Greed: 0.85
Scripted FSM NPC Engine
State: PATROL
Current FSM Transition Logic
IF Noise within vision range -> INVESTIGATE
IF Player detected -> ALERT
ELSE -> PATROL (Ignores dropped items)
FSM Execution Metrics
Tick Latency: 0.02 ms
Token Cost: 0 tokens
Predictability: 100% Deterministic
Memory & Utility AI Agent
Dynamic Action: EVALUATING
Agent Epistemic Memory Trail
0 items
- Initializing perceptual field...
Utility Decision Vectors
Utility Duty: 0.40
Utility Greed: 0.85
Inferred Prompt Latency: 115 ms
Live System Telemetry
Emergent Behavior Index: HIGH (0.88)
State Hallucination Risk: LOW (4.2%)
Est. 1k Ticks Token Cost: $0.00 vs $0.42
Calculated via 120-token context vector per agent memory evaluation cycle.
Emergent Narrative & Divergence Log
Comparing NPC Decision Offsets
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Observed Divergence: While the traditional FSM strictly ignores unhandled environmental objects like dropped gold coins unless explicitly coded in state rules, the Memory & Utility AI Agent perceives the coin, updates its epistemic memory vector, and dynamically overrides patrol duty to steal the gold coin first.
100-Iteration Automated Benchmark Report
Traditional FSM Performance
Total Scenarios: 100
Success Rate: 100% Deterministic
Avg Tick Latency: 0.018 ms
Total Tokens: 0
Novel/Emergent Actions: 0
Memory Utility AI Agent
Total Scenarios: 100
Exploit/Emergent Rate: 92%
Avg Latency: 118 ms
Estimated Tokens: 12,400
Hallucination Anomaly Rate: 3.8%
Benchmark Conclusion: Scripted FSMs provide zero-cost frame budget reliability for basic mechanics. However, dynamic AI Agents create superior emergent stealth gameplay, unscripted player exploitation, and adaptive memory retention at the cost of non-zero frame compute latency and token budgets.