A Learning Invader for the “Guarding a Territory” Game

1. Berenji, H.: Fuzzy q-learning: a new approach for fuzzy dynamic programming. In: Proceedings of the Third IEEE Conference on Fuzzy Systems, 1994. IEEE World Congress on Computational Intelligence, vol. 1, pp 486–491 (1994). doi:10.1109/FUZZY.1994.343737

2. Desouky, S., Schwartz, H.: A novel hybrid learning technique applied to a self-learning multi-robot system. In: IEEE International Conference on Systems, Man and Cybernetics, 2009. SMC 2009, pp. 2616–2623 (2009). doi:10.1109/ICSMC.2009.5346111

3. Er, M.J., San, L.: Automatic generation of fuzzy inference systems using incremental-topological-preserving-map-based fuzzy q-learning. In: IEEE International Conference on Fuzzy Systems, 2008. FUZZ-IEEE 2008. (IEEE World Congress on Computational Intelligence), pp. 467–474 (2008). doi:10.1109/FUZZY.2008.4630410

4. Fang, M., Li, H., Zhang, X.: A heuristic reinforcement learning based on state backtracking method. In: 2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology (WI-IAT), vol. 1, pp. 673–678 (2012). doi:10.1109/WI-IAT.2012.187

5. Givigi, S., Schwartz, H.M.: Decentralized strategy selection with learning automata for multiple pursuer-evader games. Adapt. Behav. 22(4), 221–234 (2014). doi:10.1177/1059712314526261. http://adb.sagepub.com/content/22/4/221.abstract

   Article  Google Scholar 

6. Isaacs, R.: Differential Games: A Mathematical Theory with Applications to Warfare and Pursuit, Control and Optimization (1999)

7. Lauri, F., Koukam, A.: Robust multi-agent patrolling strategies using reinforcement learning. In: Siarry, P., Idoumghar, L., Lepagnot, J. (eds.) Swarm Intelligence Based Optimization, Lecture Notes in Computer Science, vol. 8472, pp. 157–165. Springer International Publishing (2014)

8. Lee, Y.S., Hsia, K.H., Hsieh, J.G.: A problem of guarding a territory with two invaders and two defenders. In: 1999 IEEE International Conference on Systems, Man, and Cybernetics, 1999. IEEE SMC ’99 Conference Proceedings, vol. 3, pp. 863–868 (1999). doi:10.1109/ICSMC.1999.823341

9. Liu, J., Liu, S., Wu, H., Zhang, Y.: A pursuit-evasion algorithm based on hierarchical reinforcement learning. In: International Conference on Measuring Technology and Mechatronics Automation, 2009. ICMTMA ’09, vol. 2, pp. 482–486 (2009). doi:10.1109/ICMTMA.2009.213

10. Nguyen, H.T., Walker, E.: A first course in fuzzy logic. Chapman and Hall, Boca Raton (2006). www.summon.com

11. Rzymowski, W.: A problem of guarding line segment. In: Proceedings of the 48th IEEE Conference on Decision and Control, 2009 held jointly with the 2009 28th Chinese Control Conference. CDC/CCC 2009, pp. 6444–6447 (2009). doi:10.1109/CDC.2009.5400251

12. Schwartz, H.: Multi-Agent Machine Learning: A Reinforcement Approach. Wiley (2014)

13. Siciliano, B., Sciavicco, L., Villani, L., Oriolo, G.: Robotics Modelling, Planning and Control. Springer (2009)

14. Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. Syst. Man Cybern. SMC-15(1), 116–132 (1985). doi:10.1109/TSMC.1985.6313399

    Article  MATH  Google Scholar 

15. Wang, L.: A Course in Fuzzy Systems and Control. Prentice Hall PTR (1997)

16. Wang, S., Panzica, A., Padir, T.: Motion control for intelligent ground vehicles based on the selection of paths using fuzzy inference. In: 2013 IEEE International Conference on Technologies for Practical Robot Applications (TePRA), pp. 1–6 (2013). doi:10.1109/TePRA.2013.6556354

Download references