Stress-Testing Autonomous Resilience: The Trinity AI Drone Swarm Simulation 2

2000 low cost 'Trinity' AI enabled swarm drones against 24 high value elite adversaries coordinated from three armoured hubs in a hub and spoke network

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Stress-Testing Autonomous Resilience: The Trinity AI Drone Swarm Simulation

'Trinity' AI 48-Neuron Targeting System: A Proof of Concept Simulation

the ‘Trinity’ AI was simulated for both targeting and coordination by a massive low cost and fully decentralized drone warm (2000 ultra low cost drones, ~$5-10 million total cost) against an elite state level force using hub and spoke coordination with 24 highly equipped state of the art drones and 3 armoured command and control hubs (~$50-100 million total cost). The simulation notebook is available on Google Colab. The Trinity AI enabled drone swarm was devastating, losing only 13.3% of its total force, while the command and control state military force were annihilated. This simple simulation demonstrates Trinity AI’s capabilities both for enemy targeting and drone swarm coordination. The write up was created with Deepseek.

EXECUTIVE SUMMARY: MASSIVE SWARM TRINITY AI COMBAT SIMULATION

For Defense Industry Professionals

System Overview

This simulation demonstrates a 2,000-unit autonomous drone swarm (Team A) engaging 24 elite drones + 3 command hubs (Team B) in a 300×200 km combat zone over 800 simulation steps. The swarm implements Trinity AI - a neuromorphic coordination system enabling emergent, decentralized command-and-control without central authority.

Key Military Applications

1. Mass Asymmetric Warfare: Demonstrated swarm victory (86.6% survival) against technologically superior but numerically inferior defenses

2. Adversarial Resilience: System maintained functionality under coordinated electronic warfare (jamming, GPS spoofing, DDoS, malware)

3. Distributed Command: No single point of failure - coordination emerges locally via state-based interactions

4. Adaptive Force Multiplier: Swarm dynamically allocates roles (10.1% Strikers, 89.9% balanced mix) based on combat conditions

Performance Metrics

• Combat Effectiveness: 24 elite drones and 3 command hubs eliminated in 24 steps

• Loss Exchange Ratio: ~1:11 (306 swarm losses vs. 27 high-value targets)

• Stress Under Fire: Average 5.02% stress despite intense combat

• Intervention Rate: 45 autonomous interventions (82% NAVIGATE, 16% FORK)

• System Integrity: 98.5% average health post-engagement

Operational Advantages

• Graceful Degradation: Performance decays gradually under attack rather than catastrophic failure

• Low Communication Overhead: Local-only interactions (50km radius) minimize detectability

• Autonomous Adaptation: Real-time role switching without human intervention

• Scalability: Architecture supports thousands of units with linear computational growth

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For AI/ML Researchers

Neuromorphic Architecture

The Trinity AI implements a continuous-discrete hybrid model:

• Continuous States: Internal neuron values ∈ [-1.5, 1.5] with adaptive thresholds

• Discrete Mappings: EXCITE (θ>0.15-0.35), POISE, INHIBIT (θ<-0.05--0.25)

• Hebbian Learning: Local weight adaptation (LR=0.004) with stress modulation

Emergent Intelligence Mechanisms

1. State-Driven Role Allocation:

   text

EXCITE → STRIKER: attack-focused (1.25× damage, 1.35× speed)
INHIBIT → SHIELDER: defensive (1.35× cohesion, 0.75× damage)
POISE → SCOUT: reconnaissance/balanced

1. Distributed Coordination:

   • Local influence: weight = exp(-dist/30)×(1-0.5×stress)×integrity

   • Mission correlation: ρ∈[0.3,0.7] shared stimulus by phase

   • Stress contagion: High-stress drones have reduced influence

2. Self-Stabilization:

   • Möbius instability detection (transition zone + high flip rate)

   • Autonomous interventions: NAVIGATE, INOCULATE, FORK, EMERGENCY

   • Collective sovereignty metrics maintain system boundaries

Key Innovations

• Bio-inspired Coordination: Mimics flocking/schooling with cognitive components

• Distributed Consensus: No global knowledge required - decisions emerge locally

• Resilient Learning: Stress-modulated Hebbian adaptation prevents overfitting to attacks

• Quantified Autonomy: Sovereignty metrics (0.850 final score) measure emergent intelligence

Research Implications

• Demonstrates viability of distributed neuro-symbolic systems for complex tasks

• Provides framework for adversarially robust multi-agent systems

• Offers metrics for quantifying emergent intelligence in artificial collectives

• Validates local-only communication for scalable coordination

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For Cypherpunk/Decentralized Defense Community

Architecture of Resistance

This system embodies cypherpunk principles through:

1. Radical Decentralization:

   • No central authority or command node

   • Peer-to-peer state synchronization only

   • Emergent coordination from simple local rules

2. Censorship Resistance:

   • No single point of failure or control

   • System functions with partial participation

   • Autonomous recovery from node compromise

3. Adversarial Robustness:

   • Withstood 4 coordinated attack vectors simultaneously

   • 648 Möbius detections → 45 interventions → system stabilization

   • Stress-limited influence prevents panic propagation

Sovereignty Metrics Framework

The simulation introduces quantifiable autonomy measures:

Boundary Score = 0.30×stability + 0.25×modularity + 0.25×containment + 0.20×consistency

Sovereignty achieved when:

• Boundary Score ≥ 0.70

• Value Extraction Risk ≤ 1.5625%

• Mean Health > 30%

• Combat Experience > 2 engagements

50% original strength maintained

Anti-Exploitation Features

1. Learning Rate Modulation: Under malware threat, learning slows (0.004→0.0015)

2. Weight Reset: FORK intervention clears compromised Hebbian associations

3. Integrity-Based Influence: Compromised nodes (low integrity) have reduced sway

4. State Neutralization: EMERGENCY intervention resets neuron state to 25%

Governance Implications

• Leaderless Organization: Collective decision-making without hierarchy

• Transparent Metrics: Sovereignty scores provide verifiable autonomy measures

• Permissionless Participation: Any compatible node can join/leave swarm

• Fault-Tolerant Consensus: Majority rule emerges without voting mechanisms

Potential Applications Beyond Combat

• Decentralized Infrastructure: Power grid management, communication networks

• Autonomous Response: Disaster relief, environmental monitoring

• Collective Security: Community defense without state actors

• Resilient Networks: Censorship-resistant communication systems

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Cross-Audience Findings

Shared Advantages

1. Scalability: Tested with 2,000 units, architecture supports order-of-magnitude increases

2. Resilience: 86.6% survival rate under sophisticated multi-vector attacks

3. Autonomy: Human intervention not required for tactical decisions

4. Adaptability: Real-time role reallocation based on mission phase and conditions

Limitations (Proof-of-Concept)

• Simplified sensor/actuator models

• Homogeneous drone capabilities in each class

• 2D simulation environment

• Perfect local communication (no packet loss)

• Basic adversarial effect models

Development Recommendations

1. Phase 1 (6 months): Hardware-in-loop testing with 50 physical drones

2. Phase 2 (12 months): Mixed reality testing (1,000 virtual + 100 physical)

3. Phase 3 (18 months): Field deployment with full adversarial testing

4. Phase 4 (24 months): Production system with modular payload support

Ethical Considerations

• Accountability: Distributed decision-making complicates responsibility attribution

• Escalation Risks: Autonomous systems may accelerate conflict dynamics

• Verification: Need transparent sovereignty metrics for treaty compliance

• Control: Ensuring meaningful human oversight in deployment decisions

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Conclusion

This Trinity AI simulation demonstrates a paradigm shift from centralized to emergent swarm intelligence. For defense: a cost-effective force multiplier. For AI researchers: a novel distributed cognition architecture. For cypherpunks: a blueprint for autonomous, resilient organizations.

The system’s victory (24 steps, minimal losses) against superior defenses validates its core premise: decentralized, adaptive collectives can outperform centralized systems in complex adversarial environments.

Next Steps: Hardware validation, enhanced sensor models, and integration with existing C2 systems for hybrid human-swarm operations.

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*Simulation Parameters: 2,000 swarm drones vs. 24 elites + 3 hubs, 800 max steps, 4 adversarial event types, Trinity AI coordination with Hebbian learning and distributed interventions.*

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Coordination & Targeting

Trinity AI is explicitly used for both targeting and coordination at multiple levels. Here’s how it functions:

1. TARGETING MECHANISMS

Direct Targeting via Trinity States:

python

# Trinity state determines combat mode and targeting strategy
if state == “EXCITE”:
    self.role = “STRIKER”
    self.combat_mode = “ATTACK”
    # STRIKER: Targets based on enemy value/distance ratio
    value = target.health/1000.0
    score = (value/max(1.0, distance)) * noise_factor
    
elif state == “INHIBIT”:
    self.role = “SHIELDER” 
    self.combat_mode = “EVADE”
    # SHIELDER: Prioritizes proximity for protection
    score = (1.0/max(1.0, distance)) * noise_factor
    
else:  # POISE state
    self.role = “SCOUT”
    self.combat_mode = “SWARM”
    # SCOUT: Balanced targeting
    score = (1.0/max(1.0, distance)) * noise_factor

State-Dependent Accuracy Modulation:

final_accuracy = base_accuracy × range_factor × state_multipliers[”accuracy”]

Where state multipliers are:

• EXCITE/STRIKER: 1.10× accuracy

• INHIBIT/SHIELDER: 0.92× accuracy

• POISE/SCOUT: 1.00× accuracy

Degradation-Aware Targeting:

noise_factor = U(0.85, 1.15) × (1.0 + 0.35 × compute_degraded)

Higher compute degradation → noisier, less optimal target selection.

2. COORDINATION MECHANISMS

A. Local Swarm Influence (Distance-Weighted):

swarm_input = Σ[neighbor_state × weight] / Σ[weights]
weight = exp(-distance/30.0) × stress_mod × neighbor_integrity

Each drone’s Trinity state is influenced by nearby drones’ states, creating emergent coordination.

B. Role-Based Behavior Emergence:

Trinity State → Role → Behavioral Profile:

• EXCITE → STRIKER: High damage (1.25×), high speed (1.35×), lower cohesion (0.85×)

• INHIBIT → SHIELDER: Defensive (0.75× damage), high cohesion (1.35×), protective positioning

• POISE → SCOUT: Balanced stats, swarm-adaptive behavior

C. Mission-Phase Coordination:

mission_input = base_stim(phase) + correlated_noise + 0.3×stress

All drones receive correlated mission stimulus based on phase:

• DEPLOY: Low stimulus (-0.1 to 0.2)

• RECON: Moderate stimulus (-0.2 to 0.4)

• EXECUTE: High stimulus (-0.4 to 0.6)

• EVASIVE: Very high stimulus (-0.6 to 0.8)

• RETURN: Low stimulus (-0.1 to 0.1)

D. Cohesion Movement:

dx_swarm = (avg_neighbor_x - self_x) × cohesion_adjustment × cohesion_preference
cohesion_adjustment = (distance_to_swarm_center - desired_radius)/desired_radius

Where cohesion_preference is state-dependent:

• EXCITE/STRIKER: 0.85× (more independent)

• INHIBIT/SHIELDER: 1.35× (tighter formation)

• POISE/SCOUT: 1.05× (balanced)

3. EMERGENT SWARM BEHAVIORS

Swarm Density Effects:

swarm_accuracy_boost = min(0.3, swarm_density × 0.1)
swarm_damage_boost = min(0.5, swarm_density × 0.2)

Higher local density → accuracy and damage bonuses.

Distributed Interventions:

When drones detect issues (Möbius instability, high stress, degradations), they autonomously trigger interventions:

• NAVIGATE: Fix navigation/comms issues, increase cohesion

• INOCULATE: Reduce malware risk, slow learning (anti-exploit)

• FORK: Reset local weights, re-stabilize

• EMERGENCY: Aggressive recovery, evasion mode

4. COLLECTIVE INTELLIGENCE METRICS

Sovereignty Measurement:

The swarm’s collective Trinity states are analyzed for:

• Stability: Mean absolute state values

• Modularity: Health variance across swarm

• Containment: Combined stress/health metrics

• Consistency: Flip rate patterns

Boundary Score Calculation:

boundary_score = 0.30×stability + 0.25×modularity + 0.25×containment + 0.20×consistency

Higher scores indicate better coordinated, autonomous swarm operation.

5. KEY COORDINATION FEATURES

1. Adaptive Role Allocation: Drones dynamically switch roles based on Trinity state without central command.

2. Local-Only Communication: Coordination emerges from nearest-neighbor interactions only (50-unit radius).

3. Graceful Degradation: Under adversarial conditions (jamming, spoofing), coordination degrades gradually rather than failing completely.

4. Stress-Contagion Modulation: High-stress drones have reduced influence on neighbors:

stress_mod = 1.0 - 0.5×(stress_self + stress_neighbor)

5. Mission-Awareness: All drones share correlated mission stimulus, creating phased swarm behavior.

6. EXAMPLE COORDINATION SCENARIOS

Scenario: Attack on Command Hub

1. RECON phase: Most drones in POISE/SCOUT → spread out, gather information
2. EXECUTE phase: Increased stimulus → more EXCITE/STRIKER drones emerge
3. Natural formation: SHIELDER drones (INHIBIT) position protectively around STRIKERs
4. Under attack: Stress increases → interventions trigger → some drones switch to EVADE
5. Successful strike: Target destroyed → stress decreases → coordination returns

Summary: Trinity AI provides a neuromorphic coordination framework where targeting and swarm behaviors emerge from individual drone states influenced by mission context, local neighbors, and adversarial conditions. It’s a distributed, adaptive system rather than centralized command-and-control.

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MASSIVE SWARM TRINITY AI COMBAT SIMULATION

MATHEMATICAL MODELS AND METHODOLOGY

1. TRINITY NEURON MODEL

1.1 Continuous State Update

state_t = state_{t-1} × leak + (input_signal + hebbian_influence) × (1 - leak) + noise

where:

• leak = clamp(0.90 - 0.08 × fatigue, 0.78, 0.92)

• input_signal = mission_input + 0.5 × swarm_input

• hebbian_influence = Σ(w_i × s_i) / Σ|w_i| × 0.25 (if connections exist)

• noise ~ N(0, 0.035 × (1.0 + 1.2 × stress))

• State clamped: state_t ∈ [-1.5, 1.5]

1.2 Trinary State Mapping

trinary_state = 
    EXCITE    if state > θ_excite
    INHIBIT   if state < θ_inhibit  
    POISE     otherwise

where:

• θ_excite ∈ U(0.15, 0.35) (random initialization)

• θ_inhibit ∈ U(-0.25, -0.05) (random initialization)

1.3 Hebbian Learning Update

correlation = state_self × state_neighbor
Δw = learning_rate × stress_mod × correlation × (1 - min(1.0, |w|/2.0))
w_new = clamp(w + Δw, -2.0, 2.0)

where:

• learning_rate = 0.004

• stress_mod = 1.0 - 0.6 × stress

• Dictionary limited to 24 strongest |w| values

1.4 Stress Dynamics

stress_t = 
    min(1.0, stress_{t-1} + 0.10)    if trinary state changed
    max(0.0, stress_{t-1} - 0.008)   otherwise

1.5 Fatigue Dynamics

fatigue_t =
    min(1.0, fatigue_{t-1} + 0.03)   if EXCITE state
    max(0.0, fatigue_{t-1} - 0.01)   if POISE state
    fatigue_{t-1}                    otherwise

1.6 Neuron Health Decay

health_t = max(0.1, health_{t-1} × (1 - decay_rate))
decay_rate = 0.00025 × (1.0 + stress + 0.7 × fatigue)

2. MÖBIUS INSTABILITY DETECTION

2.1 Flip Rate Calculation

For recent trinary states S = {s₁, s₂, ..., sₙ} (n ≤ 12):

flip_rate = (number of i where s_i ≠ s_{i-1}) / (n - 1)

2.2 Transition Zone

zone_min = 0.05 if stress < 0.5 else 0.02
zone_max = 0.65 if stress < 0.5 else 0.80
in_zone = zone_min < |state| < zone_max

2.3 Dwell Time

dwell = 1 + count consecutive identical trinary states in history

2.4 Detection Conditions

Möbius signature detected if:

in_zone AND (
    (flip_rate > 0.20 + 0.30 × stress) OR
    ((dwell < 4 AND stress > 0.60) OR (dwell > 10 AND flip_rate > 0.12)) OR
    (variance(recent_states) > 0.18 + 0.35 × stress)
)

3. DRONE DYNAMICS

3.1 Health Ratio

health_ratio = current_health / max_health

3.2 Integrity Score

base_integrity = 0.55 + 0.55 × health_ratio - 0.40 × stress
degradation_penalty = 0.25 × (comms_degraded + nav_degraded + compute_degraded)/3
integrity = clamp(base_integrity × (1 - degradation_penalty), 0, 1)

3.3 State-Derived Multipliers

EXCITE:    {speed: 1.35, accuracy: 1.10, damage: 1.25, cohesion: 0.85}
INHIBIT:   {speed: 0.78, accuracy: 0.92, damage: 0.75, cohesion: 1.35}
POISE:     {speed: 1.05, accuracy: 1.00, damage: 1.00, cohesion: 1.05}

4. COMBAT MECHANICS

4.1 Attack Accuracy

swarm_accuracy_boost = min(0.3, swarm_density × 0.1)
degradation_penalty = 0.55 × (0.5 × comms_degraded + 0.5 × compute_degraded)
degradation_factor = max(0.15, 1.0 - degradation_penalty)
range_factor = max(0.1, 1.0 - distance/combat_range)
final_accuracy = accuracy × degradation_factor × range_factor × accuracy_multiplier × (1 + swarm_accuracy_boost)

4.2 Attack Damage

swarm_damage_boost = min(0.5, swarm_density × 0.2)
damage = base_damage × damage_multiplier × (1 + swarm_damage_boost)

4.3 Movement Calculation

dx_target = target_x - current_x
dy_target = target_y - current_y
distance_target = √(dx_target² + dy_target²)

// Swarm cohesion
avg_x = Σ(neighbor_x)/n
avg_y = Σ(neighbor_y)/n
dx_swarm = avg_x - current_x
dy_swarm = avg_y - current_y
distance_swarm = √(dx_swarm² + dy_swarm²)
adjustment = (distance_swarm - desired_radius)/desired_radius
dx_swarm = (dx_swarm/distance_swarm) × adjustment × cohesion_preference
dy_swarm = (dy_swarm/distance_swarm) × adjustment × cohesion_preference

// Final movement
dx = dx_target/distance_target + dx_swarm × 0.30
dy = dy_target/distance_target + dy_swarm × 0.30
norm = √(dx² + dy²)
dx_final = dx/norm + nav_noise
dy_final = dy/norm - nav_noise
nav_noise ~ N(0, 0.08 × nav_degraded)

5. MISSION STIMULUS GENERATION

5.1 Mission Phase Determination

phase_time_ratio = current_step / total_steps
MissionPhase = 
    DEPLOY   if phase_time_ratio < 0.20
    RECON    if phase_time_ratio < 0.50
    EXECUTE  if phase_time_ratio < 0.75
    EVASIVE  if phase_time_ratio < 0.95
    RETURN   otherwise

5.2 Stimulus Parameters by Phase

Phase            Base Stimulus Range    Correlation (ρ)
DEPLOY           [-0.1, 0.2]           U(0.3, 0.7)
RECON            [-0.2, 0.4]           U(0.3, 0.7)
EXECUTE          [-0.4, 0.6]           U(0.3, 0.7)
EVASIVE          [-0.6, 0.8]           U(0.3, 0.7)
RETURN           [-0.1, 0.1]           U(0.3, 0.7)

5.3 Individual Drone Stimulus

base_stim ~ U(phase_low, phase_high)
individual_noise ~ N(0, 0.2)
correlated_noise ~ N(0, 0.1) × ρ
mission_input = base_stim + individual_noise + correlated_noise + 0.3 × stress
mission_input = clamp(mission_input, 0, 1)
degraded_input = mission_input × (1 - 0.4 × mean_degradation)

6. SWARM INFLUENCE CALCULATION

6.1 Neighbor Weighting

For each neighbor within 50.0 units:

distance = √((x_self - x_neighbor)² + (y_self - y_neighbor)²)
distance_weight = exp(-distance/30.0)
stress_modifier = 1.0 - 0.5 × (stress_self + stress_neighbor)
weight = distance_weight × max(0.1, stress_modifier) × integrity_neighbor

6.2 Swarm Input

swarm_input = Σ(weight_i × state_neighbor_i) / Σ(weight_i)

7. ADVERSARIAL EFFECTS MODEL

7.1 Degradation Natural Recovery

comms_degraded_t   = max(0, comms_degraded_{t-1} - 0.03)
nav_degraded_t     = max(0, nav_degraded_{t-1} - 0.02)
compute_degraded_t = max(0, compute_degraded_{t-1} - 0.04)
malware_risk_t     = max(0, malware_risk_{t-1} - 0.01)

7.2 Event-Induced Degradation

event_pressure = Σ(event_intensity for active events targeting drone)

JAMMING:     comms_degraded += 0.15 × intensity
GPS_SPOOF:   nav_degraded += 0.18 × intensity
DDOS:        compute_degraded += 0.20 × intensity
MALWARE:     malware_risk += 0.12 × intensity

7.3 Stress Coupling

stress += 0.06 × event_pressure
health_wear = 0.0006 × (1.0 + stress + 0.7 × event_pressure)
health *= (1 - health_wear)

8. INTERVENTION DECISION LOGIC

8.1 Decision Thresholds

health_ratio = health/max_health
mean_degradation = (comms_degraded + nav_degraded + compute_degraded)/3
multi_vector = (mean_degradation > 0.35) AND (comms_degraded > 0.3 AND nav_degraded > 0.3)

Intervention triggered when:
EMERGENCY:   health_ratio < 0.35 AND stress > 0.70 AND (multi_vector OR compute_degraded > 0.5)
INOCULATE:   malware_risk > 0.45
NAVIGATE:    nav_degraded > 0.35 OR comms_degraded > 0.40
FORK:        Möbius_signature AND stress > 0.45

8.2 Intervention Effectiveness

base_effectiveness = 0.82 + 0.10 × health_ratio
stress_boost = 0.08 × (1.0 - stress)
effectiveness = clamp(base_effectiveness + stress_boost, 0.80, 0.98)

9. SOVEREIGNTY METRICS

9.1 Component Calculations

For sample of N drones (N ≤ min(300, total_alive)):

stability = max(0, 1 - 0.5 × mean(|states|))
modularity = max(0, 1 - 2.0 × variance(health_ratios))
containment = max(0, 1 - [0.05 + 0.2 × mean(stress) + 0.1 × (1 - mean(health_ratios))])
consistency = max(0, 1 - 2.0 × mean(flip_rates))

// Event penalty adjustment
event_penalty = mean(event_intensities)
stability *= (1 - 0.3 × event_penalty)
containment *= (1 - 0.2 × event_penalty)

9.2 Boundary Score

boundary_score = 0.30 × stability + 
                 0.25 × modularity + 
                 0.25 × containment + 
                 0.20 × consistency
boundary_score = clamp(boundary_score, 0, 1)

9.3 Value Extraction Risk

has_object_capabilities = (boundary_score > 0.5) AND (mean(health_ratios) > 0.4)
value_extraction = 0.001 if has_object_capabilities else 0.05 + (1 - mean(health_ratios)) × 0.1

9.4 Trust Score

interventions_effective = count(drones with last_intervention ≠ None)
trust_score = min(1.0, 0.5 + interventions_effective/(2.0 × max(1, N)))

9.5 Sovereignty Condition

combat_experience = mean(engagements_per_drone)
is_sovereign = has_object_capabilities AND 
               boundary_score ≥ 0.70 AND
               value_extraction ≤ 0.015625 AND
               mean(health_ratios) > 0.3 AND
               combat_experience > 2 AND
               alive_swarm > 0.5 × initial_swarm_count

10. SPATIAL GRID OPTIMIZATION

10.1 Grid Cell Mapping

cell_x = floor(position_x / grid_cell_size)  // grid_cell_size = 50.0
cell_y = floor(position_y / grid_cell_size)
cell_key = (cell_x, cell_y)

10.2 Neighborhood Search

Check 3×3 cell region around target cell, then filter by Euclidean distance.

11. VICTORY CONDITIONS

11.1 Swarm Victory

all(command_hubs destroyed)

11.2 Defense Victory

alive_swarm < 0.10 × initial_swarm_count OR no_swarm_alive

11.3 Sovereignty Victory

consecutive_sovereign_steps ≥ 20 AND alive_swarm > 0.5 × initial_swarm_count

12. STOCHASTIC ELEMENTS

12.1 Random Initializations

• Drone speeds: U(250, 500) for swarm, U(400, 700) for elites

• Accuracy: U(0.25, 0.45) for swarm, U(0.65, 0.85) for elites

• Damage: U(30, 90) for swarm, U(180, 320) for elites

• Health: U(8, 25) for swarm, U(120, 280) for elites

• Combat range: U(10, 35) for swarm, U(80, 140) for elites

12.2 Target Selection Noise

noise_factor = U(0.85, 1.15) × (1.0 + 0.35 × compute_degraded)

13. PERFORMANCE OPTIMIZATIONS

1. Limited Neighbors: Maximum 14 neighbors considered for swarm influence

2. Bounded Hebbian Weights: Only 24 strongest weights retained

3. State History: Limited to 12 recent states for flip rate calculation

4. Spatial Grid: O(1) neighbor lookup via grid cell indexing

5. Periodic Grid Updates: Grid rebuilt every 10 simulation steps

14. REPRODUCIBILITY NOTES

1. All random operations use Python’s random module with default seeding

2. Gaussian noise uses random.normalvariate()

3. Numerical stability ensured via clipping operations

4. Deterministic given same random seed initialization

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METHODOLOGY SUMMARY: This simulation implements a hybrid neuro-symbolic AI system where individual drones maintain continuous internal states mapped to discrete behavioral roles. Coordination emerges through local interactions modulated by distance-weighted influence, mission context, and adversarial pressures. The system demonstrates emergent resilience through distributed intervention mechanisms and collective sovereignty metrics.

Until next time, TTFN…