No Player Left Behind: Evolving Dungeons and Dragons Combat to Optimize Difficulty and Player Contributions

Creating well-balanced combat encounters can be a difficult task for Game Managers (GMs) in tabletop games such as Dungeons and Dragons (DnD). This work uses a simulation environment to generate new sets of DnD encounters that can be optimized for both difficulty and balance among player contributions. Encounters are evaluated using simulated games that can either be run probabilistically (using dice rolls) or with deterministic expected outcomes. While the expected approach allows game outcomes to be simulated substantially faster and is a good estimate of difficulty, it is a less reliable measure of balance. A genetic algorithm was used to generate encounters that meet the desired difficulty and where all players are needed for success.

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Publication Details

Journal
Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment
Published
2024-11-15
DOI
https://doi.org/10.1609/aiide.v20i1.31883
Citations
2
Primary Topic
Digital Games and Media
Type
article
Field-Weighted Citation Impact
2.26
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article

No Player Left Behind: Evolving Dungeons and Dragons Combat to Optimize Difficulty and Player Contributions

Seth Cooper, Fiona Shyne
2 citations
Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment
Digital Games and Media
2.26
article

No Player Left Behind: Evolving Dungeons and Dragons Combat to Optimize Difficulty and Player Contributions

Seth Cooper, Fiona Shyne
article en
2 citations

Abstract

Creating well-balanced combat encounters can be a difficult task for Game Managers (GMs) in tabletop games such as Dungeons and Dragons (DnD). This work uses a simulation environment to generate new sets of DnD encounters that can be optimized for both difficulty and balance among player contributions. Encounters are evaluated using simulated games that can either be run probabilistically (using dice rolls) or with deterministic expected outcomes. While the expected approach allows game outcomes to be simulated substantially faster and is a good estimate of difficulty, it is a less reliable measure of balance. A genetic algorithm was used to generate encounters that meet the desired difficulty and where all players are needed for success.

Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital EntertainmentVol. 20(1)
Universidad del Noreste (MX)
Openalex Percentile: Top 9%
Digital Games and Media
2.26
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