Article

Automatic computer game balancing: a reinforcement learning approach

Authors:

Gustavo Andrade,

Geber Ramalho,

Hugo Santana,

Vincent CorrubleAuthors Info & Claims

AAMAS '05: Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems

Pages 1111 - 1112

https://doi.org/10.1145/1082473.1082648

Published: 25 July 2005 Publication History

Get Access

Abstract

Designing agents whose behavior challenges human players adequately is a key issue in computer games development. This work presents a novel technique, based on reinforcement learning (RL), to automatically control the game level, adapting it to the human player skills in order to guarantee a good game balance. RL has commonly been used in competitive environments, in which the agent must perform as well as possible to beat its opponent. The innovative use of RL proposed here makes use of a challenge function, which estimates the current player's level, as well as changes on the action selection mechanism of the RL framework. The technique is applied to a fighting game, Knock'em, to provide empirical validation of the approach.

References

[1]

Falstein, N., The Flow Channel, Game Developer Magazine, May Issue, 2004.

Google Scholar

[2]

Koster, R., Theory of Fun for Game Design, Paraglyph Press, Phoenix, 2004.

Digital Library

Google Scholar

[3]

Sutton, R., Barto A., Reinforcement Learning: An Introduction, Cambridge, MA, 1998.

Digital Library

Google Scholar

Cited By

View all

Karavidas LApostolidis HTsiatsos T(2022)Usability Evaluation of an Adaptive Serious Game Prototype Based on Affective FeedbackInformation10.3390/info1309042513:9(425)Online publication date: 8-Sep-2022
https://doi.org/10.3390/info13090425
Liu GCai MZhao LQin TBrown ABischoff JLiu T(2022)Inspector: Pixel-Based Automated Game Testing via Exploration, Detection, and Investigation2022 IEEE Conference on Games (CoG)10.1109/CoG51982.2022.9893630(237-244)Online publication date: 21-Aug-2022
https://dl.acm.org/doi/10.1109/CoG51982.2022.9893630
Kavanagh WMiller ANorman GAndrei O(2021)Balancing Turn-Based Games With Chained Strategy GenerationIEEE Transactions on Games10.1109/TG.2019.294322713:2(113-122)Online publication date: Jun-2021
https://doi.org/10.1109/TG.2019.2943227
Show More Cited By

Index Terms

Recommendations

Design Strategies for Balancing Exertion Games: A Study of Three Approaches
DIS '16: Proceedings of the 2016 ACM Conference on Designing Interactive Systems

In sports, if players' physical and technical abilities are mismatched, the competition is often uninteresting for them. With the emergence of exertion games, this could be changing. Player balancing, known from video games, allows players with ...
Towards understanding balancing in exertion games
ACE '14: Proceedings of the 11th Conference on Advances in Computer Entertainment Technology

Playing exertion games with others can be engaging. However, players with different physical skill levels competing against each other can experience reduced engagement because they are either not challenged enough, or challenged too much. Balancing ...
How Game Balancing Affects Play: Player Adaptation in an Exergame for Children with Cerebral Palsy
DIS '17: Proceedings of the 2017 Conference on Designing Interactive Systems

Player balancing helps people with different levels of physical ability and experience play together by providing customized assistance. Player balancing is particularly important in exergames, where differences in physical ability can have a large ...

Comments

Information & Contributors

Information

Published In

AAMAS '05: Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems

July 2005

1407 pages

ISBN:1595930930

DOI:10.1145/1082473

Program Chairs:
Michal Pechoucek
Czech Republic
,
Donald Steiner
USA
,
Simon Thompson
UK

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 July 2005

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Article

Conference

AAMAS05

Sponsor:

SIGAI

AAMAS05: AAMAS '05 - Fourth International Joint Conference on Autonomous Agents and Multiagent Systems 2005

July 25 - 29, 2005

The Netherlands

Acceptance Rates

Overall Acceptance Rate 1,155 of 5,036 submissions, 23%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

34
Total Citations
View Citations
1,222
Total Downloads

Downloads (Last 12 months)57
Downloads (Last 6 weeks)3

Reflects downloads up to 25 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Karavidas LApostolidis HTsiatsos T(2022)Usability Evaluation of an Adaptive Serious Game Prototype Based on Affective FeedbackInformation10.3390/info1309042513:9(425)Online publication date: 8-Sep-2022
https://doi.org/10.3390/info13090425
Liu GCai MZhao LQin TBrown ABischoff JLiu T(2022)Inspector: Pixel-Based Automated Game Testing via Exploration, Detection, and Investigation2022 IEEE Conference on Games (CoG)10.1109/CoG51982.2022.9893630(237-244)Online publication date: 21-Aug-2022
https://dl.acm.org/doi/10.1109/CoG51982.2022.9893630
Kavanagh WMiller ANorman GAndrei O(2021)Balancing Turn-Based Games With Chained Strategy GenerationIEEE Transactions on Games10.1109/TG.2019.294322713:2(113-122)Online publication date: Jun-2021
https://doi.org/10.1109/TG.2019.2943227
Khan AVernitski ALisitsa A(2021)Untangling Braids with Multi-Agent Q-Learning2021 23rd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)10.1109/SYNASC54541.2021.00032(135-139)Online publication date: Dec-2021
https://doi.org/10.1109/SYNASC54541.2021.00032
DeLaurentis DPanchal JRaz ABalasubramani PMaheshwari ADachowicz AMall K(2021)Toward Automated Game Balance: A Systematic Engineering Design Approach2021 IEEE Conference on Games (CoG)10.1109/CoG52621.2021.9619032(1-8)Online publication date: 17-Aug-2021
https://doi.org/10.1109/CoG52621.2021.9619032
Ripamonti LDistefano FTrubian MMaggiorini DGadia D(2021)DRAGON: diversity regulated adaptive generator onlineMultimedia Tools and Applications10.1007/s11042-021-10620-wOnline publication date: 23-Mar-2021
https://doi.org/10.1007/s11042-021-10620-w
den Hengst FGrua Eel Hassouni AHoogendoorn M(2020)Reinforcement learning for personalization: A systematic literature reviewData Science10.3233/DS-2000283:2(107-147)Online publication date: 11-Nov-2020
https://doi.org/10.3233/DS-200028
Zamith Mda Silva Junior JClua EJoselli M(2020)Applying Hidden Markov Model for Dynamic Game Balancing2020 19th Brazilian Symposium on Computer Games and Digital Entertainment (SBGames)10.1109/SBGames51465.2020.00016(38-46)Online publication date: Nov-2020
https://doi.org/10.1109/SBGames51465.2020.00016
Bernardi AGadia DMaggiorini DPalazzi CRipamonti L(2020)Procedural generation of materials for real-time renderingMultimedia Tools and Applications10.1007/s11042-020-09141-9Online publication date: 15-Jul-2020
https://doi.org/10.1007/s11042-020-09141-9
Williams Ivan der Haar D(2020)A Study on Behavioural Agents for StarCraft 2Proceedings of Fifth International Congress on Information and Communication Technology10.1007/978-981-15-5856-6_47(479-489)Online publication date: 22-Oct-2020
https://doi.org/10.1007/978-981-15-5856-6_47
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Design Strategies for Balancing Exertion Games: A Study of Three Approaches

Towards understanding balancing in exertion games

How Game Balancing Affects Play: Player Adaptation in an Exergame for Children with Cerebral Palsy

Comments

Published In

Sponsors

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Conference

Acceptance Rates

Other Metrics

Article Metrics

Other Metrics

Cited By

Login options

Full Access

PDF

eReader

Abstract

References

Cited By

Index Terms

Recommendations

Design Strategies for Balancing Exertion Games: A Study of Three Approaches

Towards understanding balancing in exertion games

How Game Balancing Affects Play: Player Adaptation in an Exergame for Children with Cerebral Palsy

Comments

Information

Published In

Sponsors

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Conference

Acceptance Rates

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Figures

Other

Share

Share this Publication link

Share on social media

Affiliations