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

TL;DR

This paper presents a framework that automatically generates diverse agent behaviors of varying difficulty levels in video games, aiming to enhance adaptive difficulty management and automate testing to improve game quality.

Contribution

It introduces a novel framework combining behavior trees and genetic algorithms for automatic behavior creation and testing in games, reducing manual effort.

Findings

Successfully generated diverse agent behaviors across difficulty levels

Automated testing identified game defects and logic exploits

Framework improves game testing efficiency

Abstract

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.