Using Evolutionary Algorithms to Target Complexity Levels in Game Economies

Author:

Rogers Katja1ORCID,Le Claire Vincent2,Frommel Julian3ORCID,Mandryk Regan4ORCID,Nacke Lennart E.5

Affiliation:

1. Digital Interactions Lab, University of Amsterdam, Amsterdam, The Netherlands

2. Databases and Information Systems, University of Augsburg, Augsburg, Germany

3. Interaction/Multimedia Group, Utrecht University, Utrecht, The Netherlands

4. Department of Computer Science, University of Saskatchewan, Saskatchewan, Canada

5. HCI Games Group, University of Waterloo, Waterloo, ON, Canada

Funder

Institute of Media Informatics at Ulm University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Artificial Intelligence,Control and Systems Engineering,Software

Reference53 articles.

1. Simulating Mechanics to Study Emergence in Games

2. Virtual economics: Applying economics to the study of game worlds;lehdonvirta;Proc Conf Future Play,0

3. AI for dynamic difficulty adjustment in games, 5/2004;hunicke;Proc Assoc Advance Artif Intell,0

4. Measuring Inflation within Virtual Economies using Deep Reinforcement Learning

5. Stardew Valley,2016

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1. It might be balanced, but is it actually good? An Empirical Evaluation of Game Level Balancing;2024 IEEE Conference on Games (CoG);2024-08-05

2. G-PCGRL: Procedural Graph Data Generation via Reinforcement Learning;2024 IEEE Conference on Games (CoG);2024-08-05

3. GEEvo: Game Economy Generation and Balancing with Evolutionary Algorithms;2024 IEEE Congress on Evolutionary Computation (CEC);2024-06-30

4. The MMO Economist: AI Empowers Robust, Healthy, and Sustainable P2W MMO Economies;Companion Proceedings of the ACM Web Conference 2024;2024-05-13

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