ЗАСОБИ МОДЕЛЮВАННЯ ПОВЕДІНКИ АГЕНТІВ В ІНФОРМАЦІЙНО-КОМУНІКАЦІЙНИХ СИСТЕМА
Ключові слова:
асоби моделювання, поведінка людини, агентне моделювання, штучні нейронні мережі, нечітка логіка, генетичні алгоритми, підходи, засновані на знаннях, ланцюги Маркова
Анотація
Аналізуються найбільш поширені засоби та підходи до моделювання поведінки агентів в інформаційно-комунікаційних системах. Зокрема розглянуті таки засоби як агентне моделювання, штучні нейронні мережі, нечітка логіка, генетичні алгоритми, підходи, засновані на знаннях, ланцюги Маркова і ін. Крім класичних засобів та підходів розглянуто також нові прикладні методи, а саме: людино-центрована система, аналіз відео, накопичувач даних, що керується даними, динамічні чинники. В результаті проведеного аналізу отримані висновки про поточний стан питань моделювання поведінки людини. Визначено, що існує значна кількість підходів, і всі вони мають свої переваги і недоліки. Тому неможливо визначити оптимальний підхід до моделювання поведінки людини, але можна визначити мету, для якої застосування того чи іншого підходу може бути рекомендовано. В роботі наведена схема з інформацією, із зазначенням можливих рішень для подолання обмежень різних підходів. Аналіз цих даних показав, що агентно-орієнтований підхід вирішує більшість проблем і знімає обмеження, характерні для інших методологій. Це підтверджується тим фактом, що агентне моделювання є гнучким підходом, який широко використовується в багатьох контекстах моделювання поведінки людиниЗавантаження
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Посилання
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5. Baines T., Mason S., Siebers P. O., Ladbrook J., 2004, Humans: the missing link in manufacturing simulations, Simulation Modelling Practice and Theory, 12, 515-526.
6. Bocca E., Briano E., Bruzzone G. A., Massei M., 2007, Evaluation of the impact of different human factor models on industrial and business processes, Simulation Modelling Practice and Theory, 15, 199-218.
7. Cacciabue P. C., 1998, Modeling and Simulation of Human Behavior in System Control, Springer, Varese.
8. Liu A., Pentland A., 1999, Modeling and prediction of human behavior, Neural computation, 11(1), 229-242.
9. Embrey D., Understanding human behaviour and error, Human Reliability Associates 1, 2005; 1-10.
10. Macal C. M., North M. J., 2005, Tutorial on agentbased modeling and simulation, Proceedings of the 37th conference on Winter simulation. Winter Simulation Conference, 2-15.
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12. Bonabeau E., 2001, Agent-based modeling: methods and techniques for simulating human systems, Proc. National Academy of Sciences, 99(3), 7280-7287.
13. Deljoo A., Janssen M., Tan Y. H., 2013, The Role of Complex Systems in Public-Private Service Networks, Proceedings of the European Conference on Complex Systems 2012, 279-285.
14. Bazghandi, A., 2012, Techniques, advantages and problems of agent agent modeling for traffic simulation, Int J Comput Sci, 9(1), 115-119.
15. Elkosantini S., 2015, Toward a new generic behavior model for human centered system simulation, Simulation Modelling Practice and Theory, 52, 108-122.
16. Bruzzone A.G., Tremori A., Tarone F., Madeo F. (2011). Intelligent agents driving computer generated forces for simulating human behaviour in urban riots. International Journal of Simulation and Process Modelling. Vol. 6, Is. 4, 2011, pp. 308-316
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18. Bruzzone, A., Massei, M., Longo, F., Poggi, S., Agresta, M., Bartolucci, C., Nicoletti, L. (2014). Human behavior simulation for complex scenarios based on intelligent agents. Simulation Series, 46 (2), pp. 71-80.
19. Longo F., Nicoletti L., Vena S., Padovano A., (2014). Serious games at increased impact on culture and tourism. Proceedings of the 26th European Modeling and Simulation Symposium, EMSS 2014, pp. 641-648.
20. Longo, F., Nicoletti, L., Florio, G., Vetrano, M., Bruno, L., Caputi, L. (2015). Inside virtual: A new app for interactive and intelligent cultural heritage fruition. Proc. of the 27th European Modeling and Simulation Symp., EMSS 2015, pp. 471-478.
21. Gary An. In silico experiments of existing and hypothetical cytokine-directed clinical trials using agent-based modeling. Critical care medicine, 32(10):2050-2060, 2004.
22. Nuria Pelechano, Jan M Allbeck, and Norman I Badler. Controlling individual agents in high-density crowd simulation. In Proc. of the 2007 ACM SIG- GRAPH/Eurographics symp/ on Comp. animation, pp. 99-108. Eurographics Association, 2007.
23. Charles M Macal and Michael J North. Tutorial on agent-based modelling and simulation. Journal of simulation, 4(3): 151-162, 2010.
24. Igor Kotenko. Agent-based modeling and simulation of cyber-warfare between malefactors and security agents in internet. In Proceedings of 19th European Simulation Multiconference ’’Simulation in wider Europe”, 2005.
25. Igor Kotenko. Agent-based modelling and simulation of network cyber-attacks and cooperative defence mechanisms. Discrete Event Simulations, pages 223-246, 2010.
26. Dennis Grunewald, Marco Liitzenberger, Joel Chinnow, Rainer Bye, Karsten Bsufka, and Sahin Albayrak. Agent-based network security simulation. In Proceedings of The 10th International Conference on Autonomous Agents and Multiagent Systems- Volume 3, pages 1325-1326. International Foundation for Autonomous Agents and Multiagent Systems, 2011.
27. Holger R. M., MarkB. J., ShahinM. A., 2008, State of the art of 8artificial neural net-works in geotechnical engineering, Electronic Journal of Geotechnical Engineering, 8, 1-26.
28. Fausett L. V., 1994, Fundamentals neural networks: Architecture, algorithms, and applications, Prentice-Hall, Englewood Cliffs.
29. Schmidhuber J., 2015, Deep learning in neural networks: An overview, Neural Networks, 61, 85-117.
30. Abdel-Malek K., Arora J., Bataineh M., Marler T., 2016, Neural network for dynamic human motion prediction, Expert Systems with Applications, 48, 26-34.
31. Xu J. H., 2011 Application of Artificial Neural Network (ANN) for Prediction of Maritime Safety, Information and Management Engineering, Springer Berlin Heidelberg, 34-38.
32. Dey A.K., 2001, Understanding and using context, Personal Ubiquit. Comput, 5, 4-7.
33. Calvo-Florese M. D.,Cuellar M. P., Lilius J., Rodriguez N. D., 2014, A fuzzy ontology for semantic modelling and recognition of human behaviour, Knowledge-Based Systems, 66(0), 46-60.
34. Enam, S., Godil S., QidwaiU, ShamimM., 2011, Fuzzy logic: A "simple" solution for complexities in neurosciences?, Surgical neurology international.
35. Reusch B., 2006, Theory and Applications: International Conference 9th Fuzzy Days in Dortmund, 2006 Proceedings, 38, 18-20.
36. Cannon M. E., Sied S., 2004, Fuzzy logic-based map matching algorithm for vehicle navigation system in urban canyons, ION National Technical Meeting, San Diego, CA, p. 26-28.
37. Baines T., Mason S., Siebers P. O., Ladbrook J., 2004, Humans: the missing link in manufacturing simulation?, Simulation Modelling Practice and Theory, 12, 515-526.
38. Sugeno M., Yasukawa T., 1993, A Fuzzy-Logic-Based- Approch to Qualitative Modelling, IEE Transaction on Fuzzy Logic System, 1, vol. 1, 7-31.
39. De Pedro T., Garcia R., Gonzalez C., Naranjo J. E., Sotelo M. A., 2007, Using Fuzzy Logic in Automated Vehicle Control, IEEE Intelligent System, 22 (1), 36-45.
40. Acampora G., Foggia P., Saggese A., Vento V., 2015, A hierarchical neuro-fuzzy architecture for human behavior analysis, Information Sciences, 130, BOMS.
41. Acampora G., Foggia P., Saggese A., Vento V., 2012, Combining Neural Networks and Fuzzy Systems for Human Behavior Understanding, 2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance, 88-93.
42. Deepa S. N., Sivanandam S. N., 2007, Introduction to genetic algorithms, Springer Science & Business Media, New York, 34-35.
43. Tabassum M., Kuruvilla M., 2014, A genetic algorithm analysis towards optimization solutions, International Journal of Digital Information and Wireless Communications, IJDIWC, 4(1).
44. Kosorukoff A., 2001, Human based genetic algorithm, Systems, Man, and Cybernetics, 2001 IEEE International Conference, IEEE, 5, 3464-3469.
45. Luenberger D. G., 1979, Introduction to Dynamic Systems: Theory, Model and Applications, Stanford, John Wiley & Sons.
46. Liu A., Pentland A., 1999, Modeling and prediction of human behavior, Neural computation, 11(1), 229-242.
47. Dongyue H., Jixiang N., Qiang Z., Xiaoqing W., Zhiting L., Analyzing and modeling heterogeneous behavior, Physica A, 450, 2016; 287-293
48. Liu A., Pentland A., 1999, Modeling and prediction of human behavior, Neural computation, 11(1), 229-242.
49. Vladimir Gorodetski and Igor Kotenko. Attacks against computer network: formal grammar-based framework and simulation tool. In Proceedings of International Workshop on Recent Advances in Intrusion Detection, pages 219-238. Springer, 2002.
50. Blake Shepard, Cynthia Matuszek, C Bruce Fraser, William Wechtenhiser, David Crabbe, Zelal Gungordii, John Jantos, Todd Hughes, Larry Lefkowitz, Michael J Witbrock, et al. A knowledge-based approach to network security: applying eye in the domain of network risk assessment. In Proceedings of the National Conference on Artificial Intelligence, volume 20, page 1563. Menlo Park, CA; Cambridge, MA; London; AAAI Press; MIT Press; 1999, 2005.
51. Jordi Conesa, Veda C Storey, and Vijayan Sugumaran. Usability of upper level ontologies: The case of researchcyc. Data & Knowledge Engineering, 69(4):343-356, 2010.
52. Botia, J. A., Campuzano F., Garcia-Valverde T., Serrano, E., 2014, Generation of human computational models with knowledge engineering, Engineering Applications of Artificial Intelligence, 35, 259-276.
53. Oliver N., 2016, Data-driven Human Behavior Models: Opportunities and Challenges, Proceedings of the 4th Spanish Conference on Information Retrieval, ACM.
54. Abebe E, Almashor M., Beloglazov A., Richter J., Charles Barton Steer K. C. B, 2016, Simulation of wildfire evacuation with dynamic factors and model composition, Simulation Modelling Practice and Theory, 60, 144-159.
55. Sun, R. (2006). The CLARION cognitive architecture: Extending cognitive modeling to social simulation. In: Cognition and Multi-Agent Interaction: From Cognitive Modeling to Social Simulation (SUN, R., ed.). Cambridge University Press, pp. 79-99. http://www.cogsci.rpi.edu/~rsun/sun.clarion2005.pdf.
2. Ardalan A., Ejeta L. T., Paton D., 2015, Application of Behavioral Theories to Disaster and Emergency Health Preparedness: A Systematic Review, PLoS currents, 7.
3. Mavor A. S., Pew R. W., 1998,Modeling human and organizational behavior: Application to military simulations, National Academies Press, Washington D C..
4. Cordar A., Lampotang S., Lok B., Robb A., Wendling, A., White C., 2015, A comparison of speaking up behavior during conflict with real and virtual humans, Computers in Human Behavior, 52, 12-21.
5. Baines T., Mason S., Siebers P. O., Ladbrook J., 2004, Humans: the missing link in manufacturing simulations, Simulation Modelling Practice and Theory, 12, 515-526.
6. Bocca E., Briano E., Bruzzone G. A., Massei M., 2007, Evaluation of the impact of different human factor models on industrial and business processes, Simulation Modelling Practice and Theory, 15, 199-218.
7. Cacciabue P. C., 1998, Modeling and Simulation of Human Behavior in System Control, Springer, Varese.
8. Liu A., Pentland A., 1999, Modeling and prediction of human behavior, Neural computation, 11(1), 229-242.
9. Embrey D., Understanding human behaviour and error, Human Reliability Associates 1, 2005; 1-10.
10. Macal C. M., North M. J., 2005, Tutorial on agentbased modeling and simulation, Proceedings of the 37th conference on Winter simulation. Winter Simulation Conference, 2-15.
11. Castle C. J. E., Crooks A. T., 2006, Principles and concepts of agent-based Modeling for developing geospatial simulations, CASA, London.
12. Bonabeau E., 2001, Agent-based modeling: methods and techniques for simulating human systems, Proc. National Academy of Sciences, 99(3), 7280-7287.
13. Deljoo A., Janssen M., Tan Y. H., 2013, The Role of Complex Systems in Public-Private Service Networks, Proceedings of the European Conference on Complex Systems 2012, 279-285.
14. Bazghandi, A., 2012, Techniques, advantages and problems of agent agent modeling for traffic simulation, Int J Comput Sci, 9(1), 115-119.
15. Elkosantini S., 2015, Toward a new generic behavior model for human centered system simulation, Simulation Modelling Practice and Theory, 52, 108-122.
16. Bruzzone A.G., Tremori A., Tarone F., Madeo F. (2011). Intelligent agents driving computer generated forces for simulating human behaviour in urban riots. International Journal of Simulation and Process Modelling. Vol. 6, Is. 4, 2011, pp. 308-316
17. Bruzzone, A.G., Frascio, M., Longo, F., Massei, M., Siri, A., Tremori, A. (2012). MARIA: An agent driven simulation for a web based serious game devoted to renew education processes in health care. Proceedings of the 1st International Workshop on Innovative Simulation for Health Care, IWISH 2012, pp. 188-194.
18. Bruzzone, A., Massei, M., Longo, F., Poggi, S., Agresta, M., Bartolucci, C., Nicoletti, L. (2014). Human behavior simulation for complex scenarios based on intelligent agents. Simulation Series, 46 (2), pp. 71-80.
19. Longo F., Nicoletti L., Vena S., Padovano A., (2014). Serious games at increased impact on culture and tourism. Proceedings of the 26th European Modeling and Simulation Symposium, EMSS 2014, pp. 641-648.
20. Longo, F., Nicoletti, L., Florio, G., Vetrano, M., Bruno, L., Caputi, L. (2015). Inside virtual: A new app for interactive and intelligent cultural heritage fruition. Proc. of the 27th European Modeling and Simulation Symp., EMSS 2015, pp. 471-478.
21. Gary An. In silico experiments of existing and hypothetical cytokine-directed clinical trials using agent-based modeling. Critical care medicine, 32(10):2050-2060, 2004.
22. Nuria Pelechano, Jan M Allbeck, and Norman I Badler. Controlling individual agents in high-density crowd simulation. In Proc. of the 2007 ACM SIG- GRAPH/Eurographics symp/ on Comp. animation, pp. 99-108. Eurographics Association, 2007.
23. Charles M Macal and Michael J North. Tutorial on agent-based modelling and simulation. Journal of simulation, 4(3): 151-162, 2010.
24. Igor Kotenko. Agent-based modeling and simulation of cyber-warfare between malefactors and security agents in internet. In Proceedings of 19th European Simulation Multiconference ’’Simulation in wider Europe”, 2005.
25. Igor Kotenko. Agent-based modelling and simulation of network cyber-attacks and cooperative defence mechanisms. Discrete Event Simulations, pages 223-246, 2010.
26. Dennis Grunewald, Marco Liitzenberger, Joel Chinnow, Rainer Bye, Karsten Bsufka, and Sahin Albayrak. Agent-based network security simulation. In Proceedings of The 10th International Conference on Autonomous Agents and Multiagent Systems- Volume 3, pages 1325-1326. International Foundation for Autonomous Agents and Multiagent Systems, 2011.
27. Holger R. M., MarkB. J., ShahinM. A., 2008, State of the art of 8artificial neural net-works in geotechnical engineering, Electronic Journal of Geotechnical Engineering, 8, 1-26.
28. Fausett L. V., 1994, Fundamentals neural networks: Architecture, algorithms, and applications, Prentice-Hall, Englewood Cliffs.
29. Schmidhuber J., 2015, Deep learning in neural networks: An overview, Neural Networks, 61, 85-117.
30. Abdel-Malek K., Arora J., Bataineh M., Marler T., 2016, Neural network for dynamic human motion prediction, Expert Systems with Applications, 48, 26-34.
31. Xu J. H., 2011 Application of Artificial Neural Network (ANN) for Prediction of Maritime Safety, Information and Management Engineering, Springer Berlin Heidelberg, 34-38.
32. Dey A.K., 2001, Understanding and using context, Personal Ubiquit. Comput, 5, 4-7.
33. Calvo-Florese M. D.,Cuellar M. P., Lilius J., Rodriguez N. D., 2014, A fuzzy ontology for semantic modelling and recognition of human behaviour, Knowledge-Based Systems, 66(0), 46-60.
34. Enam, S., Godil S., QidwaiU, ShamimM., 2011, Fuzzy logic: A "simple" solution for complexities in neurosciences?, Surgical neurology international.
35. Reusch B., 2006, Theory and Applications: International Conference 9th Fuzzy Days in Dortmund, 2006 Proceedings, 38, 18-20.
36. Cannon M. E., Sied S., 2004, Fuzzy logic-based map matching algorithm for vehicle navigation system in urban canyons, ION National Technical Meeting, San Diego, CA, p. 26-28.
37. Baines T., Mason S., Siebers P. O., Ladbrook J., 2004, Humans: the missing link in manufacturing simulation?, Simulation Modelling Practice and Theory, 12, 515-526.
38. Sugeno M., Yasukawa T., 1993, A Fuzzy-Logic-Based- Approch to Qualitative Modelling, IEE Transaction on Fuzzy Logic System, 1, vol. 1, 7-31.
39. De Pedro T., Garcia R., Gonzalez C., Naranjo J. E., Sotelo M. A., 2007, Using Fuzzy Logic in Automated Vehicle Control, IEEE Intelligent System, 22 (1), 36-45.
40. Acampora G., Foggia P., Saggese A., Vento V., 2015, A hierarchical neuro-fuzzy architecture for human behavior analysis, Information Sciences, 130, BOMS.
41. Acampora G., Foggia P., Saggese A., Vento V., 2012, Combining Neural Networks and Fuzzy Systems for Human Behavior Understanding, 2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance, 88-93.
42. Deepa S. N., Sivanandam S. N., 2007, Introduction to genetic algorithms, Springer Science & Business Media, New York, 34-35.
43. Tabassum M., Kuruvilla M., 2014, A genetic algorithm analysis towards optimization solutions, International Journal of Digital Information and Wireless Communications, IJDIWC, 4(1).
44. Kosorukoff A., 2001, Human based genetic algorithm, Systems, Man, and Cybernetics, 2001 IEEE International Conference, IEEE, 5, 3464-3469.
45. Luenberger D. G., 1979, Introduction to Dynamic Systems: Theory, Model and Applications, Stanford, John Wiley & Sons.
46. Liu A., Pentland A., 1999, Modeling and prediction of human behavior, Neural computation, 11(1), 229-242.
47. Dongyue H., Jixiang N., Qiang Z., Xiaoqing W., Zhiting L., Analyzing and modeling heterogeneous behavior, Physica A, 450, 2016; 287-293
48. Liu A., Pentland A., 1999, Modeling and prediction of human behavior, Neural computation, 11(1), 229-242.
49. Vladimir Gorodetski and Igor Kotenko. Attacks against computer network: formal grammar-based framework and simulation tool. In Proceedings of International Workshop on Recent Advances in Intrusion Detection, pages 219-238. Springer, 2002.
50. Blake Shepard, Cynthia Matuszek, C Bruce Fraser, William Wechtenhiser, David Crabbe, Zelal Gungordii, John Jantos, Todd Hughes, Larry Lefkowitz, Michael J Witbrock, et al. A knowledge-based approach to network security: applying eye in the domain of network risk assessment. In Proceedings of the National Conference on Artificial Intelligence, volume 20, page 1563. Menlo Park, CA; Cambridge, MA; London; AAAI Press; MIT Press; 1999, 2005.
51. Jordi Conesa, Veda C Storey, and Vijayan Sugumaran. Usability of upper level ontologies: The case of researchcyc. Data & Knowledge Engineering, 69(4):343-356, 2010.
52. Botia, J. A., Campuzano F., Garcia-Valverde T., Serrano, E., 2014, Generation of human computational models with knowledge engineering, Engineering Applications of Artificial Intelligence, 35, 259-276.
53. Oliver N., 2016, Data-driven Human Behavior Models: Opportunities and Challenges, Proceedings of the 4th Spanish Conference on Information Retrieval, ACM.
54. Abebe E, Almashor M., Beloglazov A., Richter J., Charles Barton Steer K. C. B, 2016, Simulation of wildfire evacuation with dynamic factors and model composition, Simulation Modelling Practice and Theory, 60, 144-159.
55. Sun, R. (2006). The CLARION cognitive architecture: Extending cognitive modeling to social simulation. In: Cognition and Multi-Agent Interaction: From Cognitive Modeling to Social Simulation (SUN, R., ed.). Cambridge University Press, pp. 79-99. http://www.cogsci.rpi.edu/~rsun/sun.clarion2005.pdf.
Опубліковано
2019-12-28
Як цитувати
Milov O. Засоби моделювання поведінки агентів в інформаційно-комунікаційних система / O. Milov, M. Kostyak, S. Milevsky, S. Pogasiy // Системи управління, навігації та зв’язку. Збірник наукових праць. – Полтава: ПНТУ, 2019. – Т. 6 (58). – С. 63-70. – doi:https://doi.org/10.26906/SUNZ.2019.6.063.
Розділ
Інформаційні технології
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