Goal-Oriented Adversarial Movement Behaviours with Genetic Programming

This paper was written as part of high distinction work for HIT3046 Artificial Intelligence for Games at Swinburne University of Technology.


Genetic programming has been successfully applied in numerous game-based contexts. Though a popular construct in game development, industry tends to favour manually coded rather than generated behaviour trees despite their demonstrated performance.

This work applies genetic programming to determine a behaviour tree capable of moving an agent through an environment to a goal location whilst avoiding detection.

Tests using five alternate game maps show the potential of the technique to solve this problem.

The full paper can be found here. Code is available on GitHub and Bitbucket.