Skip to main content

The Impact of the Cost Function on the Operation of the Intelligent Agent in 2D Games

  • Conference paper
  • First Online:
Information and Software Technologies (ICIST 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 920))

Included in the following conference series:

Abstract

A large part of the technology development depends on the needs of users. Apart from the hardware requirements for programs used by large companies or smaller groups, the wide applications and hardware load are games and graphics. Increasing the quality of games by improving their story quality requires a lot of more efficient and effective algorithms. In this work, we propose the use of a hybrid approach to the management of opponents’ movements on the classic two-dimensional game called the Tron. Our solution is based on the use of the idea of a simulated annealing algorithm in order to select the agent’s movement technique depending on the cost function. The algorithm has been implemented and tested depending on the used parameters. Obtained results were discussed depending on the advantages and disadvantages of using this type of solution in more complex games.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Chodarev, S., Bacíková, M.: Development of Oberon-0 using YAJCo. In: 2017 IEEE 14th International Scientific Conference on Informatics, pp. 122–127. IEEE (2017)

    Google Scholar 

  2. Beritelli, F., Capizzi, G., Sciuto, G.L., Napoli, C., Scaglione, F.: Automatic heart activity diagnosis based on gram polynomials and probabilistic neural networks. Biomed. Eng. Lett. 8(1), 77–85 (2018)

    Article  Google Scholar 

  3. Włodarczyk-Sielicka, M., Wawrzyniak, N.: Problem of bathymetric big data interpolation for inland mobile navigation system. In: Damaševičius, R., Mikašytė, V. (eds.) ICIST 2017. CCIS, vol. 756, pp. 611–621. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67642-5_51

    Chapter  Google Scholar 

  4. Marszałek, Z.: Parallelization of modified merge sort algorithm. Symmetry 9(9), 176 (2017)

    Article  Google Scholar 

  5. Gabryel, M.: A bag-of-features algorithm for applications using a NoSQL database. In: Dregvaite, G., Damasevicius, R. (eds.) ICIST 2016. CCIS, vol. 639, pp. 332–343. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46254-7_26

    Chapter  Google Scholar 

  6. Rohlfshagen, P., Liu, J., Perez-Liebana, D., Lucas, S.M.: Pac-Man conquers academia: two decades of research using a classic arcade game. IEEE Trans. Games (2017)

    Google Scholar 

  7. Ye, M., Hu, G.: Game design and analysis for price-based demand response: an aggregate game approach. IEEE Trans. Cybern. 47(3), 720–730 (2017)

    Article  Google Scholar 

  8. Khalifa, A., Green, M.C., Perez-Liebana, D., Togelius, J.: General video game rule generation. In: 2017 IEEE Conference on Computational Intelligence and Games (CIG), pp. 170–177. IEEE (2017)

    Google Scholar 

  9. Grossi, G., Ross, B.: Evolved communication strategies and emergent behaviour of multi-agents in pursuit domains. In: 2017 IEEE Conference on Computational Intelligence and Games (CIG), pp. 110–117. IEEE (2017)

    Google Scholar 

  10. Esmaeili, A., Mozayani, N., Motlagh, M.R.J., Matson, E.T.: A socially-based distributed self-organizing algorithm for holonic multiagent systems: case study in a task environment. Cogn. Syst. Res. 43, 21–44 (2017)

    Article  Google Scholar 

  11. Kunanusont, K., Lucas, S.M., Pérez-Liébana, D.: General video game AI: learning from screen capture. In: 2017 IEEE Congress on Evolutionary Computation (CEC), pp. 2078–2085. IEEE (2017)

    Google Scholar 

  12. Ašeriškis, D., Damaševicius, R.: Gamification patterns for gamification applications. Procedia Comput. Sci. 39, 83–90 (2014)

    Article  Google Scholar 

  13. Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgments

Authors acknowledge contribution to this project of the “Diamond Grant 2016” No. 0080/DIA/2016/45 from the Polish Ministry of Science and Higher Education and the Rector pro-quality grant No. 09/010/RGJ18/0033 at the Silesian University of Technology, Poland.

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Dawid Połap or Marcin Woźniak .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Połap, D., Woźniak, M. (2018). The Impact of the Cost Function on the Operation of the Intelligent Agent in 2D Games. In: Damaševičius, R., Vasiljevienė, G. (eds) Information and Software Technologies. ICIST 2018. Communications in Computer and Information Science, vol 920. Springer, Cham. https://doi.org/10.1007/978-3-319-99972-2_23

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-99972-2_23

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-99971-5

  • Online ISBN: 978-3-319-99972-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics