Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
A Salient Object Detection Algorithm Based on Region Merging and Clustering Read chapter Read first chapter

2020 | OriginalPaper | Chapter

Adaptive Game AI-Based Dynamic Difficulty Scaling via the Symbiotic Game Agent

Authors : Siphesihle Philezwini Sithungu, Elizabeth Marie Ehlers

Published in: Intelligent Information Processing X

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

This work presents AdaptiveSGA, a model for implementing Dynamic Difficulty Scaling through Adaptive Game AI via the Symbiotic Game Agent framework. The use of Dynamic Difficulty Balancing in modern computer games is useful when looking to improve the entertainment value of a game. Moreover, the Symbiotic Game Agent, as a framework, provides flexibility and robustness as a design principle for game agents. The work presented here leverages both the advantages of Adaptive Game AI and Symbiotic Game Agents to implement a robust, efficient and testable model for game difficulty scaling. The model is discussed in detail and is compared to the original Symbiotic Game Agent architecture. Finally, the paper describes how it was applied in simulated soccer. Finally, experimental results, which show that Dynamic Difficulty Balancing was achieved, are briefly analyzed.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Similarity Evaluation with Wikipedia Features
next chapter Scientific Paper Recommendation Using Author’s Dual Role Citation Relationship
Literature
1.
Tan, C.H., Tan, K.C., Tay, A.: Dynamic game difficulty scaling using adaptive behavior-based AI. IEEE Trans. Comput. Intell. AI Games 3, 289–301 (2011)CrossRef
2.
Cunha, E.M.C.A.M.: An Adaptive Game AI Architecture
3.
4.
Spronck, P., Sprinkhuizen-Kuyper, I., Postma, E.: Difficulty scaling of game AI. In: Proceedings of the 5th International Conference on Intelligent Games and Simulation (GAME-ON 2004) (2004)
5.
Gomez-Hicks, G., Kauchak, D.: Dynamic game difficulty balancing for backgammon. In: Proceedings of the 49th Annual Southeast Regional Conference (2011)
6.
Andrade, G., Ramalho, G., Gomes, A.S., Corruble, V.: Dynamic game balancing: an evaluation of user satisfaction. AIIDE 6, 3–8 (2006)
7.
Hawkins, G., Nesbitt, K., Brown, S.: Dynamic difficulty balancing for cautious players and risk takers. Int. J. Comput. Games Technol. 2012, 3 (2012)CrossRef
8.
9.
Andrade, G., Ramalho, G., Santana, H., Corruble, V.: Extending reinforcement learning to provide dynamic game balancing. In: Proceedings of the Workshop on Reasoning, Representation, and Learning in Computer Games, 19th International Joint Conference on Artificial Intelligence (IJCAI) (2005)
10.
Nareyek, A.: Intelligent agents for computer games. In: International Conference on Computers and Games (2000)
11.
12.
Douglas, A.E.: The Symbiotic Habit. Princeton University Press, Princeton (2010)
13.
Obodoekwe, C., Coulter, D.A., Ehlers, E.M.: A model for improving serious game player immersion via symbiotic agents, natural language processing, and facial expression analysis. In: International Conference on Computer Games, Multimedia & Allied Technology (CGAT) (2017)
Metadata
Title
Adaptive Game AI-Based Dynamic Difficulty Scaling via the Symbiotic Game Agent
Authors
Siphesihle Philezwini Sithungu
Elizabeth Marie Ehlers
Copyright Year
2020
Book
Intelligent Information Processing X

Print ISBN: 978-3-030-46930-6

Electronic ISBN: 978-3-030-46931-3

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-46931-3_11

Premium Partner

    Image Credits