# Generating Diverse and Competitive Play-Styles for Strategy Games

arXiv: https://arxiv.org/abs/2104.08641 · HTML: https://mlsanity.com/paper/2104.08641/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 28 references that are in the map.
- First posted on arXiv 17 Apr 2021.

## Builds on (most cited first, 8 of 8)

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Illuminating search spaces by mapping elites](https://mlsanity.com/paper/1504.04909/) (2015)
- [General Video Game AI: a Multi-Track Framework for Evaluating Agents, Games and Content Generation Algorithms](https://mlsanity.com/paper/1802.10363/) (2018)
- [Talakat: Bullet Hell Generation through Constrained Map-Elites](https://mlsanity.com/paper/1806.04718/) (2018)
- [Diverse Agents for Ad-Hoc Cooperation in Hanabi](https://mlsanity.com/paper/1907.03840/) (2019)
- [Rolling Horizon Evolutionary Algorithms for General Video Game Playing](https://mlsanity.com/paper/2003.12331/) (2020)
- [Asymmetric Action Abstractions for Multi-Unit Control in Adversarial Real-Time Games](https://mlsanity.com/paper/1711.08101/) (2017)
- [Efficient Evolutionary Methods for Game Agent Optimisation: Model-Based is Best](https://mlsanity.com/paper/1901.00723/) (2019)

## Built on by (most cited first, 6 of 6)

- [Generative Personas That Behave and Experience Like Humans](https://mlsanity.com/paper/2209.00459/) (2022)
- [Online Damage Recovery for Physical Robots with Hierarchical Quality-Diversity](https://mlsanity.com/paper/2210.09918/) (2022)
- [Hierarchical Quality-Diversity for Online Damage Recovery](https://mlsanity.com/paper/2204.05726/) (2022)
- [Tackling Neural Architecture Search With Quality Diversity Optimization](https://mlsanity.com/paper/2208.00204/) (2022)
- [Adapting Procedural Content Generation to Player Personas Through Evolution](https://mlsanity.com/paper/2112.04406/) (2021)
- [Elastic Monte Carlo Tree Search with State Abstraction for Strategy Game Playing](https://mlsanity.com/paper/2205.15126/) (2022)

## Abstract (arXiv)

Designing agents that are able to achieve different play-styles while maintaining a competitive level of play is a difficult task, especially for games for which the research community has not found super-human performance yet, like strategy games. These require the AI to deal with large action spaces, long-term planning and partial observability, among other well-known factors that make decision-making a hard problem. On top of this, achieving distinct play-styles using a general algorithm without reducing playing strength is not trivial. In this paper, we propose Portfolio Monte Carlo Tree Search with Progressive Unpruning for playing a turn-based strategy game (Tribes) and show how it can be parameterized so a quality-diversity algorithm (MAP-Elites) is used to achieve different play-styles while keeping a competitive level of play. Our results show that this algorithm is capable of achieving these goals even for an extensive collection of game levels beyond those used for training.