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Purpose: To model decision-making processes with a tree-like structure of choices. Example: A simple enemy AI that makes sequential decisions like if-else statements, determining actions such as attacking, defending, or retreating.

Purpose: To represent strategic value across the game space, influencing AI decision-making. Example: A strategy game where the AI uses an influence map to determine strong and weak areas of control, optimizing troop movements.

Purpose: To plan sequences of actions by decomposing high-level tasks into more manageable subtasks. Example: An AI planning a mission by breaking it down into smaller tasks like 'secure area', 'gather resources', and 'build base'.

Purpose: To enable AI to learn and make decisions based on training data, simulating a basic brain structure. Example: Teaching an AI racer to drive on different tracks by learning from thousands of races, adjusting its neural network weights through backpropagation.

Purpose: To control AI behavior through predefined scripts for predictable and directed outcomes. Example: Cutscenes or specific gameplay moments where NPCs perform predetermined actions, like a shopkeeper greeting a player.

Purpose: To find the shortest path between two points efficiently. Example: Pathfinding for NPCs to navigate complex terrain using a grid of nodes, where A* algorithm is applied to find the most cost-effective path.

Purpose: To simulate evolution and natural selection processes to optimize solutions over time. Example: Evolving NPC behavior patterns over generations to improve their effectiveness in combat situations.

Purpose: To manage an AI's states and behaviors in an organized manner. Example: Implementing enemy behavior that switches between 'patrol', 'chase', and 'attack' states depending on the player's actions.

Purpose: To simulate realistic group movement of AI entities, such as herds or flocks. Example: Implementing bird-like creatures that move cohesively in groups using alignment, cohesion, and separation rules.

Purpose: To make decisions in complex games by exploration and exploitation of possible future states. Example: An AI in a game of Go or Chess simulating many possible moves and outcomes to choose the most promising path.

Purpose: To create complex AI behaviors by structuring them into a tree hierarchy. Example: Organizing enemy decision-making into a tree structure, with nodes representing tasks like 'find player', 'move to player', and 'shoot player'.

Purpose: To execute actions based on a set of predefined rules that the AI follows. Example: A trading AI that executes buy and sell decisions based on market conditions described by a set of if-then rules.

Purpose: To allow AI to understand and navigate the game's 3D space more effectively. Example: Creating a mesh that covers walkable surfaces and using algorithms to navigate AI characters across it, avoiding obstacles.

Purpose: To create complex and unpredictable AI behaviors from the interaction of simpler systems. Example: Simulated ecosystems where the interactions between AI-controlled creatures result in unexpected behaviors and dynamics.