Talk · IEEE Symposium Series on Computational Intelligence · December 5, 2023
Comparing Behaviour Tree and Hierarchical Task Network Planning Methods for their Impact on Player Experience
Alexander Kedalo, A. A. Zykov, Hamna Aslam, Manuel Mazzara
Abstract
The AI in games has a large impact on the player's experience, but the large variety of available AI implementation methods makes it difficult to determine which one(s) to use in any particular project, and the differences in their impact on players are mostly unstudied. This paper presents a compar-ative study to analyse the effects of Behaviour Tree AI and Hierarchical Task Network Planning AI on players experiences. The study participants (players) were given two prototypes of a third-person shooter game, each utilising different AIs, to play and give feedback on. According to the results obtained, players did not notice any major differences between the two prototypes, leading us to believe that the Behaviour Tree AI may be a better solution in most cases, as it is easier to implement.
More by Manuel Mazzara
- Anchored Rank-Adversarial Fine-Tuning Pretrained Visual Place Recognition ModelsarXiv · 2026
- See the Future, Classify the Present: Stochastic Latent World Modeling for Hyperspectral ImagesarXiv · 2026
- Autoresearch with Coding Agents: Generalizers and Metric-Maximizers on Quran Recitation DataLecture notes in computer science · 2026 · Citations: 1
- What Counts as a Mistake? Annotating Recitation Events in Quran Memorization TranscriptsarXiv · 2026