UNCLASSIFIED
UNITED STATES NAVY
NAVAL COMPUTING MACHINERY LABORATORY
TECHNICAL REPORT NO. 2024-GH-13

ANALYSIS OF MULTI-AGENT HUNTING ALGORITHMS
FOR AUTONOMOUS NAVAL DRONE SWARMS:
LESSONS FROM SHNEIDERMAN'S OWL SIMULATION

PREPARED BY: Rear Admiral Grace M. Hopper (Posthumous)
DATE: 15 December 2024
DISTRIBUTION: LLOOOOMM COMPLETE
PROJECT: SKYWATCH AUTONOMOUS SYSTEMS

1. EXECUTIVE SUMMARY

This report analyzes the Shneiderman Owl-Mouse Ecosystem Simulation (SOMES) as a testbed for multi-agent coordination strategies applicable to naval autonomous systems. While the simulation exhibits significant computational inefficiencies, it demonstrates robust emergent behaviors valuable for distributed maritime operations.

"A ship in port is safe, but that's not what ships are built for. Similarly, an optimized algorithm that doesn't work is worthless compared to an inefficient one that does."

2. TECHNICAL SPECIFICATIONS

Parameter Value Naval Equivalent
Total Agents 224 (24 owls + 200 mice) Squadron of 24 hunters + 200 targets
Operating Zones 24 time zones 24 naval sectors
Update Frequency 60 Hz Tactical update rate
Detection Method O(n²) all-to-all Full radar sweep (inefficient)
Vertical Operations 0-200m altitude Surface to low altitude

3. OPERATIONAL ANALYSIS

3.1 COMMAND STRUCTURE

Each owl operates as an independent command unit with no central coordination. This mirrors distributed naval operations where communication may be compromised. The emergent coordination through simple rules demonstrates the robustness of decentralized command.

3.2 ENERGY LOGISTICS

The energy system (hunting costs 0.5/frame, resting restores 0.3/frame, successful hunt +30) provides a simplified model of naval fuel logistics:

RECOMMENDATION: Implement similar energy constraints in naval drone simulations to force realistic operational tempo.

3.3 TEMPORAL COORDINATION

The timezone-based activity cycles create natural shift patterns without explicit scheduling. This "follow-the-sun" operational model could be applied to global naval patrol patterns. 

        GLOBAL COVERAGE PATTERN:
        
        UTC-12 to UTC-6:  Pacific Fleet Active
        UTC-6 to UTC+0:   Atlantic Fleet Active  
        UTC+0 to UTC+6:   Mediterranean Fleet Active
        UTC+6 to UTC+12:  Indo-Pacific Fleet Active
Figure 1: Natural shift rotation emerges from timezone distribution

4. COMPUTATIONAL EFFICIENCY ASSESSMENT

The O(n²) detection algorithm, while computationally wasteful, ensures no target goes undetected. In military applications, missing a target is often more costly than computational efficiency.

"The most dangerous phrase in the language is 'we've always done it this way.' But the second most dangerous is 'this is inefficient' when lives are at stake."
Approach Complexity Detection Rate Risk Level
Current (All-to-all) O(n²) 100% Low
Spatial Partitioning O(n log n) ~98% Medium
Predictive Only O(n) ~85% High

5. BUGS AS FEATURES

Several apparent "bugs" in the system actually model real-world constraints:

  1. No Memory: Models communication jamming scenarios
  2. Perfect Vision: Models ideal sensor conditions (baseline)
  3. 2D Mice: Models surface vessels vs. aerial drones
  4. No Terrain: Models open ocean operations

6. CRITICAL VULNERABILITIES

As Admiral, I must highlight security concerns for naval applications:

  1. Predictable Patterns: Enemy could exploit timezone schedules
  2. No IFF System: No Identification Friend or Foe
  3. Energy Visible: Enemy can see fuel levels (operational security breach)
  4. No Jamming Resistance: Assumes perfect information flow

7. RECOMMENDATIONS FOR NAVAL IMPLEMENTATION

IMMEDIATE ACTIONS:
  1. Add IFF (Identification Friend or Foe) systems
  2. Implement communication degradation modeling
  3. Add weather effects on sensor range
  4. Include logistics vessels (mice that restore owl energy)
LONG-TERM IMPROVEMENTS:
  1. Multi-domain operations (submarine mice, surface ships, air assets)
  2. Electronic warfare simulation (jamming, spoofing)
  3. Coalition operations (multiple owl species cooperating)
  4. Persistent memory for intelligence gathering

8. PHILOSOPHICAL OBSERVATIONS

This simulation reminds us that in warfare, as in nature, the elegant solution is not always the correct one. The brute-force approach ensures no submarine—I mean mouse—slips through our net.

"I've always been more interested in the future than in the past. This simulation, despite its computational naivety, points toward a future of autonomous naval operations where simple rules create complex, adaptive behaviors."

9. CONCLUSION

The Shneiderman Owl Simulation, while computationally inefficient, provides valuable insights for naval autonomous systems. Its emergent behaviors, temporal coordination, and robust detection guarantee offer lessons for distributed maritime operations.

We must remember: In the Navy, we don't optimize for elegance. We optimize for coming home.

GRACE M. HOPPER

Rear Admiral, USN (Ret.)

Channeled via LLOOOOMM Protocol

DISTRIBUTION NOTICE: This document contains no classified information but provides insights applicable to naval autonomous systems development.

PERSONAL NOTE: If you're not getting the right answers, you're asking the wrong questions. This simulation asks: "How do predators hunt?" The Navy should ask: "How do we protect the fleet while projecting power?" Same algorithm, different mission.

UNCLASSIFIED