Analyzing Police Patrol Routes by Simulating the Physical Reorganization of Agents

  • Adriano Melo
  • Mairon Belchior
  • Vasco Furtado
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3891)


In this article we describe a tool for assisting the investigation of different strategies for the physical reorganization of agents. We show how the tool was used in the public safety domain to help in the study of strategies of preventive policing. A society of agents that simulates criminal and police behavior in a geographical region was constructed. In this society, artificial agents representing the police are responsible for preventing crimes. The organizational structure of the police is characterized by the existence of a centralized command that has the task of distributing and redistributing the police force in a region according to an analysis on crime and the factors that influence it. The simulation of different strategies of physical reorganization is a first step to better understand the influence that different police patrol routes have on the reduction of crime rates.


Crime Rate MultiAgent System Police Force Artificial Agent Agent Society 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Almeida, A., Ramalho, G.L., Santana, H.P., Tedesco, P., Menezes, T.R., Corruble, V., Chevaleyre, Y.: Recent Advances on Multi-Agent Patrolling. In: Bazzan, A.L.C., Labidi, S. (eds.) SBIA 2004. LNCS (LNAI), vol. 3171, Springer, Heidelberg (2004)Google Scholar
  2. 2.
    Becker, G.: Crime and Punishment: An Economic Approach. The Journal of Political Economy 76, 169–217 (1968)CrossRefGoogle Scholar
  3. 3.
    Benenson, I., Torrens, P.M.: Geosimulation: object-based modeling of urban phenomena. Computers, Environment and Urban Systems 28(1/2), 1–8 (2004)CrossRefGoogle Scholar
  4. 4.
    Carley, K., Gasser, L.: Computational organization theory. In: Weiss, G. (ed.) Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence, pp. 299–330. MIT Press, Cambridge (1999)Google Scholar
  5. 5.
    Cohen, L., Felson, M.: Social change and crime rate trends: a routine approach. American Sociological Review 44, 588–608 (1979)CrossRefGoogle Scholar
  6. 6.
    Collier, N. Repast: An extensible framework for agent simulation (2003),
  7. 7.
    Dignum, V., Dignum, F., Sonenberg, L.: Towards dynamic organization of agent societies. In: Vouros, G. (ed.) Workshop on Coordination in Emergent Agent Societies, ECAI 2004, pp. 70–78 (2004)Google Scholar
  8. 8.
    Dignum, V., Dignum, F., Furtado, V., Melo, A., Sonenberg, L.: Towards a Simulation Tool for Evaluating Dynamic Reorganization of Agents Societies. In: Workshop on Socially Inspired Computing. AISB Convention (2005)Google Scholar
  9. 9.
    Hannebauer, M.: Autonomous Dynamic Reconfiguration in Multi¬Agent Systems. In: Hannebauer, M. (ed.) Autonomous Dynamic Reconfiguration in Multi-Agent Systems. LNCS (LNAI), vol. 2427, Springer, Heidelberg (2002)CrossRefGoogle Scholar
  10. 10.
    Furtado, V., Vasconcelos, E.: A Multi-Agent Simulator for Teaching Police Allocation. In: Proceedings of the 17th Innovative Applications of Artificial Intelligence, IAAI 2005, Pittsburgh, USA (July 2005)Google Scholar
  11. 11.
    Garcia, A.C.B., Sichman, J.S.: Agentes e sistemas multiagentes. In: Sistemas Inteligentes: Fundamentos e Aplicaçoes, S. O. Rezende, Ed. Editora Manole Ltda., Barueri, Sao Paulo, Brazil (2003)Google Scholar
  12. 12.
    Gutin, G., Punnen, A.P.: The Traveling Salesman Problem and Its Variations, Series Combinatorial Optimization, vol. 12. Springer, Heidelberg (2002)MATHGoogle Scholar
  13. 13.
    Valetto, G., Kaiser, G., Gaurav Kc, S.: A mobile agent approach to process based dynamic adaptation of complex software systems. In: 8th European Workshop on Software Process Technology, pp. 102–116 (2001)Google Scholar
  14. 14.
    Winoto, P.: A Simulation of the Market of the Offenses in Multiagent Systems. Is zero Crimes Attainable? Multi-Agent-Based Simulation II, Bologna, Italy (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Adriano Melo
    • 1
  • Mairon Belchior
    • 1
  • Vasco Furtado
    • 1
  1. 1.Master of Informatics (MIA)UNIFOR – University of FortalezaEdson Queiroz, FortalezaBrazil

Personalised recommendations