Top Down Stealth Toolkit

UE4 Asset Top Down Stealth Toolkit



• Top-Down Camera perspective.
• A custom-built AI Perception system tailored specifically for developing Stealth games enable AI agents to perceive stimuli through four different types of perception models: Visual, Aural, Intuitive, & Motion perception.
• A Stimulus generation system that enables users to easily customize & add new types of perceivable stimuli.
• Patrol Guards that can respond to a wide variety of stimuli including the player character, incapacitated team mates, alarms, footstep & gunshot noises, & more.
• Automated security devices like Cameras, Motion Sensors, Laser Security Systems, & Turrets function provide additional layers of challenge to the player.
• Use Gadgets & Weapons to distract or disable AI Bots.
• A Global Alert Level system that dynamically increases the difficulty through deployment of AI reinforcements.
• Edge Detection algorithms help deliver increased performance for Line of Sight Visualization.
• A Mission Stats Display system that tracks & updates high score information for every level.

Technical Details:​

• Modular components are employed throughout the toolkit to implement features with minimal coupling.
• The Global Alert Level system controls the Global Alert Meter by employing event dispatchers to continuously listen in new stimuli being perceived by AI agents across the level.
• The AI Surveillance Controller directs the activation of all AI agents within the level. This system can be leveraged to create different starting conditions for each level, choosing to activate all security measures by default or have them activated in a modular fashion based on the overall threat perceived by the AI.
• The AI Sensory Manager continuously evaluates all stimuli against various agents & assigns new objectives to the AI agents based on the results.
• The Patrol Guard AI uses Behavior Trees to respond to the various objectives assigned by the AI Sensory Manager.
• The Line of Sight Visualization system employs raycast driven edge detection approximations to deliver vastly increased performance over brute force models.

Input: Mouse & Keyboard
Network Replicated: No
Supported Development Platforms: Windows
Supported Target Build Platforms: Windows
Documentation: Fully Commented
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