In the streets of Chinese cities, we often see that illegal pedlars sell some fake and inferior products such as outdated food and inferior household goods to people who do not know about this, which may cause serious health problem. Besides, pedlars often cause people to gather and so may lead to traffic accidents. Thus, there are great requirements how to control illegal pedlars, and how to analyze, model and predict illegal pedlars activities. Such research will help urban inspectors decide better strategies to guarantee public order. Thus, in this paper, we explore this problem, and propose a model called TALENTED (Target Attributes LEarNing model with TEmporal Dependence) to deal with the problem. TALENTED provides three main contributions. First, a new learning model is proposed to predict the probability of each target being attacked, and our model consists of three aspects: (i) This model considers a richer set of domain features; (ii) Adversaries’ previous behaviors affect their new actions; (iii) Each target has different attributes and the adversaries weight them differently. Second, we adopt a game-theoretic algorithm to compute the defender’s optimal strategy. Finally, simulation results illustrate the reasonability and validity of our new model.


Learning model Public order Stackelberg Security Game 



This paper is supported by Nature Science Foundation of China under grant No. 61572095.


  1. 1.
    Basilico, N., Gatti, N., Amigoni, F.: Leader-follower strategies for robotic patrolling in environments with arbitrary topologies. In: AAMAS (2009)Google Scholar
  2. 2.
    Ford, B., Brown, M., Yadav, A., Singh, A., Sinha, A., Srivastava, B., Kiekintveld, C., Tambe, M.: Protecting the NECTAR of the Ganga River through game-theoretic factory inspections. In: Demazeau, Y., Ito, T., Bajo, J., Escalona, M.J. (eds.) PAAMS 2016. LNCS (LNAI), vol. 9662, pp. 97–108. Springer, Cham (2016). Scholar
  3. 3.
    Yang, R., Ford, B., Tambe, M.: Adaptive resource allocation for wildlife protection against illegal poachers. In: Proceedings of the 2014 International Conference on Autonomous Agents and Multi-agent Systems, pp. 453–460. AAMAS (2014)Google Scholar
  4. 4.
    Nguyen, T.H., Sinha, A., Gholami, S.: CAPTURE: a new predictive anti-poaching tool for wildlife protection. In: Proceedings of the 2016 International Conference on Autonomous Agents and Multiagent Systems, pp. 767–775. AAMAS (2016)Google Scholar
  5. 5.
    Kar, D., Fang, F., Delle, F.F.: A game of thrones: when human behavior models compete in repeated Stackelberg security games. In: Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems, pp. 1381–1390. AAMAS (2015)Google Scholar
  6. 6.
    Kiekintveld, C., Jain, M., Tsai, J.: Computing optimal randomized resource allocations for massive security games. In: Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems, pp. 689–696. AAMAS (2009)Google Scholar
  7. 7.
    Carthy, S., Marie, M., Tambe, M.: Preventing illegal logging: simultaneous optimization of resource teams and tactics for security. In: AAAI Conference on Artificial Intelligence (2016)Google Scholar
  8. 8.
    Duan, K., Keerthi, S.S., Chu, W., Shevade, S.K., Poo, A.N.: Multi-category classification by Soft-Max combination of binary classifiers. In: Windeatt, T., Roli, F. (eds.) MCS 2003. LNCS, vol. 2709, pp. 125–134. Springer, Heidelberg (2003). Scholar
  9. 9.
    Gholami, S., Wilder, B., Brown, M., Sinha, A.: A game theoretic approach on addressing collusion among human adversaries. In: Proceedings of the 2016 International Conference on Autonomous Agents and Multiagent Systems (2016)Google Scholar
  10. 10.
    Haskell, W.B., Kar, D., Fang, F.: Robust protection of fisheries with compass. In: AAAI, pp. 2978–2983 (2014)Google Scholar
  11. 11.
    Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Springer Series in Statistics. Springer, Berlin (2001)CrossRefzbMATHGoogle Scholar
  12. 12.
    Yang, R., Ordonez, F., Tambe, M.: Computing optimal strategy against quantal response in security games. In: Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems, pp. 847–854. AAMAS (2012)Google Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018

Authors and Affiliations

  1. 1.School of Software TechnologyDalian University of TechnologyDalianChina

Personalised recommendations