Многомодальная когнитивная обработка с использованием искусственной эндокринной системы для развития аффективных виртуальных агентов
Ключевые слова:
многомодальность, эмоциональный агент, когнитивная робототехника, эмоциональные вычисления, искусственная эндокринная системаАннотация
В этой статье представлена всеобъемлющая архитектура эмоционального и аффективного процесса, происходящего в виртуальном агенте. Соединяя визуальные, аудио- и текстовые эмоции пользователей как аффективные источники в системе, виртуальный агент может оценивать настроение клиентов. С целью имитации воздействия гормонов человека в виртуальном агенте в предлагаемой системе используется искусственная эндокринная система (ИЭС) для выявления настроения и биологических потребностей посредством контроля уровня концентрации воздействующих гормонов. Аффективный процессор агента задействует модули ИЭС, параметров личности и настроения для управления внутренним состоянием. Интеллектуальный виртуальный агент взаимодействует с клиентами в соответствии со своими аффективными состояниями. Предлагаемая система представляет собой полную платформу для захвата каналов эмоций в сети с целью анализа и обработки их в аффективном движке для определения эмоциональной окраски ответа.Литература
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47. Kim H.J., Shin K.H., Swanger N. Burnout and engagement: A comparative analysis using the Big Five personality dimensions // International Journal of Hospitality Management. 2008. vol. 28. no. 1. pp. 96–104.
48. Del B.A., Vicario E., Zingoni D. An interactive environment for the visual programming of virtual agents // Proceedings of 1994 IEEE Symposium on Visual Languages. 1994. pp. 145–152.
49. Alfonsi B. “Sassy” Chatbot Wins with Wit // IEEE Intelligent Systems. 2006. pp. 6–7.
50. Herrero P., de Antonio A. Modelling Intelligent Virtual Agent Skills with Human-Like Senses // Conference of Computer Science. Springer. 2004. vol. 3038. pp. 575–582.
51. Del B.A., Vicario E. Specification by-Example of Virtual Agents Behavior // IEEE Translations on Visualization and Computer Graphics. 1995. vol. 1. no. 4. pp. 350– 360.
52. Heudin J.C. Evolutionary virtual agent // Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology. 2004. pp. 93–98.
53. Zhao R., Papangelis A., Cassell J. A dyadic computational model of rapport management for human-virtual agent interaction // International Conference on Intelligent Virtual Agents. 2014. pp. 514–527.
54. Badler N., Allbeck J., Zhao L., Byun M. Representing and Parameterizing Agent Behaviors // Computer Animation. 2002. pp. 133–143.
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57. Adobbati R. Gamebots: A 3D Virtual World Test-Bed For Multi-Agent Research // Proceedings of the Second International Workshop on Infrastructure for Agents, MAS, and Scalable MAS. 2001. vol. 5. 6 p.
58. Fernandez-Ares A. et. al. Its time to stop: A comparison of termination conditions in the evolution of game bots // European Conference on the Applications of Evolutionary Computation. 2015. pp. 355–368.
59. Jutla D., Craig J., Bodorik P. Enabling and measuring electronic customer relationship management readiness // Proceedings of the 34th Annual Hawaii International Conference on System Sciences. 2001. 10 p.
60. Johnson A., Roush T., Fulton M., Reese A. Implementing Physical Capabilities for an Existing Chatbot by Using a Repurposed Animatronic to Synchronize Motor Positioning with Speech // International Journal of Advanced Studies in Computers, Science and Engineering. 2017. vol. 6. pp. 20.
61. Vieira A., Sehgal A. How Banks Can Better Serve Their Customers Through Artificial Techniques // Digital Marketplaces Unleashed. Digital Marketplaces Unleashed. 2018. pp. 311–326.
62. Folstad A., Brandtzaeg P.B. Chatbots and the new world of HCI // Interactions. 2017. vol. 24. no. 4. pp. 38–42.
63. Liu Y. et. al. Chatting system, method and apparatus for virtual pet // Google Patents. 2014. US Patent 8645479.
2. Samani H.A., Elham S. A multidisciplinary artificial intelligence model of an affective robot // International Journal of Advanced Robotic Systems. SAGE Publications Sage. 2012. vol. 9. pp. 1–11.
3. Sam T., Silvervarg A., Gulz A., Tom Z. Physical vs. Virtual Agent Embodiment and Effects on Social Interaction // International Conference on Intelligent Virtual Agents. 2016. pp. 412–415.
4. Benton M. et al. Quality in Chatbots and Intelligent Conversational Agents // Software Quality Professional Magazine. 2017. vol. 19(3).
5. Samani H. Cognitive robotics // CRC Press. 2015. 215 p.
6. Ivan M. Some Related Article I Wrote // Some Fine Journal. 1999. vol. 99. pp. 1–100.
7. Andreas N. A Book He Wrote // Erewhon: His Publisher. 1999.
8. Liu P., Han S., Meng Z. Tong Y. Facial expression recognition via a boosted deep belief network // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2014. pp. 1805–1812.
9. Happy S.L., Aurobinda R. Automatic facial expression recognition using features of salient facial patches // IEEE Transactions on Affective Computing. 2015. vol. 6. pp. 1–12.
10. Evangelos S., Hatice G., Andrea C. Automatic analysis of facial affect: A survey of registration, representation, and recognitio // IEEE transactions on pattern analysis and machine intelligence. 2015. vol. 37. pp. 1113–1133.
11. Ge S., Samani H., Ong Y., Hang C. Active affective facial analysis for human-robot interaction // The 17th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN 2008). 2008. pp. 83–88.
12. Abboud B., Davoine F., Dang M. Facial expression recognition and synthesis based on an appearance model // Signal Processing: Image Communication. 2004. vol. 19. pp. 723–740.
13. Cohen I. et al. Facial expression recognition from video sequences: temporal and static modeling // Computer Vision and Image Understanding. 2003. vol. 91. pp. 160–187.
14. Krumhuber E., Kappas A., Manstead A. Effects of dynamic aspects of facial expressions: a review // Emotion Review. 2013. vol. 5. pp. 41–46.
15. Bartlett M., Littlewort G., Fasel I., Movellan J. Real Time Face Detection and Facial Expression Recognition: Development and Applications to Human Computer Interaction // CVPR Workshop on Computer Vision and Pattern Recognition for Human-Computer Interaction. 2003. vol. 5. pp. 52–53.
16. Fasel B., Luettin J. Automatic facial expression analysis: a survey // Pattern Recognition. 2003. vol. 36. pp. 259–275.
17. Mohammad S. Sentiment analysis: Detecting valence, emotions, and other affectual states from text // Emotion measurement. 2015. pp. 201–238.
18. Li W., Xu H. Text-based emotion classification using emotion cause extraction // Expert Systems with Applications. 2014. vol. 41. pp. 1742–1749.
19. Lee C., Lee G. Emotion recognition for affective user interface // The 16th IEEE International Symposium on Robot and Human Interactive Communication. 2007. vol. 8. pp. 798–801.
20. Zhe X., Boucouvalas A.C. Text-to-Emotion Engine for Real Time Internet Communication // Proceedings of International Symposium on Communication Systems. Networks and DSPs. 2002. pp. 164–168.
21. Lee C.M., Narayanan S.S. Toward detecting emotions in spoken dialogs // IEEE transactions on speech and audio processing. 2005. vol. 13. pp. 293–303.
22. Cristianini N., Shawe-Taylor J. An Introduction to Support Vector Machines // Cambridge University Press. 2000. 204 p.
23. Povoda L. et al. Optimization Methods in Emotion Recognition System // Radioengineering. 2016. vol. 25. pp. 565.
24. Saadatian E. et al. Artificial Intelligence Model of an Smartphone-Based Virtual Companion // International Conference on Entertainment Computing. 2014. pp. 173–178.
25. Elham S., Samani H., Arash T., Ryohei N. Technologically mediated intimate communication: An overview and future directions // International Conference on Entertainment Computing. 2013. pp. 93–104.
26. Zhang Y., Ren F., Kuroiwa S. Semi-Automatic Emotion Recognition from Chinese Text // Proceedings of the Ninth IASTED International Conference on Intelligent Systems and Control. 2006.
27. Salton G., Yang C.S. On the Specification of Term Values in Automatic Indexing // Journal of documentation. 1973. vol. 29. no. 4. pp. 351–372.
28. Dasarathy B.V. Nearest neighbor (NN) norms: nn pattern classification techniques // Calif.: IEEE Computer Society Press. 1991. 550 p.
29. Bhatti M.W., Wang Y., Guan L. A neural network approach for human emotion recognition in speech // Proceedings of the 2004 International Symposium on Circuits and Systems (ISCAS’04). 2004. vol. 2. pp. II–181.
30. Specht D.F. A general regression neural network // IEEE transactions on neural networks. 1991. vol. 2(6). pp. 568–576.
31. Dellaert F., Polzin T., Waibel A. Recognizing emotion in speech // Proceedings of the Fourth International Conference on Spoken Language (ICSLP 96). 1996. vol. 3. pp. 1970–1973.
32. Teng Z., Ren F., Kuroiwa S. Emotion Recognition from Text based on the Rough Set Theory and the Support Vector Machines // International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE 2007). 2007. pp. 36–41.
33. Breazeal C.L. Designing Sociable Robots // Bradford Book. 2002. 282 p.
34. Oudeyer P.Y. The production and recognition of emotions in speech: features and algorithms // International Journal of Human-Computer Studies. 2003. vol. 59. pp. 157–183.
35. McGilloway S. et al. Approaching Automatic Recognition of Emotion from Voice: A Rough Benchmark // ISCA Tutorial and Research Workshop (ITRW) on Speech and Emotion. 2000. 6 p.
36. Purves W.K., Orians G.H., Heller H.C. Life: The Science of Biology: 7th ed. // 2003. 1121 p.
37. Straub R.H. Interaction of the endocrine system with inflammation: a function of energy and volume regulation. Arthritis research & therapy // 2014. vol. 16. no. 1. 15 p.
38. Samani H., Saadatian E., Jalaeian B. Biologically Inspired Artificial Endocrine System for Human Computer Interaction // International Conference on Human-Computer Interaction. 2015. pp. 71–81.
39. Norman A.W., Litwack G. Hormones // Academic Press. 1987. 806 p.
40. Morrison M.F. Hormones, Gender and the Aging Brain: The Endocrine Basis of Geriatric Psychiatry // Cambridge University Press, 2000. 259 p.
41. Pfaff D.W., Phillips M.I., Rubin R.T. Principles of Hormone/Behavior Relations // Academic Press. 2004. 360 p.
42. Timmis J., Neal M. Once more Unto the Breach: Towards Artificial Homeostatsis // Recent deveopments in Biologically inspired computing. 2005. pp. 340–365.
43. Vargas P. et al. Artificial homeostatic system: a novel approach // European Conference on Artificial Life (ECAL 2005). 2005. pp. 754–764.
44. Russell J.A. A circumplex model of affect // Journal of Personality and Social Psychology.1980. vol. 39. pp. 1161–1178.
45. Thayer R.E. The Biopsychology of Mood and Arousal // Oxford University Press. 1989. 234 p.
46. Barrick M.R., Mount M.K. The Big Five Personality Dimensions and Job Performance: A Meta-Analysis // Personnel Psychology. 1991. vol. 44. no. 1. pp. 1–26.
47. Kim H.J., Shin K.H., Swanger N. Burnout and engagement: A comparative analysis using the Big Five personality dimensions // International Journal of Hospitality Management. 2008. vol. 28. no. 1. pp. 96–104.
48. Del B.A., Vicario E., Zingoni D. An interactive environment for the visual programming of virtual agents // Proceedings of 1994 IEEE Symposium on Visual Languages. 1994. pp. 145–152.
49. Alfonsi B. “Sassy” Chatbot Wins with Wit // IEEE Intelligent Systems. 2006. pp. 6–7.
50. Herrero P., de Antonio A. Modelling Intelligent Virtual Agent Skills with Human-Like Senses // Conference of Computer Science. Springer. 2004. vol. 3038. pp. 575–582.
51. Del B.A., Vicario E. Specification by-Example of Virtual Agents Behavior // IEEE Translations on Visualization and Computer Graphics. 1995. vol. 1. no. 4. pp. 350– 360.
52. Heudin J.C. Evolutionary virtual agent // Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology. 2004. pp. 93–98.
53. Zhao R., Papangelis A., Cassell J. A dyadic computational model of rapport management for human-virtual agent interaction // International Conference on Intelligent Virtual Agents. 2014. pp. 514–527.
54. Badler N., Allbeck J., Zhao L., Byun M. Representing and Parameterizing Agent Behaviors // Computer Animation. 2002. pp. 133–143.
55. Samani H. Lovotics: Loving robots // LAP LAMBERT Academic Publishing. 2012. 168 p.
56. Bloch L.R., Lemish D. Disposable Love: The Rise and Fall of a Virtual // New Media & Society. 1999. vol. 1. no. 3. pp. 283–303.
57. Adobbati R. Gamebots: A 3D Virtual World Test-Bed For Multi-Agent Research // Proceedings of the Second International Workshop on Infrastructure for Agents, MAS, and Scalable MAS. 2001. vol. 5. 6 p.
58. Fernandez-Ares A. et. al. Its time to stop: A comparison of termination conditions in the evolution of game bots // European Conference on the Applications of Evolutionary Computation. 2015. pp. 355–368.
59. Jutla D., Craig J., Bodorik P. Enabling and measuring electronic customer relationship management readiness // Proceedings of the 34th Annual Hawaii International Conference on System Sciences. 2001. 10 p.
60. Johnson A., Roush T., Fulton M., Reese A. Implementing Physical Capabilities for an Existing Chatbot by Using a Repurposed Animatronic to Synchronize Motor Positioning with Speech // International Journal of Advanced Studies in Computers, Science and Engineering. 2017. vol. 6. pp. 20.
61. Vieira A., Sehgal A. How Banks Can Better Serve Their Customers Through Artificial Techniques // Digital Marketplaces Unleashed. Digital Marketplaces Unleashed. 2018. pp. 311–326.
62. Folstad A., Brandtzaeg P.B. Chatbots and the new world of HCI // Interactions. 2017. vol. 24. no. 4. pp. 38–42.
63. Liu Y. et. al. Chatting system, method and apparatus for virtual pet // Google Patents. 2014. US Patent 8645479.
Опубликован
2018-02-02
Как цитировать
Самани, Х. (2018). Многомодальная когнитивная обработка с использованием искусственной эндокринной системы для развития аффективных виртуальных агентов. Труды СПИИРАН, 1(56), 56-75. https://doi.org/10.15622/sp.56.3
Раздел
Искусственный интеллект, инженерия данных и знаний
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