Towards Diverse Non-Player Character behaviour discovery in multi-agent environments

Jan Kirk*, Marco Scirea

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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Abstract

This paper introduces a method for developing diverse Non-Player Character (NPC) behaviour through a multiagent genetic algorithm based on Map-Elites. We examine the outcomes of implementing our system in a test environment, with a particular emphasis on the diversity of the evolved agents in the feature space. This research is motivated by how diverse NPCs are an important factor for improving player experience. We show how our multi agent map-elite algorithm is capable of isolating the evolved NPCs in the chosen feature space. Results showed that variation in agent fitness could be predicted with 40 % from agent genomes, when agents played 100 games each.

Original languageEnglish
Title of host publication 2024 IEEE Conference on Games (CoG)
Number of pages4
PublisherIEEE Press
Publication dateAug 2024
ISBN (Electronic)9798350350678
DOIs
Publication statusPublished - Aug 2024
Event6th Annual IEEE Conference on Games, CoG 2024 - Milan, Italy
Duration: 5. Aug 20248. Aug 2024

Conference

Conference6th Annual IEEE Conference on Games, CoG 2024
Country/TerritoryItaly
CityMilan
Period05/08/202408/08/2024
SponsorHotel Principe di Savoia Milano - Dorchester Collection, IEEE, IEEE Computational Intelligence Society, Politecnico Milano, University of Milan, Universitas Studiorum Mediolanensis
SeriesProceedings - IEEE Conference on Games
ISSN2325-4270

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