal-dominance: a general framework for describing and measuring individual differences in temperament. Curr Psychol. 1996;14(4):261–92.

    Article  MathSciNet  Google Scholar 

  97. El-Nasr MS, Yen J, Ioerger TR. Flame–fuzzy logic adaptive model of emotions. Auton Agents Multi-Agent Syst. 2000;3:219–257.

  98. Gebhard P. Alma: a layered model of affect. In Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems. 2005, pp. 29–36.

  99. Ojha S, Vitale J, Williams M-A. EEGS: a transparent model of emotions. 2020. arXiv preprint. arXiv: 2011.02573.

  100. McDuff D, Czerwinski M. Designing emotionally sentient agents. Communications of the ACM. 2018;61(12):74–83.

    Article  Google Scholar 

  101. Rosales J-H, Rodríguez L-F, Ramos F. A general theoretical framework for the design of artificial emotion systems in autonomous agents. Cogn Syst Res. 2019;58:324–41.

    Article  Google Scholar 

  102. Smith CA, Lazarus RS, et al. Emotion and adaptation. Handbook of personality: Theory and research; 1990. p. 609–37.

    Google Scholar 

  103. Castellanos S, Rodríguez L-F, Castro LA, Gutierrez-Garcia JO. A computational model of emotion assessment influenced by cognition in autonomous agents. Biologically inspired cognitive architectures. 2018;25:26–36.

    Article  Google Scholar 

  104. Scherer KR. Appraisal considered as a process of multilevel sequential checking. Appraisal processes in emotion: Theory, methods, research. 2001;92(120):57.

    Google Scholar 

  105. Ortony A, Clore GL, Collins A. The cognitive structure of emotions. Cambridge University Press; 1990.

    Google Scholar 

  106. Gebhard P, Kipp M, Klesen M, Rist T. Adding the emotional dimension to scripting character dialogues. In International Workshop on Intelligent Virtual Agents. 2003, Springer, pp. 48–56.

  107. van Vugt HC, Hoorn JF, Konijn EA. Interactive engagement with embodied agents: an empirically validated framework. Comput Anim Virtual Worlds. 2009;20(2–3):195–204.

    Article  Google Scholar 

  108. Ojha S, Williams M-A. Emotional appraisal: a computational perspective. In Fifth annual conference on advances in cognitive systems. 2017, ACS.

  109. Ojha S, Vitale J, Williams M-A. A domain-independent approach of cognitive appraisal augmented by higher cognitive layer of ethical reasoning. In Annual meeting of the cognitive science society. 2017, Cognitive Science Society.

  110. Broekens J, Bosse T, Marsella SC. Challenges in computational modeling of affective processes. IEEE Transactions on Affective Computing. 2013;4(3):242–5.

    Article  Google Scholar 

  111. Pollack ME, Ringuette M. Introducing the Tileworld: experimentally evaluating agent architectures. In AAAI. 1990;90:183–189.

  112. Bourgais M, Taillandier P, Vercouter L. An agent architecture coupling cognition and emotions for simulation of complex systems. In Social Simulation Conference. 2016.

  113. Grignard A, Taillandier P, Gaudou B, Vo DA, Huynh NQ, Drogoul A. Gama 1.6: advancing the art of complex agent-based modeling and simulation. In International conference on principles and practice of multi-agent systems. 2013, Springer, pp. 117–131.

  114. Ojha S, Vitale J, Williams M-A. Computational emotion models: a thematic review. Int J Soc Robot. 2020, 1–27.

  115. Taverner J, Alfonso B, Vivancos E, Botti VJ. Modeling personality in the affective agent architecture GenIA3. In ICAART. 2018;1:236–243.

  116. Mohammadi G, Vuilleumier P. A multi-componential approach to emotion recognition and the effect of personality. IEEE Trans Affect Comput. 2020.

  117. Gross JJ. Emotion regulation: conceptual and empirical foundations. 2014

  118. Gross JJ. Emotion regulation: affective, cognitive, and social consequences. Psychophysiology. 2002;39(3):281–91.

    Article  Google Scholar 

  119. Lin J, Spraragen M, Zyda M. Computational models of emotion and cognition. In Advances in Cognitive Systems. 2012, Citeseer.

Download references

Author information

Authors and Affiliations

  1. School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran

    Raziyeh Zall & Mohammad Reza Kangavari

Authors
  1. Raziyeh Zall
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohammad Reza Kangavari
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Raziyeh Zall.

Ethics declarations

Ethical Standard

This article does not contain any studies with animals performed by any of the authors.

Informed Consent

Informed consent was not required as no human or animals were involved.

Conflict of Interest

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zall, R., Kangavari, M.R. Comparative Analytical Survey on Cognitive Agents with Emotional Intelligence. Cogn Comput 14, 1223–1246 (2022). https://doi.org/10.1007/s12559-022-10007-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12559-022-10007-5

Keywords

Search

Navigation