By Chengfei Wang, Zhaohui Zhang, Runping Xu, Ming Li (auth.), Zhenhua Li, Xiang Li, Yong Liu, Zhihua Cai (eds.)
This publication constitutes the refereed complaints of the sixth overseas Symposium on Intelligence Computation and purposes, ISICA 2012, held in Wuhan, China, in October 2012. The seventy two revised complete papers offered have been conscientiously reviewed and chosen from various submissions. The papers are geared up in topical sections on synthetic lifestyles, adaptive habit, brokers, and ant colony optimization; combinatorial and numerical optimization; communications and laptop networks; information mining; evolutionary multi-objective and dynamic optimization; clever computation, clever studying platforms; neural networks; real-world applications.
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Additional info for Computational Intelligence and Intelligent Systems: 6th International Symposium, ISICA 2012, Wuhan, China, October 27-28, 2012. Proceedings
Simulation cost consequently decreases. Since there is no other new change for the simulation, the simulation cost keeps stable for the remain simulation time. 6 Conclusion In this paper, an agent-based model is proposed for simulating human-like crowd’s movement in dense places. Aiming at the behaviour and movement of each pedestrian in crowd, the model is able to produce a low detail-of-level simulation results. In each simulation time step, agent needs to select a position as its goal and subsequently determine its preferred moving direction.
In: Proceedings of Theory and Practice of Computer Graphics 2005, pp. 35–42 (2005) An Agent-Based Model for Simulating Human-Like Crowd in Dense Places 19 22. : Towards a cognitive model of crowd behavior based on social comparison theory. In: Proceedings of the Twenty-Second AAAI Conference on Artiﬁcial Intelligence, Vancouver, British Columbia, Canada, pp. 731–737 (July 2007) 23. : Theory and applications of Cellular Automata. World Press (1986) 24. : Simulation of evacuation processes using a bionics-inspired cellular automaton model for pedestrian dynamics.
Ants use their experience to update the state space pheromone distribution. As in CACS, a Gaussian function is utilized to model the pheromone distribution over the continuous state space. Ants use this pheromone distribution to move from their current position toward the minimum cost destinations. The destinations are chosen using a normal PDF. The inner loop is terminated after a predeﬁned number of iterations. Finally, the current state estimation is made using a mean operator. In the following subsections, these steps are discussed in detail.