A subset of AI focused on algorithms that enable systems to learn from data and improve over time without explicit programming.

Computational models inspired by the human brain, used in machine learning to recognize patterns and make decisions.

A type of machine learning where agents learn by interacting with their environment and receiving feedback through rewards or penalties.

A field of AI that focuses on the interaction between computers and human language, enabling NPCs to understand and respond to player dialogue.

A model used in AI that makes decisions based on a series of branching choices, suitable for creating NPC behavior.

AI systems that modify their behavior based on player actions, enhancing engagement and gameplay experience.

A class of algorithms used in reinforcement learning that balances exploration and exploitation to maximize rewards.

Principles guiding the responsible development of AI, ensuring fairness, transparency, and respect for player autonomy.

The ability of AI systems to process information and make decisions instantaneously during gameplay.

Software frameworks used for game development, providing tools for rendering graphics, physics simulation, and AI integration.

Characters in a game that are not controlled by players but exhibit behaviors programmed by developers.

Methods used to improve the efficiency and performance of AI algorithms in real-time gaming environments.

The process of analyzing a program to determine where time and resources are being spent, aiding in optimization.

A cycle where the output of a system influences its future inputs, crucial for training adaptive AI.

The process of testing a game with real players to gather feedback on gameplay and AI behavior.

Using scripts and tools to test AI behaviors systematically, ensuring they function correctly under various scenarios.

The means by which players interact with the game, including menus, buttons, and HUD elements.

The rules and systems that govern gameplay, including how AI interacts with players.

Evaluating the performance of AI systems against established standards to ensure efficiency.

Creating an early model of a game to test concepts and functionality before full-scale development.

A collection of data used to train machine learning models, critical for developing effective AI.

A step-by-step procedure or formula for solving a problem, fundamental to programming AI behavior.

An entity in AI that perceives its environment and takes actions to achieve specific goals.

Combining data from multiple sensors to improve the accuracy of AI decisions, often used in NPC behavior.

A technique for assessing how the results of a statistical analysis will generalize to an independent dataset.