Agent-Cellular Automata

  • Xuewei Li
  • Jinpei Wu
  • Xueyan Li


In the previous two chapters (“Cellular Genetic Algorithm” and “Cellular Neural Networks”), we have seen that self-adaptive individuals distributed within the cellular lattice play roles of genes, chromosomes, and neurons respectively. The ability of learning among individual cells is strengthened, and at the same time the function of traditional artificial intelligence algorithm is realized. Next, we might as well consider this question—when individuals in the cellular space have different rules of evolution and learning, or they can move by themselves, or individual evolution rules change with time, can we give these individuals human characteristics such as emotions, beliefs, roles, consciousness, etc., and eventually make the space of cellular automata form a “real world” participated in by intelligent agents?


  1. Bauer, A., Beauchemin, C. A., et al. (2009). Agent-based modeling of host-pathogen systems: The successes and challenges. Information Sciences, 179(10), 1379–1389.CrossRefGoogle Scholar
  2. Bin, J., Gao, Z., Li, K., et al. (2007). Modeling and simulation of traffic system based on cellular automata. Beijing: Science Press.Google Scholar
  3. Binghan, J., & Bingham, B. (2007). Hybrid one-dimensional reversible cellular automata are regular. Discrete Applied Mathematics,155(18), 2555–2566.CrossRefGoogle Scholar
  4. Li, W., Packard, N. H., & Langton, C. (1990). Transition phenomena in cellular automata rule space. Physica D: Nonlinear Phenomena, 45(1–3), 77–94.CrossRefGoogle Scholar
  5. Liu, C., & Sun, S. (2010). Comparative study of agent and CA method, with agent simulating the system behaviors. Practice and Understanding of Mathematics, 40(2), 164–169.Google Scholar
  6. Mitchell Waldrop, M. (1997). Complexity: The emerging science at the edge of order and chaos (C. Ling, Trans). Beijing: SDX Joint Publishing Company.Google Scholar
  7. Ni, J. (2011). Theory and application of complex system multi-agent modeling and control. Beijing: Electronic Industry Press.Google Scholar
  8. Podrouzek, J. (2009). Stochastic cellular automata in dynamic environmental modeling: Practical applications. Electronic notes in Theoretical Computer Science, 252(1), 143–156.CrossRefGoogle Scholar
  9. Porter, M. E. (2007). The competitive advantage of nations (M. Li & R. Qiu, Trans). Beijing: CITIC Press.Google Scholar
  10. Shi, C., & Wei, Z. (2007). Agent based computing. Beijing: Tsinghua University Press.Google Scholar
  11. Wu, J., & Bin, Hu. (2009). Simulation of multi-agent entities with interactions between informationization and group behavior. System Engineering Journal, 24(2), 218–225.Google Scholar
  12. Wu, X., Yuan, Z., Li, Y., et al. (2008). A single-lane double probability cellular automaton model. Beijing Jiaotong University Journal, 32(6), 42–46.Google Scholar
  13. Yin, F. (2011). Study of the theoretical model and development method of agent. Electronic Design Engineering, 19(10), 63–66.Google Scholar

Copyright information

© Beijing Jiaotong University Press and Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Beijing Union UniversityBeijingChina
  2. 2.Wuyi UniversityJiangmenChina
  3. 3.Beijing Jiaotong universityBeijingChina

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