Advertisement

Intelligent Pathfinding Algorithm in Web Games

  • Hailong HuangEmail author
Conference paper
  • 30 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1146)

Abstract

In recent years, with the hardware development of the web game is getting better and better, the game graphics computing ratio decreased a lot, the traditional scene and picture effect obtained great development at the same time, players began to pay attention to the content of a video game, hope the game role behavior can more close to real people, which makes the game developers in the artificial intelligence to enhance investment in the game, and in game ai, the role of intelligent pathfinding system is always the key point of the research. Intelligent pathfinding algorithm plays A leading role in intelligent pathfinding system. At present, the most classic intelligent pathfinding algorithm used in video games is A* (A-Star) algorithm. Many other excellent pathfinding algorithms are also developed from this algorithm. The purpose of this paper is to get rid of the limitation of having to use standard A* algorithm functions to guide pathfinding so as to quickly find A shortest path in the map. In this paper, through A detailed study of the standard A* algorithm, several optimization methods are proposed to improve it. Firstly, heuristic coefficients are added to the heuristic function of A* algorithm to optimize the search for redundant paths in pathfinding process. Binary heap is used to maintain the open list in A* algorithm. Then, the path weight in the map is optimized. The generated path is smoothed by “take the median insertion method”, and the A* algorithm is further optimized by the zonal search method, so that it can be applied to A wider range of game map types. Finally, through the test of a simulation experiment program produced by Unity3D engine, the above methods are verified. The results of this paper show that after improving the standard intelligent pathfinding algorithm, pathfinding efficiency is higher, the range of use is wider, the player experience is better, and it can be applied in the game.

Keywords

Web games Intelligent pathfinding A-Star algorithm Path weight 

References

  1. 1.
    Zulhemay, M.N., Zaharan, M.Z., Zaini, H.: The conceptual foundation of engaging game design in learning solar system. Adv. Sci. Lett. 427(35), 1235–1238 (2018)Google Scholar
  2. 2.
    Zabaleta, O.G., Barrangú, J.P., Arizmendi, C.M.: Quantum game application to spectrum scarcity problems. Physica A 466(262), 455 (2017)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Feng, J.-Z., Zhang, J.-G., Qiu, J.-W.: Game algorithm of multi-objective reliability design optimization based on simulating animal behavior. Trans. Beijing Inst. Technol. 38(5), 449–453 (2018)MathSciNetGoogle Scholar
  4. 4.
    Peizhe, L., Muqing, W., Wenxing, L.: A game-theoretic and energy-efficient algorithm in an improved software-defined wireless sensor network. IEEE Access 47(9), 16–17 (2017)Google Scholar
  5. 5.
    Liu, Y., Chen, C.S., Sung, C.W.: A game theoretic distributed algorithm for FeICIC optimization in LTE-A HetNets. IEEE/ACM Trans. Netw. 437(45), 11–14 (2017)Google Scholar
  6. 6.
    Wu, J., Shen, X., Jiao, K.: Game-based memetic algorithm to the vertex cover of networks. IEEE Trans. Cybern. 534(36), 13–15 (2018)Google Scholar
  7. 7.
    Hong, Z.F., Chu, C.B., Zhang, L.D.L.: Optimizing an emission trading scheme for local governments: a Stackelberg game model and hybrid algorithm. Int. J. Prod. Econ. 193(4), 172–182 (2017)CrossRefGoogle Scholar
  8. 8.
    Liang, L., Lu, W., Tornatore, M.: Game-assisted distributed decision making to build virtual TDM-PONs in C-RANs adaptively. IEEE/OSA J. Opt. Commun. Netw. 9(7), 6–8 (2017)CrossRefGoogle Scholar
  9. 9.
    De Assis, M.V.O., Hamamoto, A.H., Abrão, T.: A game theoretical based system using holt-winters and genetic algorithm with fuzzy logic for DoS/DDoS mitigation on SDN networks. IEEE Access 5(99), 9485–9496 (2017)CrossRefGoogle Scholar
  10. 10.
    Han, S., Li, X.-B., Ma, K.: Improved game-theoretic algorithm for spectrum sharing in cognitive radio. Trans. Beijing Inst. Technol. 37(7), 758–764 (2017)Google Scholar
  11. 11.
    Subba, B., Biswas, S., Karmakar, S.: A game theory based multi layered intrusion detection framework for VANET. Future Gener. Comput. Syst. 82(13), 12–28 (2017)Google Scholar
  12. 12.
    Wang, H.-J., Qiu, Z., Dong, R.-S.: Energy balanced and self adaptation topology control game algorithm for wireless sensor networks. Kongzhi yu Juece/Control Decis. 34(1), 72–80 (2019)zbMATHGoogle Scholar
  13. 13.
    Elhattab, M.K., Elmesalawy, M.M., Ismail, T.: A matching game for device association and resource allocation in heterogeneous cloud radio access network. IEEE Commun. Lett. 564(46), 41–47 (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Guangzhou Vocational and Technical University of Science and TechnologyGuangzhouChina

Personalised recommendations