# Automatic difficulty management and testing in games using a framework based on behavior trees and genetic algorithms

arXiv: https://arxiv.org/abs/1909.04368 · HTML: https://mlsanity.com/paper/1909.04368/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2019.

## Built on by (most cited first, 2 of 2)

- [Behavior Trees in Robot Control Systems](https://mlsanity.com/paper/2203.13083/) (2022)
- [EvolvingBehavior: Towards Co-Creative Evolution of Behavior Trees for Game NPCs](https://mlsanity.com/paper/2209.01020/) (2022)

## Abstract (arXiv)

The diversity of agent behaviors is an important topic for the quality of video games and virtual environments in general. Offering the most compelling experience for users with different skills is a difficult task, and usually needs important manual human effort for tuning existing code. This can get even harder when dealing with adaptive difficulty systems. Our paper's main purpose is to create a framework that can automatically create behaviors for game agents of different difficulty classes and enough diversity. In parallel with this, a second purpose is to create more automated tests for showing defects in the source code or possible logic exploits with less human effort.