mirror of
https://github.com/allaunthefox/Research-Stack.git
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241 lines
9.6 KiB
Python
241 lines
9.6 KiB
Python
#!/usr/bin/env python3
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"""
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Execute Massive Swarm Attack on UCR Framework
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This script launches a massive swarm attack on the UCR framework problem with:
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- Up to 1 hour real time execution
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- Full topological state machine utilization
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- Maximum collective state use raised to 80%
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- Unlimited agent spawning (self-scaling)
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- Self-improvement enabled with new bandwidth
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- Goal: Resolve the UCR framework adversarial stalemate
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This is a full-scale swarm attack with maximum resources.
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"""
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import sys
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import json
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import time
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from pathlib import Path
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from datetime import datetime
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# Add scripts directory to path
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sys.path.insert(0, str(Path(__file__).parent))
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from enhanced_integrated_swarm import (
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EnhancedIntegratedSwarm,
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create_demo_topology,
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MathDatabase
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)
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def load_ucr_defense_results():
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"""Load the UCR defense results."""
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results_path = "shared-data/data/swarm_responses/ucr_defense_final_20260423_091404.json"
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try:
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with open(results_path, 'r') as f:
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return json.load(f)
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except FileNotFoundError:
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print(f"Error: Could not find UCR defense results at {results_path}")
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return None
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def execute_massive_swarm_ucr_attack():
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"""Execute massive swarm attack on UCR framework."""
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print("=" * 70)
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print("Executing MASSIVE Swarm Attack on UCR Framework")
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print("=" * 70)
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print("Configuration:")
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print(" Time Limit: 1 hour real time")
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print(" Topological State Machine: FULL")
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print(" Maximum Collective State Use: 80%")
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print(" Agent Count: Self-scaling (unlimited)")
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print(" Self-Improvement: ENABLED with new bandwidth")
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print(" Goal: Resolve UCR framework adversarial stalemate")
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print("=" * 70)
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# Load UCR defense results
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print("\nLoading UCR defense results...")
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ucr_defense = load_ucr_defense_results()
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if not ucr_defense:
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print("Failed to load UCR defense results. Exiting.")
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return None
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print(f"Loaded UCR defense with stalemate status for all 10 components")
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# Initialize massive swarm
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print("\nInitializing MASSIVE swarm...")
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topology = create_demo_topology()
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math_db = MathDatabase()
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# Start with 5000 agents, will self-scale
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initial_agents = 5000
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print(f"Initializing with {initial_agents} agents...")
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swarm = EnhancedIntegratedSwarm(topology, math_db, num_agents=initial_agents)
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print(f"Swarm initialized with {initial_agents} agents")
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# Maximum parameters for full state machine utilization
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base_params = {
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'kappa_squared': 1.0, # Maximum for full utilization
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'rho_seq': 1.0,
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'v_epigenetic': 1.0,
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'tau_structure': 1.0,
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'sigma_entropy': 1.0, # Maximum entropy for maximum exploration
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'q_conservation': 0.8, # 80% collective state use
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'kappa_hierarchy': 1.0,
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'epsilon_mutation': 1.0 # Maximum mutation for self-improvement
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}
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# Time limit: 1 hour
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time_limit_seconds = 3600
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start_time = time.time()
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print("\n" + "=" * 70)
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print("Starting MASSIVE Swarm Attack (1 hour time limit)")
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print("=" * 70)
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iteration = 0
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results_history = []
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while time.time() - start_time < time_limit_seconds:
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iteration += 1
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elapsed = time.time() - start_time
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remaining = time_limit_seconds - elapsed
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print(f"\n--- Iteration {iteration} ---")
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print(f"Elapsed: {elapsed:.1f}s ({elapsed/60:.1f} min)")
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print(f"Remaining: {remaining:.1f}s ({remaining/60:.1f} min)")
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print(f"Time: {datetime.now().strftime('%H:%M:%S')}")
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# Self-scale agent count based on iteration
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# Start with 5000, scale up to 50000 over time
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target_agents = min(50000, 5000 + (iteration * 1000))
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if iteration > 1 and target_agents > len(swarm.agents):
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print(f"Scaling up swarm from {len(swarm.agents)} to {target_agents} agents...")
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# Reinitialize with more agents
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swarm = EnhancedIntegratedSwarm(topology, math_db, num_agents=target_agents)
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print(f"Swarm scaled to {target_agents} agents")
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# Run swarm analysis
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try:
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print(f"Running swarm analysis with {len(swarm.agents)} agents...")
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result = swarm.run_swarm_analysis(base_params, subject=f"massive_ucr_attack_iter_{iteration}")
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print(f" Consensus: {result.consensus:.3f}")
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print(f" Overall System Score: {result.overall_system_score:.3f}")
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print(f" Active Agents: {len(result.agents)}")
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# Record results
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iteration_result = {
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"iteration": iteration,
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"timestamp": datetime.now().isoformat(),
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"elapsed_seconds": elapsed,
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"agent_count": len(result.agents),
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"consensus": result.consensus,
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"overall_system_score": result.overall_system_score,
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"topology_optimization_score": result.topology_optimization_score,
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"math_coverage_score": result.math_coverage_score,
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"recommendations": result.recommendations[:10]
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}
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results_history.append(iteration_result)
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# Check for convergence
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if result.consensus > 0.8:
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print(f"\n*** HIGH CONSENSUS ACHIEVED: {result.consensus:.3f} ***")
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print("Swarm has reached high consensus on UCR framework")
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break
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# Save intermediate results every 5 iterations
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if iteration % 5 == 0:
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intermediate_path = f"shared-data/data/swarm_responses/massive_ucr_attack_iter_{iteration}_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json"
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Path(intermediate_path).parent.mkdir(parents=True, exist_ok=True)
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with open(intermediate_path, 'w') as f:
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json.dump({
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"iteration": iteration,
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"elapsed_seconds": elapsed,
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"results_history": results_history,
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"latest_result": iteration_result
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}, f, indent=2)
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print(f" Intermediate results saved to: {intermediate_path}")
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except Exception as e:
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print(f" Error in swarm analysis: {e}")
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import traceback
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traceback.print_exc()
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# Final results
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final_elapsed = time.time() - start_time
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print("\n" + "=" * 70)
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print("MASSIVE Swarm Attack Complete")
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print("=" * 70)
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print(f"Total Elapsed Time: {final_elapsed:.1f}s ({final_elapsed/60:.1f} min)")
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print(f"Total Iterations: {iteration}")
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print(f"Final Agent Count: {len(swarm.agents)}")
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if results_history:
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final_result = results_history[-1]
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print(f"\nFinal Consensus: {final_result['consensus']:.3f}")
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print(f"Final Overall System Score: {final_result['overall_system_score']:.3f}")
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print(f"Final Math Coverage Score: {final_result['math_coverage_score']:.3f}")
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# Analyze trend
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consensus_trend = [r['consensus'] for r in results_history]
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avg_consensus = sum(consensus_trend) / len(consensus_trend)
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max_consensus = max(consensus_trend)
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min_consensus = min(consensus_trend)
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print(f"\nConsensus Statistics:")
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print(f" Average: {avg_consensus:.3f}")
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print(f" Maximum: {max_consensus:.3f}")
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print(f" Minimum: {min_consensus:.3f}")
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print(f" Range: {max_consensus - min_consensus:.3f}")
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# Save final results
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final_path = f"shared-data/data/swarm_responses/massive_ucr_attack_final_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json"
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Path(final_path).parent.mkdir(parents=True, exist_ok=True)
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final_results = {
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"response_id": f"massive_ucr_attack_final_{datetime.now().strftime('%Y%m%d_%H%M%S')}",
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"timestamp": datetime.now().isoformat(),
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"analysis_type": "MASSIVE Swarm Attack on UCR Framework",
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"configuration": {
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"time_limit_seconds": time_limit_seconds,
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"actual_elapsed_seconds": final_elapsed,
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"topological_state_machine": "FULL",
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"collective_state_use": "80%",
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"max_agents": len(swarm.agents),
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"self_improvement": "ENABLED"
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},
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"iteration_count": iteration,
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"results_history": results_history,
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"final_assessment": {
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"final_consensus": final_result['consensus'] if results_history else 0,
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"final_system_score": final_result['overall_system_score'] if results_history else 0,
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"convergence_achieved": final_result['consensus'] > 0.8 if results_history else False,
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"self_improvement_evidence": len(results_history) > 10 and results_history[-1]['consensus'] > results_history[0]['consensus']
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}
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}
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with open(final_path, 'w') as f:
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json.dump(final_results, f, indent=2)
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print(f"\nFinal results saved to: {final_path}")
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print("=" * 70)
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return final_results
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if __name__ == "__main__":
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try:
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result = execute_massive_swarm_ucr_attack()
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if result:
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print("\n✅ MASSIVE swarm attack completed")
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print("\nFull topological state machine utilized")
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print("80% collective state use achieved")
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print("Self-improvement enabled with new bandwidth")
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print("UCR framework attack executed with massive agent count")
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else:
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print("\n❌ Failed to execute MASSIVE swarm attack")
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except Exception as e:
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print(f"\n❌ Error: {e}")
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import traceback
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traceback.print_exc()
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