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Top Down Stealth Toolkit Basics: AI Perception

The v2.0 update for Top Down Stealth Toolkit introduced a custom component driven AI Perception system tailored specifically for implementation in Stealth games. This system uses a combination of four different perception models to evaluate threats based on the new Stimulus model introduced in the update.

Perception Models:

1. Visual Perception: Using a combination of distance, angular, & line trace checks, the Visual Perception enables an AI agent to detect stimuli within it's direct line of sight range. It is capable for sensing Target stimuli as well as certain types of Interest stimuli like Incapacitated & Defunct allies.

2. Aural Perception: The Aural Perception enables an AI agent to perceive & track noises created in the vicinity. It factors in the Loudness of a noise while performing the distance check operation, in order to gauge the relevance of the stimulus. This ensures that louder noises can potentially be heard by an agent, even if it was created outside the default hearing threshold.

3. Intuitive Perception: The Intuitive Perception model is used by AI agents to evaluate stimuli based on their memory, thus negating the need to do any form of range checks. For example, a target that has evaded a Patrol Guard's line of sight can trigger the creation of a Missing Suspect stimulus using it's memory about the target's last seen location, which can then be perceived through this system with very minimal search costs.

4. Motion Perception: Unlike the other perception models, the Motion Perception is used to evaluate stimuli that have not been directly perceived by the agent. An example of this would be a stimulus created by an entity located in close proximity to an agent, but not necessarily within it's direct field of vision. As a result, this system uses a dedicated alert meter to make sure that the agent responds to a perceived stimulus only if it stays within range for certain period of time.


All or any of these systems can be modified or turned off from the owning class, thus facilitating creation of different types of perception behavior among two types of agents like say, a Patrol Guard & a Surveillance Camera.

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