Emergent behavior is the ability of a game AI to generate new and unexpected behaviors that are not explicitly programmed into the game.

Evolutionary computation is the process of creating game AI that can generate new and unexpected behaviors by using techniques inspired by natural evolution.

Machine learning is the ability of a game AI to learn from its experiences and improve its performance over time.

Supervised learning is the type of machine learning in which the AI learns from a set of labeled data.

Unsupervised learning is the type of machine learning in which the AI learns from a set of unlabeled data.

Reinforcement learning is the type of machine learning in which the AI learns from its interactions with the environment.

Transfer learning is the type of machine learning in which the AI learns from the knowledge of other AIs.

Procedural generation is the ability of a game AI to generate new content, such as levels, characters, and items.

Personalized AI is the ability of a game AI to generate content that is tailored to the individual player's preferences.

Balanced AI is the ability of a game AI to generate content that is challenging but not frustrating.

Aesthetic AI is the ability of a game AI to generate content that is visually appealing.

Narrative AI is the ability of a game AI to generate content that is narratively engaging.

Emotional AI is the ability of a game AI to generate content that is emotionally impactful.

Social AI is the ability of a game AI to generate content that is socially engaging.

Educational AI is the ability of a game AI to generate content that is educational.