Abstract
This paper proposes a new approach for a crowd navigation in evacuation which is a good example of a complex system. The mathematical concepts such as differential equations is used in simple system but when the complexity increases, these concepts become less effective. The processing of complex systems such as crowd navigation requires the manipulation of vague, imprecise, uncertain, or both imprecise and uncertain information. The proposed approach use path finding for static obstacle and perception of the environment with spatial behaviour for dynamic obstacle, which affects their travel speeds and emerging appeared phenomena. Our system is modelled by agent and tested by a processing simulation, several modules such as A* planning, physical factors of agents have been programmed with java and successfully inserted into a system. The paper presents many simulations for evacuation of crowd behaviour and emergence of the crowd. Experimental results show how our agents can plan the best path with any collision in an unknown environment. Our application can be used as a framework to simulate real situation of complex system.
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References
Abdelhak, H., Ayesh, A., Olivier, D.: Cognitive emotional based architecture for crowd simulation. J. Intell. Comput. 3(2), 55–66 (2012)
Chenney, S.: Flow tiles. In: Proceedings of the 2004 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, pp. 233–242. Eurographics Association (2004)
Chennoufi, M., Bendella, F., Bouzid, M.: Multi-agent simulation collision avoidance of complex system: application to evacuation crowd behavior. Int. J. Ambient Comput. Intell. (IJACI) 9(1), 43–59 (2018)
Costa, J., Paul, T.: Toward a new generation of personality theories: theoretical contexts for the five-factor model. In: The Five-Factor Model of Personality: Theoretical Perspectives, vol. 51 (1996)
Couture, M., Valcartier, D.: Complexity and Chaos-State-of-the-Art; Overview of Theoretical Concepts. Minister of National Defence, Canada (2007)
Deepak, D., John, S.J.: Information systems on hesitant fuzzy sets. Int. J. Rough Sets Data Anal. (IJRSDA) 3(1), 71–97 (2016)
El-Hadidy, M.A.A., El-Bagoury, A.A.A.H.: Optimal search strategy for a three-dimensional randomly located target. Int. J. Oper. Res. 29(1), 115–126 (2017)
Glorennec, P.Y.: Algorithmes d’apprentissage pour systèmes d’inférence floue. Hermes (1999)
Hart, P.E., Nflsson, N., Raphael, B.: A formal bas# s for heunsuc deterrmnatmn of mmmmm path cost. IEEE Trans. SSC 4, 100–107 (1968)
Hassas, S.: Systèmes Complexes à Base de Multi-agents Situés. University Claude Bernard Lyon (2003)
He, G.G., Yang, Y., Chen, Z.H., Gu, C.H., Pan, Z.G.: A review of behavior mechanisms and crowd evacuation animation in emergency exercises. J. Zhejiang Univ. Sci. C 14(7), 477–485 (2013)
Helbing, D., Farkas, I., Vicsek, T.: Simulating dynamical features of escape panic. Nature 407(6803), 487 (2000)
Helbing, D., Farkas, I.J., Molnar, P., Vicsek, T.: Simulation of pedestrian crowds in normal and evacuation situations. Pedestr. Evacuation Dyn. 21(2), 21–58 (2002)
Ji, Q., Gao, C.: Simulating crowd evacuation with a leader-follower model. IJCSES 1(4), 249–252 (2006)
Lin, C.H., Chen, P.Y., Chen, W.T.: An adaptive guiding protocol for crowd evacuation based on wireless sensor networks. In: 2013 IEEE 77th Vehicular Technology Conference (VTC Spring), pp. 1–5. IEEE (2013)
Lu, D., Zhang, G., Liu, Y., Wang, D., Liu, H.: AFSA based path planning method for crowd evacuation. J. Inform. Comput. Sci. 11(11), 3815–3823 (2014)
Olya, M.H.: Applying Dijkstra’s algorithm for general shortest path problem with normal probability distribution arc length. Int. J. Oper. Res. 21(2), 143–154 (2014)
Olya, M.H.: Finding shortest path in a combined exponential-gamma probability distribution arc length. Int. J. Oper. Res. 21(1), 25–37 (2014)
Ortony, A., Clore, G.L., Collins, A.: The Cognitive Structure of Emotions. Cambridge University Press (1990)
Pillac, V., Van Hentenryck, P., Even, C.: A conflict-based path-generation heuristic for evacuation planning. Trans. Res. Part B: Methodol. 83, 136–150 (2016)
Reynolds, C.W.: Flocks, herds and schools: a distributed behavioral model. In: ACM SIGGRAPH Computer Graphics, vol. 21, pp. 25–34. ACM (1987)
Samah, K., Hussin, B., Basari, A.S.H.: Modification of Dijkstra’s algorithm for safest and shortest path during emergency evacuation. Appl. Math. Sci. 9(31), 1531–1541 (2015)
Sarkar, M., Banerjee, S., Hassanien, A.E.: Evaluating the degree of trust under context sensitive relational database hierarchy using hybrid intelligent approach. Int. J. Rough Sets Data Anal. (IJRSDA) 2(1), 1–21 (2015)
Schadschneider, A.: Cellular automaton approach to pedestrian dynamics-theory (2001). arXiv:cond-mat/0112117
Shao, W., Terzopoulos, D.: Autonomous pedestrians. Graph. Models 69(5–6), 246–274 (2007)
Sun, X., Koenig, S.: The fringe-saving A* search algorithm-a feasibility study. IJCAI 7, 2391–2397 (2007)
Zimmermann, H.J.: Fuzzy Set Theory—and Its Applications (1991)
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Chennoufi, M., Bendella, F. (2021). The Emergence and Decision Support in Complex System with Fuzzy Logic Control. In: Masrour, T., Cherrafi, A., El Hassani, I. (eds) Artificial Intelligence and Industrial Applications. A2IA 2020. Advances in Intelligent Systems and Computing, vol 1193. Springer, Cham. https://doi.org/10.1007/978-3-030-51186-9_13
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