Swarm Learning

The swarm implements continuous learning through:

1. Performance Feedback Loops: Each decision's outcome updates agent weights

2. Emergent Specialization: Agents evolve specialized roles based on performance

3. Adaptive Coordination: Communication patterns evolve based on effectiveness

// Update agent weights based on performance
function updateSwarmWeights(swarm, performanceResults) {
  const agents = swarm.getAgents();
  const currentWeights = swarm.getAgentWeights();
  const newWeights = {};
  
  // Calculate performance score for each agent
  const performanceScores = {};
  
  for (const agent of agents) {
    const agentId = agent.getId();
    
    // Get agent's signals that contributed to decisions
    const agentSignals = performanceResults.contributingSignals.filter(
      signal => signal.agentId === agentId
    );
    
    if (agentSignals.length === 0) {
      performanceScores[agentId] = 0;
      continue;
    }
    
    // Calculate accuracy score
    const correctSignals = agentSignals.filter(signal => signal.wasCorrect);
    const accuracy = correctSignals.length / agentSignals.length;
    
    // Calculate profit contribution
    const profitContribution = agentSignals.reduce(
      (sum, signal) => sum + (signal.profitContribution || 0),
      0
    );
    
    // Combined performance score (accuracy + profit weighted)
    performanceScores[agentId] = (accuracy * 0.4) + (normalizeValue(profitContribution) * 0.6);
  }
  
  // Apply softmax to convert scores to weights
  const totalScore = Object.values(performanceScores).reduce(
    (sum, score) => sum + Math.exp(score * 5),
    0
  );
  
  for (const agentId in performanceScores) {
    // New weight based on exponential of performance score
    const newWeight = Math.exp(performanceScores[agentId] * 5) / totalScore;
    
    // Blend with existing weight for stability (80% new, 20% old)
    newWeights[agentId] = (newWeight * 0.8) + (currentWeights[agentId] * 0.2);
  }
  
  // Update swarm with new weights
  swarm.updateAgentWeights(newWeights);
  
  return {
    previousWeights: currentWeights,
    newWeights,
    performanceScores
  };
}

// Helper function to normalize values to 0-1 range
function normalizeValue(value, min = -1, max = 1) {
  return (value - min) / (max - min);
}

// Example usage
const weightUpdateResults = updateSwarmWeights(marketSwarm, latestPerformance);
console.log('Weight updates:', weightUpdateResults);

Practical Implementation

// Complete swarm implementation example
async function runMarketSwarm() {
  // Initialize swarm
  const marketSwarm = new AgentSwarm({
    name: 'Crypto Market Analysis Swarm',
    objective: 'maximum_risk_adjusted_return',
    communicationProtocol: 'message_passing',
    consensusMechanism: 'weighted_voting',
    adaptiveLearning: true
  });
  
  // Add specialized agents
  marketSwarm.addAgent(new MomentumAgent(), { weight: 0.25 });
  marketSwarm.addAgent(new SentimentAgent(), { weight: 0.20 });
  marketSwarm.addAgent(new CorrelationAgent(), { weight: 0.20 });
  marketSwarm.addAgent(new VolatilityAgent(), { weight: 0.15 });
  marketSwarm.addAgent(new FundamentalsAgent(), { weight: 0.20 });
  
  // Initialize swarm
  await marketSwarm.initialize();
  
  // Get current market data
  const marketData = await dataProvider.getMarketData({
    assets: ['BTC', 'ETH', 'SOL', 'AVAX', 'LINK'],
    timeframes: ['1h', '4h', '1d'],
    metrics: ['price', 'volume', 'sentiment', 'on-chain']
  });
  
  // Get swarm analysis
  const swarmAnalysis = await composeSwarmSignals(marketSwarm, marketData);
  
  // Refine the initial consensus
  const refinedAnalysis = await refineSwarmDecision(
    marketSwarm,
    swarmAnalysis.consensusResults
  );
  
  // Generate trading decisions based on refined consensus
  const tradingDecisions = marketSwarm.generateTradingDecisions(
    refinedAnalysis.finalConsensus,
    {
      minimumConfidence: 0.7,
      riskManagement: riskConfig
    }
  );
  
  // Execute trades if approved
  if (tradingDecisions.approved) {
    const executionResults = await executeTrades(tradingDecisions.trades);
    console.log('Trade execution results:', executionResults);
    
    // Update swarm weights based on previous performance
    const latestPerformance = await getPerformanceMetrics(marketSwarm);
    updateSwarmWeights(marketSwarm, latestPerformance);
  }
  
  return {
    analysis: refinedAnalysis,
    decisions: tradingDecisions
  };
}

// Run the swarm on a schedule
setInterval(runMarketSwarm, 4 * 60 * 60 * 1000);  // Every 4 hours

This swarm intelligence framework enables INTUE to harness the collective wisdom of specialized agents, creating a market analysis system greater than the sum of its parts.