Adaptive AI Systems Design
Role: Systems Design / UX / Interaction Design
Focus: Player behavior modeling, AI state systems, information clarity
Scope: Solo project
OVERVIEW
Stalking Prey is a stealth experience where players must continuously adapt their behavior as enemy AI learns and responds through pattern recognition.
Playing as a vampire defending its home from human-driven deforestation, the player infiltrates enemy camps undetected, converting members to their cause.
This solo-developed case study explores how AI-driven behavior systems can be translated into readable, player-facing mechanics that support informed decision-making, clarity, and tension.
AI STATE BEHAVIOR
MEMORY & ADAPTATION
The AI operates on two layers of memory:
• Short-term memory captures immediate player behavior within and across adjacent rooms
• Long-term memory accumulates player behavior over time- across spatial movement, traversal patterns, and repeated failures- to form persistent zones of interest.
Memory is applied differently depending on the AI’s awareness state:
• Lower awareness states (Neutral, Skeptical, Suspicious) rely on long-term memory, applying learned patterns between encounters
• Higher awareness states (Alert, Panic) leverage short-term memory in real time, responding dynamically within the current encounter
This allows the AI to both learn over time and react in the moment, reinforcing adaptive player behavior.
INPUT CATEGORIES
Spatial Behavior
• Linger time - identifies where players feel safe
• Trail - records player paths for pattern recognition
Movement Behavior
• Pacing - fast vs. slow movement
• Sneak type - short, long, shadow
• Travel mode - ground, flight
Stealth Strategy
• Shadow reliance - increases patrol intensity and expands vision cones
• Attack patterns - aggressive vs. cautious behavior influences AI reactivity
• Blind spot usage - generates suspected zones in AI memory
FINITE STATE MACHINE
AI FSM
Player FSM
EDGE CASES
Players may attempt to manipulate the system by:
• Creating decoy paths
• Artificially generating problem zones
• Triggering intentional detection
In response, the AI escalates into a Panic state, leveraging short-term memory in real time and behaving aggressively to maintain challenge.
KEY DESIGN DECISION
Mind-Read Overlay
Translating AI learning into player understanding introduces a key tension:
too much information overwhelms, while too little obscures how the system behaves.
too much information overwhelms, while too little obscures how the system behaves.
Decision
Prioritize transparency—making the AI’s learning visible to reinforce behavior change and strategic adaptation.
Approach
Surface only actionable information (patterns, danger zones, recent failures), while reducing low-signal or purely descriptive data to prevent cognitive overload.
This ensures the system teaches through feedback, allowing players to understand not just what the AI is doing, but why it is behaving that way.
NEXT STEPS
• Implement a functional AI FSM in Unity
• Conduct user testing to evaluate the effectiveness of Vision and Mind-Read overlays
• Iterate on AI behavior and visualization based on findings
