Non-player characters (NPCs) or virtual characters created and controlled by artificial intelligence systems or algorithms in video games, simulations, or other interactive settings are sometimes referred to as character AI. These artificial intelligence (AI) systems control the activities, choices, and interactions of virtual world characters, giving them a more lifelike, responsive, and captivating appearance for users or gamers.

Character AI can use a range of methods and tools, such as:

1. Behaviour Trees:

2. These are hierarchical structures used to model the behaviour of NPCs. They consist of nodes representing actions, conditions, and decision points, allowing for complex and adaptive behaviour.

3. Finite State Machines (FSMs):

4. FSMs are commonly used to model the behaviour of characters with a finite number of states and transitions between them based on certain conditions or events.

5. Reactive AI:

6. This involves creating NPCs that react to stimuli or events in real-time, such as changes in the environment or interactions with other characters.

7. Machine Learning:

8. Some character AI systems employ machine learning techniques to allow NPCs to learn and adapt their behaviour based on experience or data.

9. Pathfinding Algorithms:

10. These algorithms enable NPCs to navigate through the virtual environment efficiently, avoiding obstacles and reaching their destinations.

11. Natural Language Processing (NLP):

12. In some cases, character AI systems may incorporate NLP to enable more natural and dynamic interactions between players and NPCs through speech- or text-based communication.

How does Charecter AI work?

The artificial intelligence systems in charge of directing the behaviour and activities of characters in a virtual world are referred to as character AI, or NPC (Non-Player Character) AI in the game industry. Depending on the intricacy of the game and the authors' objectives, the details can vary significantly, but here's a broad rundown of how character AI often functions:

1. Decision Making: Character AI involves decision-making algorithms that determine what actions the characters should take in various situations. These decisions can range from simple tasks like walking and jumping to more complex behaviours like interacting with objects, engaging in combat, or following a scripted storyline.

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3. Behaviour Trees: One common approach to implementing character AI is through behaviour trees. Behaviour trees organise a character's behaviours into a hierarchical structure, with higher-level goals at the top (e.g., "find cover") and more specific actions at the bottom (e.g., "move to specific coordinates"). The AI evaluates the tree at each step to decide which action to take next based on the current situation.

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5. State Machines: Another approach is using finite state machines (FSMs), where the character's behaviour is defined by its current state and transitions between states based on certain conditions. For example, a character might have states like "idle," "patrol," "alert," and "attack," with transitions triggered by events such as spotting an enemy or taking damage.

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7. Pathfinding: Many character AI systems incorporate pathfinding algorithms to determine the best route for characters to navigate the game world. Pathfinding algorithms calculate efficient paths around obstacles and terrain features, allowing characters to move smoothly from one location to another.