ruv-swarm
Version:
High-performance neural network swarm orchestration in WebAssembly
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JavaScript
/**
* Neural Network Manager
* Manages per-agent neural networks with WASM integration
*/
import { createNeuralModel, MODEL_PRESETS } from './neural-models/index.js';
import {
NEURAL_PRESETS,
getPreset,
getCategoryPresets,
searchPresetsByUseCase,
getRecommendedPreset,
validatePresetConfig,
} from './neural-models/presets/index.js';
import {
COMPLETE_NEURAL_PRESETS,
CognitivePatternSelector,
NeuralAdaptationEngine,
} from './neural-models/neural-presets-complete.js';
import { CognitivePatternEvolution } from './cognitive-pattern-evolution.js';
import { MetaLearningFramework } from './meta-learning-framework.js';
import { NeuralCoordinationProtocol } from './neural-coordination-protocol.js';
import { DAACognition } from './daa-cognition.js';
class NeuralNetworkManager {
constructor(wasmLoader) {
this.wasmLoader = wasmLoader;
this.neuralNetworks = new Map();
// Enhanced capabilities
this.cognitiveEvolution = new CognitivePatternEvolution();
this.metaLearning = new MetaLearningFramework();
this.coordinationProtocol = new NeuralCoordinationProtocol();
this.daaCognition = new DAACognition();
// Complete neural presets integration
this.cognitivePatternSelector = new CognitivePatternSelector();
this.neuralAdaptationEngine = new NeuralAdaptationEngine();
// Cross-agent memory and knowledge sharing
this.sharedKnowledge = new Map();
this.agentInteractions = new Map();
this.collaborativeMemory = new Map();
// Performance tracking and optimization
this.performanceMetrics = new Map();
this.adaptiveOptimization = true;
this.federatedLearningEnabled = true;
this.templates = {
deep_analyzer: {
layers: [128, 256, 512, 256, 128],
activation: 'relu',
output_activation: 'sigmoid',
dropout: 0.3,
},
nlp_processor: {
layers: [512, 1024, 512, 256],
activation: 'gelu',
output_activation: 'softmax',
dropout: 0.4,
},
reinforcement_learner: {
layers: [64, 128, 128, 64],
activation: 'tanh',
output_activation: 'linear',
dropout: 0.2,
},
pattern_recognizer: {
layers: [256, 512, 1024, 512, 256],
activation: 'relu',
output_activation: 'sigmoid',
dropout: 0.35,
},
time_series_analyzer: {
layers: [128, 256, 256, 128],
activation: 'lstm',
output_activation: 'linear',
dropout: 0.25,
},
transformer_nlp: {
modelType: 'transformer',
preset: 'base',
dimensions: 512,
heads: 8,
layers: 6,
},
cnn_vision: {
modelType: 'cnn',
preset: 'cifar10',
inputShape: [32, 32, 3],
outputSize: 10,
},
gru_sequence: {
modelType: 'gru',
preset: 'text_classification',
hiddenSize: 256,
numLayers: 2,
bidirectional: true,
},
autoencoder_compress: {
modelType: 'autoencoder',
preset: 'mnist_compress',
bottleneckSize: 32,
variational: false,
},
gnn_social: {
modelType: 'gnn',
preset: 'social_network',
nodeDimensions: 128,
numLayers: 3,
},
resnet_classifier: {
modelType: 'resnet',
preset: 'resnet18',
inputDimensions: 784,
outputDimensions: 10,
},
vae_generator: {
modelType: 'vae',
preset: 'mnist_vae',
latentDimensions: 20,
betaKL: 1.0,
},
lstm_sequence: {
modelType: 'lstm',
preset: 'sentiment_analysis',
hiddenSize: 256,
numLayers: 2,
bidirectional: true,
},
// Special template for preset-based models
preset_model: {
modelType: 'preset', // Will be overridden by actual model type
usePreset: true,
},
// Advanced neural architectures (27+ models)
attention_mechanism: {
modelType: 'attention',
preset: 'multi_head_attention',
heads: 8,
dimensions: 512,
dropoutRate: 0.1,
},
diffusion_model: {
modelType: 'diffusion',
preset: 'denoising_diffusion',
timesteps: 1000,
betaSchedule: 'cosine',
},
neural_ode: {
modelType: 'neural_ode',
preset: 'continuous_dynamics',
solverMethod: 'dopri5',
tolerance: 1e-6,
},
capsule_network: {
modelType: 'capsnet',
preset: 'dynamic_routing',
primaryCaps: 32,
digitCaps: 10,
},
spiking_neural: {
modelType: 'snn',
preset: 'leaky_integrate_fire',
neuronModel: 'lif',
threshold: 1.0,
},
graph_attention: {
modelType: 'gat',
preset: 'multi_head_gat',
attentionHeads: 8,
hiddenUnits: 256,
},
neural_turing: {
modelType: 'ntm',
preset: 'differentiable_memory',
memorySize: [128, 20],
controllerSize: 100,
},
memory_network: {
modelType: 'memnn',
preset: 'end_to_end_memory',
memorySlots: 100,
hops: 3,
},
neural_cellular: {
modelType: 'nca',
preset: 'growing_patterns',
channels: 16,
updateRule: 'sobel',
},
hypernetwork: {
modelType: 'hypernet',
preset: 'weight_generation',
hyperDim: 512,
targetLayers: ['conv1', 'conv2'],
},
meta_learning: {
modelType: 'maml',
preset: 'few_shot_learning',
innerLR: 0.01,
outerLR: 0.001,
innerSteps: 5,
},
neural_architecture_search: {
modelType: 'nas',
preset: 'differentiable_nas',
searchSpace: 'mobile_search_space',
epochs: 50,
},
mixture_of_experts: {
modelType: 'moe',
preset: 'sparse_expert_routing',
numExperts: 8,
expertCapacity: 2,
},
neural_radiance_field: {
modelType: 'nerf',
preset: '3d_scene_reconstruction',
positionEncoding: 10,
directionEncoding: 4,
},
wavenet_audio: {
modelType: 'wavenet',
preset: 'speech_synthesis',
dilationChannels: 32,
residualChannels: 32,
},
pointnet_3d: {
modelType: 'pointnet',
preset: 'point_cloud_classification',
pointFeatures: 3,
globalFeatures: 1024,
},
neural_baby_ai: {
modelType: 'baby_ai',
preset: 'instruction_following',
vocabSize: 100,
instructionLength: 20,
},
world_model: {
modelType: 'world_model',
preset: 'environment_prediction',
visionModel: 'vae',
memoryModel: 'mdn_rnn',
},
flow_based: {
modelType: 'normalizing_flow',
preset: 'density_estimation',
flowType: 'real_nvp',
couplingLayers: 8,
},
energy_based: {
modelType: 'ebm',
preset: 'contrastive_divergence',
energyFunction: 'mlp',
samplingSteps: 100,
},
neural_processes: {
modelType: 'neural_process',
preset: 'function_approximation',
latentDim: 128,
contextPoints: 10,
},
set_transformer: {
modelType: 'set_transformer',
preset: 'permutation_invariant',
inducingPoints: 32,
dimensions: 128,
},
neural_implicit: {
modelType: 'neural_implicit',
preset: 'coordinate_networks',
coordinateDim: 2,
hiddenLayers: 8,
},
evolutionary_neural: {
modelType: 'evolutionary_nn',
preset: 'neuroevolution',
populationSize: 50,
mutationRate: 0.1,
},
quantum_neural: {
modelType: 'qnn',
preset: 'variational_quantum',
qubits: 4,
layers: 6,
},
optical_neural: {
modelType: 'onn',
preset: 'photonic_computation',
wavelengths: 16,
modulators: 'mach_zehnder',
},
neuromorphic: {
modelType: 'neuromorphic',
preset: 'event_driven',
spikeEncoding: 'rate',
synapticModel: 'stdp',
},
};
// Store instances of new neural models
this.neuralModels = new Map();
}
async createAgentNeuralNetwork(agentId, config = {}) {
// Initialize cognitive evolution for this agent
await this.cognitiveEvolution.initializeAgent(agentId, config);
// Apply meta-learning if enabled
if (config.enableMetaLearning) {
config = await this.metaLearning.adaptConfiguration(agentId, config);
}
// Check if this is a new neural model type
const template = config.template || 'deep_analyzer';
const templateConfig = this.templates[template];
if (templateConfig && templateConfig.modelType) {
// Create new neural model with enhanced capabilities
return this.createAdvancedNeuralModel(agentId, template, config);
}
// Load neural module if not already loaded
const neuralModule = await this.wasmLoader.loadModule('neural');
if (!neuralModule || neuralModule.isPlaceholder) {
console.warn('Neural network module not available, using simulation');
return this.createSimulatedNetwork(agentId, config);
}
const {
layers = null,
activation = 'relu',
learningRate = 0.001,
optimizer = 'adam',
} = config;
// Use template or custom layers
const networkConfig = layers ? { layers, activation } : this.templates[template];
try {
// Create network using WASM module
const networkId = neuralModule.exports.create_neural_network(
JSON.stringify({
agent_id: agentId,
layers: networkConfig.layers,
activation: networkConfig.activation,
learning_rate: learningRate,
optimizer,
}),
);
const network = new NeuralNetwork(networkId, agentId, networkConfig, neuralModule);
this.neuralNetworks.set(agentId, network);
return network;
} catch (error) {
console.error('Failed to create neural network:', error);
return this.createSimulatedNetwork(agentId, config);
}
}
createSimulatedNetwork(agentId, config) {
const network = new SimulatedNeuralNetwork(agentId, config);
this.neuralNetworks.set(agentId, network);
return network;
}
async createAdvancedNeuralModel(agentId, template, customConfig = {}) {
const templateConfig = this.templates[template];
if (!templateConfig || !templateConfig.modelType) {
throw new Error(`Invalid template: ${template}`);
}
// Merge template config with custom config
const config = {
...templateConfig,
...customConfig,
};
// Select cognitive patterns based on model type and task
const taskContext = {
requiresCreativity: customConfig.requiresCreativity || false,
requiresPrecision: customConfig.requiresPrecision || false,
requiresAdaptation: customConfig.requiresAdaptation || false,
complexity: customConfig.complexity || 'medium',
};
const cognitivePatterns = this.cognitivePatternSelector.selectPatternsForPreset(
config.modelType,
template,
taskContext,
);
config.cognitivePatterns = cognitivePatterns;
// Use preset if specified
if (config.preset && MODEL_PRESETS[config.modelType]) {
const presetConfig = MODEL_PRESETS[config.modelType][config.preset];
Object.assign(config, presetConfig);
}
try {
// Create the neural model
const model = await createNeuralModel(config.modelType, config);
// Wrap in a compatible interface
const wrappedModel = new AdvancedNeuralNetwork(agentId, model, config);
// Enhanced registration with cognitive capabilities
this.neuralNetworks.set(agentId, wrappedModel);
this.neuralModels.set(agentId, model);
// Register with coordination protocol
await this.coordinationProtocol.registerAgent(agentId, wrappedModel);
// Initialize neural adaptation engine
await this.neuralAdaptationEngine.initializeAdaptation(agentId, config.modelType, template);
// Initialize performance tracking
this.performanceMetrics.set(agentId, {
creationTime: Date.now(),
modelType: config.modelType,
cognitivePatterns: cognitivePatterns || [],
adaptationHistory: [],
collaborationScore: 0,
});
console.log(`Created ${config.modelType} neural network for agent ${agentId} with enhanced cognitive capabilities`);
return wrappedModel;
} catch (error) {
console.error(`Failed to create advanced neural model: ${error}`);
return this.createSimulatedNetwork(agentId, config);
}
}
async fineTuneNetwork(agentId, trainingData, options = {}) {
const network = this.neuralNetworks.get(agentId);
if (!network) {
throw new Error(`No neural network found for agent ${agentId}`);
}
const {
epochs = 10,
batchSize = 32,
learningRate = 0.001,
freezeLayers = [],
enableCognitiveEvolution = true,
enableMetaLearning = true,
} = options;
// Apply cognitive pattern evolution during training
if (enableCognitiveEvolution) {
await this.cognitiveEvolution.evolvePatterns(agentId, trainingData);
}
// Apply meta-learning optimization
if (enableMetaLearning) {
const optimizedOptions = await this.metaLearning.optimizeTraining(agentId, options);
Object.assign(options, optimizedOptions);
}
// Enhanced training with adaptive optimization
const result = await network.train(trainingData, { epochs, batchSize, learningRate, freezeLayers });
// Update performance metrics
const metrics = this.performanceMetrics.get(agentId);
if (metrics) {
const adaptationResult = {
timestamp: Date.now(),
trainingResult: result,
cognitiveGrowth: await this.cognitiveEvolution.assessGrowth(agentId),
accuracy: result.accuracy || 0,
cognitivePatterns: metrics.cognitivePatterns,
performance: result,
insights: [],
};
metrics.adaptationHistory.push(adaptationResult);
// Record adaptation in neural adaptation engine
await this.neuralAdaptationEngine.recordAdaptation(agentId, adaptationResult);
}
return result;
}
async enableCollaborativeLearning(agentIds, options = {}) {
const {
strategy = 'federated',
syncInterval = 30000,
privacyLevel = 'high',
enableKnowledgeSharing = true,
enableCrossAgentEvolution = true,
} = options;
const networks = agentIds.map(id => this.neuralNetworks.get(id)).filter(n => n);
if (networks.length < 2) {
throw new Error('At least 2 neural networks required for collaborative learning');
}
// Create enhanced collaborative learning session
const session = {
id: `collab-${Date.now()}`,
networks,
agentIds,
strategy,
syncInterval,
privacyLevel,
active: true,
knowledgeGraph: new Map(),
evolutionTracker: new Map(),
coordinationMatrix: new Array(agentIds.length).fill(0).map(() => new Array(agentIds.length).fill(0)),
};
// Initialize neural coordination protocol
await this.coordinationProtocol.initializeSession(session);
// Enable cross-agent knowledge sharing
if (enableKnowledgeSharing) {
await this.enableKnowledgeSharing(agentIds, session);
}
// Enable cross-agent cognitive evolution
if (enableCrossAgentEvolution) {
await this.cognitiveEvolution.enableCrossAgentEvolution(agentIds, session);
}
// Start enhanced synchronization
if (strategy === 'federated') {
this.startFederatedLearning(session);
} else if (strategy === 'knowledge_distillation') {
this.startKnowledgeDistillation(session);
} else if (strategy === 'neural_coordination') {
this.startNeuralCoordination(session);
}
return session;
}
startFederatedLearning(session) {
const syncFunction = () => {
if (!session.active) {
return;
}
// Aggregate gradients from all networks
const gradients = session.networks.map(n => n.getGradients());
// Apply privacy-preserving aggregation
const aggregatedGradients = this.aggregateGradients(gradients, session.privacyLevel);
// Update all networks with aggregated gradients
session.networks.forEach(n => n.applyGradients(aggregatedGradients));
// Schedule next sync
setTimeout(syncFunction, session.syncInterval);
};
// Start synchronization
setTimeout(syncFunction, session.syncInterval);
}
aggregateGradients(gradients, privacyLevel) {
// Enhanced aggregation with cognitive pattern preservation
const aggregated = {};
const cognitiveWeights = this.cognitiveEvolution.calculateAggregationWeights(gradients);
// Privacy levels with advanced secure aggregation
let noise = 0;
let differentialPrivacy = false;
switch (privacyLevel) {
case 'high':
noise = 0.01;
differentialPrivacy = true;
break;
case 'medium':
noise = 0.005;
break;
case 'low':
noise = 0.001;
break;
}
// Cognitive-weighted gradient aggregation
gradients.forEach((grad, index) => {
const weight = cognitiveWeights[index] || (1 / gradients.length);
Object.entries(grad).forEach(([key, value]) => {
if (!aggregated[key]) {
aggregated[key] = 0;
}
let aggregatedValue = value * weight;
// Apply differential privacy if enabled
if (differentialPrivacy) {
const sensitivity = this.calculateSensitivity(key, gradients);
const laplacianNoise = this.generateLaplacianNoise(sensitivity, noise);
aggregatedValue += laplacianNoise;
} else {
aggregatedValue += (Math.random() - 0.5) * noise;
}
aggregated[key] += aggregatedValue;
});
});
return aggregated;
}
calculateSensitivity(parameterKey, gradients) {
// Calculate L1 sensitivity for differential privacy
const values = gradients.map(grad => Math.abs(grad[parameterKey] || 0));
return Math.max(...values) - Math.min(...values);
}
generateLaplacianNoise(sensitivity, epsilon) {
// Generate Laplacian noise for differential privacy
const scale = sensitivity / epsilon;
const u1 = Math.random();
const u2 = Math.random();
return scale * Math.sign(u1 - 0.5) * Math.log(1 - 2 * Math.abs(u1 - 0.5));
}
getNetworkMetrics(agentId) {
const network = this.neuralNetworks.get(agentId);
if (!network) {
return null;
}
return network.getMetrics();
}
saveNetworkState(agentId, filePath) {
const network = this.neuralNetworks.get(agentId);
if (!network) {
throw new Error(`No neural network found for agent ${agentId}`);
}
return network.save(filePath);
}
async loadNetworkState(agentId, filePath) {
const network = this.neuralNetworks.get(agentId);
if (!network) {
throw new Error(`No neural network found for agent ${agentId}`);
}
return network.load(filePath);
}
// ===============================
// PRESET INTEGRATION METHODS
// ===============================
/**
* Create a neural network from a production preset
* @param {string} agentId - Agent identifier
* @param {string} category - Preset category (nlp, vision, timeseries, graph)
* @param {string} presetName - Name of the preset
* @param {object} customConfig - Optional custom configuration overrides
*/
async createAgentFromPreset(agentId, category, presetName, customConfig = {}) {
// First check complete neural presets
const completePreset = COMPLETE_NEURAL_PRESETS[category]?.[presetName];
if (completePreset) {
return this.createAgentFromCompletePreset(agentId, category, presetName, customConfig);
}
try {
const preset = getPreset(category, presetName);
validatePresetConfig(preset);
console.log(`Creating ${agentId} from preset: ${preset.name}`);
console.log(`Expected performance: ${preset.performance.expectedAccuracy} accuracy in ${preset.performance.inferenceTime}`);
// Merge preset config with custom overrides
const config = {
...preset.config,
...customConfig,
modelType: preset.model,
presetInfo: {
category,
presetName,
name: preset.name,
description: preset.description,
useCase: preset.useCase,
performance: preset.performance,
},
};
return this.createAdvancedNeuralModel(agentId, 'preset_model', config);
} catch (error) {
console.error(`Failed to create agent from preset: ${error.message}`);
throw error;
}
}
/**
* Create a neural network from complete preset (27+ models)
* @param {string} agentId - Agent identifier
* @param {string} modelType - Model type (transformer, cnn, lstm, etc.)
* @param {string} presetName - Name of the preset
* @param {object} customConfig - Optional custom configuration overrides
*/
async createAgentFromCompletePreset(agentId, modelType, presetName, customConfig = {}) {
const preset = COMPLETE_NEURAL_PRESETS[modelType]?.[presetName];
if (!preset) {
throw new Error(`Complete preset not found: ${modelType}/${presetName}`);
}
console.log(`Creating ${agentId} from complete preset: ${preset.name}`);
console.log(`Expected performance: ${preset.performance.expectedAccuracy} accuracy in ${preset.performance.inferenceTime}`);
console.log(`Cognitive patterns: ${preset.cognitivePatterns.join(', ')}`);
// Get optimized cognitive patterns
const taskContext = {
requiresCreativity: customConfig.requiresCreativity || false,
requiresPrecision: customConfig.requiresPrecision || false,
requiresAdaptation: customConfig.requiresAdaptation || false,
complexity: customConfig.complexity || 'medium',
cognitivePreference: customConfig.cognitivePreference,
};
const cognitivePatterns = this.cognitivePatternSelector.selectPatternsForPreset(
preset.model,
presetName,
taskContext,
);
// Merge preset config with custom overrides
const config = {
...preset.config,
...customConfig,
modelType: preset.model,
cognitivePatterns,
presetInfo: {
modelType,
presetName,
name: preset.name,
description: preset.description,
useCase: preset.useCase,
performance: preset.performance,
cognitivePatterns: preset.cognitivePatterns,
},
};
// Select appropriate template based on model type
const templateMap = {
transformer: 'transformer_nlp',
cnn: 'cnn_vision',
lstm: 'lstm_sequence',
gru: 'gru_sequence',
autoencoder: 'autoencoder_compress',
vae: 'vae_generator',
gnn: 'gnn_social',
gat: 'graph_attention',
resnet: 'resnet_classifier',
attention: 'attention_mechanism',
diffusion: 'diffusion_model',
neural_ode: 'neural_ode',
capsnet: 'capsule_network',
snn: 'spiking_neural',
ntm: 'neural_turing',
memnn: 'memory_network',
nca: 'neural_cellular',
hypernet: 'hypernetwork',
maml: 'meta_learning',
nas: 'neural_architecture_search',
moe: 'mixture_of_experts',
nerf: 'neural_radiance_field',
wavenet: 'wavenet_audio',
pointnet: 'pointnet_3d',
world_model: 'world_model',
normalizing_flow: 'flow_based',
ebm: 'energy_based',
neural_process: 'neural_processes',
set_transformer: 'set_transformer',
};
const template = templateMap[preset.model] || 'preset_model';
return this.createAdvancedNeuralModel(agentId, template, config);
}
/**
* Create a neural network from a recommended preset based on use case
* @param {string} agentId - Agent identifier
* @param {string} useCase - Use case description
* @param {object} customConfig - Optional custom configuration overrides
*/
async createAgentForUseCase(agentId, useCase, customConfig = {}) {
const recommendedPreset = getRecommendedPreset(useCase);
if (!recommendedPreset) {
// Try searching by use case
const searchResults = searchPresetsByUseCase(useCase);
if (searchResults.length === 0) {
throw new Error(`No preset found for use case: ${useCase}`);
}
const bestMatch = searchResults[0];
console.log(`Found preset for "${useCase}": ${bestMatch.preset.name}`);
return this.createAgentFromPreset(
agentId,
bestMatch.category,
bestMatch.presetName,
customConfig,
);
}
return this.createAgentFromPreset(
agentId,
recommendedPreset.category,
recommendedPreset.presetName,
customConfig,
);
}
/**
* Get all available presets for a category
* @param {string} category - Preset category
*/
getAvailablePresets(category = null) {
if (category) {
return getCategoryPresets(category);
}
return NEURAL_PRESETS;
}
/**
* Search presets by use case or description
* @param {string} searchTerm - Search term
*/
searchPresets(searchTerm) {
return searchPresetsByUseCase(searchTerm);
}
/**
* Get performance information for a preset
* @param {string} category - Preset category
* @param {string} presetName - Preset name
*/
getPresetPerformance(category, presetName) {
const preset = getPreset(category, presetName);
return preset.performance;
}
/**
* List all available preset categories and their counts
*/
getPresetSummary() {
const summary = {};
Object.entries(NEURAL_PRESETS).forEach(([category, presets]) => {
summary[category] = {
count: Object.keys(presets).length,
presets: Object.keys(presets),
};
});
return summary;
}
/**
* Get detailed information about agent's preset (if created from preset)
* @param {string} agentId - Agent identifier
*/
getAgentPresetInfo(agentId) {
const network = this.neuralNetworks.get(agentId);
if (!network || !network.config || !network.config.presetInfo) {
return null;
}
return network.config.presetInfo;
}
/**
* Update existing agent with preset configuration
* @param {string} agentId - Agent identifier
* @param {string} category - Preset category
* @param {string} presetName - Preset name
* @param {object} customConfig - Optional custom configuration overrides
*/
async updateAgentWithPreset(agentId, category, presetName, customConfig = {}) {
const existingNetwork = this.neuralNetworks.get(agentId);
if (existingNetwork) {
// Save current state if needed
console.log(`Updating agent ${agentId} with new preset: ${category}/${presetName}`);
}
// Preserve cognitive evolution history
const cognitiveHistory = await this.cognitiveEvolution.preserveHistory(agentId);
const metaLearningState = await this.metaLearning.preserveState(agentId);
// Remove existing network
this.neuralNetworks.delete(agentId);
this.neuralModels.delete(agentId);
// Create new network with preset and restored cognitive capabilities
const newNetwork = await this.createAgentFromPreset(agentId, category, presetName, customConfig);
// Restore cognitive evolution and meta-learning state
await this.cognitiveEvolution.restoreHistory(agentId, cognitiveHistory);
await this.metaLearning.restoreState(agentId, metaLearningState);
return newNetwork;
}
/**
* Batch create agents from presets
* @param {Array} agentConfigs - Array of {agentId, category, presetName, customConfig}
*/
async batchCreateAgentsFromPresets(agentConfigs) {
const results = [];
const errors = [];
for (const config of agentConfigs) {
try {
const agent = await this.createAgentFromPreset(
config.agentId,
config.category,
config.presetName,
config.customConfig || {},
);
results.push({ agentId: config.agentId, success: true, agent });
} catch (error) {
errors.push({ agentId: config.agentId, error: error.message });
}
}
return { results, errors };
}
// ===============================
// ENHANCED NEURAL CAPABILITIES
// ===============================
/**
* Enable knowledge sharing between agents
* @param {Array} agentIds - List of agent IDs
* @param {Object} session - Collaborative session object
*/
async enableKnowledgeSharing(agentIds, session) {
const knowledgeGraph = session.knowledgeGraph;
for (const agentId of agentIds) {
const agent = this.neuralNetworks.get(agentId);
if (!agent) {
continue;
}
// Extract knowledge from agent
const knowledge = await this.extractAgentKnowledge(agentId);
knowledgeGraph.set(agentId, knowledge);
// Store in shared knowledge base
this.sharedKnowledge.set(agentId, knowledge);
}
// Create knowledge sharing matrix
const sharingMatrix = await this.createKnowledgeSharingMatrix(agentIds);
session.knowledgeSharingMatrix = sharingMatrix;
console.log(`Knowledge sharing enabled for ${agentIds.length} agents`);
}
/**
* Extract knowledge from a neural network agent
* @param {string} agentId - Agent identifier
*/
async extractAgentKnowledge(agentId) {
const network = this.neuralNetworks.get(agentId);
if (!network) {
return null;
}
const knowledge = {
agentId,
timestamp: Date.now(),
modelType: network.modelType,
weights: await this.extractImportantWeights(network),
patterns: await this.cognitiveEvolution.extractPatterns(agentId),
experiences: await this.metaLearning.extractExperiences(agentId),
performance: network.getMetrics(),
specializations: await this.identifySpecializations(agentId),
};
return knowledge;
}
/**
* Extract important weights from a neural network
* @param {Object} network - Neural network instance
*/
async extractImportantWeights(network) {
// Use magnitude-based importance scoring
const weights = network.getWeights();
const importantWeights = {};
Object.entries(weights).forEach(([layer, weight]) => {
if (weight && weight.length > 0) {
// Calculate importance scores (magnitude-based)
const importance = weight.map(w => Math.abs(w));
const threshold = this.calculateImportanceThreshold(importance);
importantWeights[layer] = weight.filter((w, idx) => importance[idx] > threshold);
}
});
return importantWeights;
}
/**
* Calculate importance threshold for weight selection
* @param {Array} importance - Array of importance scores
*/
calculateImportanceThreshold(importance) {
const sorted = importance.slice().sort((a, b) => b - a);
// Take top 20% of weights
const topPercentile = Math.floor(sorted.length * 0.2);
return sorted[topPercentile] || 0;
}
/**
* Identify agent specializations based on performance patterns
* @param {string} agentId - Agent identifier
*/
async identifySpecializations(agentId) {
const metrics = this.performanceMetrics.get(agentId);
if (!metrics) {
return [];
}
const specializations = [];
// Analyze adaptation history for specialization patterns
for (const adaptation of metrics.adaptationHistory) {
if (adaptation.trainingResult && adaptation.trainingResult.accuracy > 0.8) {
specializations.push({
domain: this.inferDomainFromTraining(adaptation),
confidence: adaptation.trainingResult.accuracy,
timestamp: adaptation.timestamp,
});
}
}
return specializations;
}
/**
* Infer domain from training patterns
* @param {Object} adaptation - Adaptation record
*/
inferDomainFromTraining(adaptation) {
// Simple heuristic - in practice, would use more sophisticated analysis
const accuracy = adaptation.trainingResult.accuracy;
const loss = adaptation.trainingResult.loss;
if (accuracy > 0.9 && loss < 0.1) {
return 'classification';
}
if (accuracy > 0.85 && loss < 0.2) {
return 'regression';
}
if (loss < 0.3) {
return 'generation';
}
return 'general';
}
/**
* Create knowledge sharing matrix between agents
* @param {Array} agentIds - List of agent IDs
*/
async createKnowledgeSharingMatrix(agentIds) {
const matrix = {};
for (let i = 0; i < agentIds.length; i++) {
const agentA = agentIds[i];
matrix[agentA] = {};
for (let j = 0; j < agentIds.length; j++) {
const agentB = agentIds[j];
if (i === j) {
matrix[agentA][agentB] = 1.0; // Self-similarity
continue;
}
const similarity = await this.calculateAgentSimilarity(agentA, agentB);
matrix[agentA][agentB] = similarity;
}
}
return matrix;
}
/**
* Calculate similarity between two agents
* @param {string} agentA - First agent ID
* @param {string} agentB - Second agent ID
*/
async calculateAgentSimilarity(agentA, agentB) {
const knowledgeA = this.sharedKnowledge.get(agentA);
const knowledgeB = this.sharedKnowledge.get(agentB);
if (!knowledgeA || !knowledgeB) {
return 0;
}
// Calculate multiple similarity metrics
const structuralSimilarity = this.calculateStructuralSimilarity(knowledgeA, knowledgeB);
const performanceSimilarity = this.calculatePerformanceSimilarity(knowledgeA, knowledgeB);
const specializationSimilarity = this.calculateSpecializationSimilarity(knowledgeA, knowledgeB);
// Weighted combination
return (structuralSimilarity * 0.4 + performanceSimilarity * 0.3 + specializationSimilarity * 0.3);
}
/**
* Calculate structural similarity between agents
* @param {Object} knowledgeA - Knowledge from agent A
* @param {Object} knowledgeB - Knowledge from agent B
*/
calculateStructuralSimilarity(knowledgeA, knowledgeB) {
if (knowledgeA.modelType !== knowledgeB.modelType) {
return 0.1;
}
// Compare weight patterns (simplified cosine similarity)
const weightsA = Object.values(knowledgeA.weights).flat();
const weightsB = Object.values(knowledgeB.weights).flat();
if (weightsA.length === 0 || weightsB.length === 0) {
return 0.5;
}
const minLength = Math.min(weightsA.length, weightsB.length);
let dotProduct = 0;
let normA = 0;
let normB = 0;
for (let i = 0; i < minLength; i++) {
dotProduct += weightsA[i] * weightsB[i];
normA += weightsA[i] * weightsA[i];
normB += weightsB[i] * weightsB[i];
}
const similarity = dotProduct / (Math.sqrt(normA) * Math.sqrt(normB));
return Math.max(0, Math.min(1, similarity));
}
/**
* Calculate performance similarity between agents
* @param {Object} knowledgeA - Knowledge from agent A
* @param {Object} knowledgeB - Knowledge from agent B
*/
calculatePerformanceSimilarity(knowledgeA, knowledgeB) {
const perfA = knowledgeA.performance;
const perfB = knowledgeB.performance;
const accuracyDiff = Math.abs(perfA.accuracy - perfB.accuracy);
const lossDiff = Math.abs(perfA.loss - perfB.loss);
// Inverse relationship - smaller differences = higher similarity
const accuracySimilarity = 1 - Math.min(1, accuracyDiff);
const lossSimilarity = 1 - Math.min(1, lossDiff);
return (accuracySimilarity + lossSimilarity) / 2;
}
/**
* Calculate specialization similarity between agents
* @param {Object} knowledgeA - Knowledge from agent A
* @param {Object} knowledgeB - Knowledge from agent B
*/
calculateSpecializationSimilarity(knowledgeA, knowledgeB) {
const specsA = new Set(knowledgeA.specializations.map(s => s.domain));
const specsB = new Set(knowledgeB.specializations.map(s => s.domain));
const intersection = new Set([...specsA].filter(x => specsB.has(x)));
const union = new Set([...specsA, ...specsB]);
return union.size > 0 ? intersection.size / union.size : 0;
}
/**
* Start knowledge distillation learning
* @param {Object} session - Collaborative session
*/
startKnowledgeDistillation(session) {
const distillationFunction = async() => {
if (!session.active) {
return;
}
try {
// Identify teacher and student agents
const teachers = await this.identifyTeacherAgents(session.agentIds);
const students = session.agentIds.filter(id => !teachers.includes(id));
// Perform knowledge distillation
for (const teacher of teachers) {
for (const student of students) {
await this.performKnowledgeDistillation(teacher, student, session);
}
}
console.log(`Knowledge distillation completed for session ${session.id}`);
} catch (error) {
console.error('Knowledge distillation failed:', error);
}
// Schedule next distillation
setTimeout(distillationFunction, session.syncInterval);
};
// Start distillation process
setTimeout(distillationFunction, 1000);
}
/**
* Identify teacher agents based on performance
* @param {Array} agentIds - List of agent IDs
*/
async identifyTeacherAgents(agentIds) {
const agentPerformances = [];
for (const agentId of agentIds) {
const network = this.neuralNetworks.get(agentId);
if (network) {
const metrics = network.getMetrics();
agentPerformances.push({
agentId,
performance: metrics.accuracy || 0,
});
}
}
// Sort by performance and take top 30%
agentPerformances.sort((a, b) => b.performance - a.performance);
const numTeachers = Math.max(1, Math.floor(agentPerformances.length * 0.3));
return agentPerformances.slice(0, numTeachers).map(ap => ap.agentId);
}
/**
* Perform knowledge distillation between teacher and student
* @param {string} teacherAgentId - Teacher agent ID
* @param {string} studentAgentId - Student agent ID
* @param {Object} session - Collaborative session
*/
async performKnowledgeDistillation(teacherAgentId, studentAgentId, session) {
const teacher = this.neuralNetworks.get(teacherAgentId);
const student = this.neuralNetworks.get(studentAgentId);
if (!teacher || !student) {
return;
}
try {
// Extract soft targets from teacher
const teacherKnowledge = this.sharedKnowledge.get(teacherAgentId);
if (!teacherKnowledge) {
return;
}
// Create distillation loss function
const distillationTemperature = 3.0;
const alpha = 0.7; // Weight for distillation loss vs hard target loss
// Apply knowledge distillation (simplified)
const distillationResult = await this.applyKnowledgeDistillation(
student,
teacherKnowledge,
{ temperature: distillationTemperature, alpha },
);
// Update collaboration matrix
const teacherIdx = session.agentIds.indexOf(teacherAgentId);
const studentIdx = session.agentIds.indexOf(studentAgentId);
if (teacherIdx >= 0 && studentIdx >= 0) {
session.coordinationMatrix[studentIdx][teacherIdx] += distillationResult.improvement;
}
} catch (error) {
console.error(`Knowledge distillation failed between ${teacherAgentId} and ${studentAgentId}:`, error);
}
}
/**
* Apply knowledge distillation to student network
* @param {Object} student - Student network
* @param {Object} teacherKnowledge - Teacher's knowledge
* @param {Object} options - Distillation options
*/
async applyKnowledgeDistillation(student, teacherKnowledge, options) {
const { temperature, alpha } = options;
// Simulate knowledge transfer (in practice, would involve actual training)
const beforeMetrics = student.getMetrics();
// Apply teacher's patterns to student (simplified)
const patterns = teacherKnowledge.patterns;
if (patterns && patterns.length > 0) {
await this.cognitiveEvolution.transferPatterns(student.agentId, patterns);
}
const afterMetrics = student.getMetrics();
const improvement = Math.max(0, afterMetrics.accuracy - beforeMetrics.accuracy);
return { improvement, beforeMetrics, afterMetrics };
}
/**
* Start neural coordination protocol
* @param {Object} session - Collaborative session
*/
startNeuralCoordination(session) {
const coordinationFunction = async() => {
if (!session.active) {
return;
}
try {
// Update coordination matrix
await this.updateCoordinationMatrix(session);
// Perform neural coordination
await this.coordinationProtocol.coordinate(session);
// Apply coordination results
await this.applyCoordinationResults(session);
console.log(`Neural coordination completed for session ${session.id}`);
} catch (error) {
console.error('Neural coordination failed:', error);
}
// Schedule next coordination
setTimeout(coordinationFunction, session.syncInterval);
};
// Start coordination process
setTimeout(coordinationFunction, 1000);
}
/**
* Update coordination matrix based on agent interactions
* @param {Object} session - Collaborative session
*/
async updateCoordinationMatrix(session) {
for (let i = 0; i < session.agentIds.length; i++) {
for (let j = 0; j < session.agentIds.length; j++) {
if (i === j) {
continue;
}
const agentA = session.agentIds[i];
const agentB = session.agentIds[j];
// Calculate interaction strength
const interactionStrength = await this.calculateInteractionStrength(agentA, agentB);
session.coordinationMatrix[i][j] = interactionStrength;
}
}
}
/**
* Calculate interaction strength between two agents
* @param {string} agentA - First agent ID
* @param {string} agentB - Second agent ID
*/
async calculateInteractionStrength(agentA, agentB) {
const interactions = this.agentInteractions.get(`${agentA}-${agentB}`) || [];
if (interactions.length === 0) {
return 0.1;
} // Minimal baseline interaction
// Calculate recency-weighted interaction strength
const now = Date.now();
let totalStrength = 0;
let totalWeight = 0;
for (const interaction of interactions) {
const age = now - interaction.timestamp;
const weight = Math.exp(-age / (24 * 60 * 60 * 1000)); // Exponential decay over 24 hours
totalStrength += interaction.strength * weight;
totalWeight += weight;
}
return totalWeight > 0 ? totalStrength / totalWeight : 0.1;
}
/**
* Apply coordination results to agents
* @param {Object} session - Collaborative session
*/
async applyCoordinationResults(session) {
const coordinationResults = await this.coordinationProtocol.getResults(session.id);
if (!coordinationResults) {
return;
}
for (const [agentId, coordination] of coordinationResults.entries()) {
const agent = this.neuralNetworks.get(agentId);
if (!agent) {
continue;
}
// Apply coordination adjustments
if (coordination.weightAdjustments) {
await this.applyWeightAdjustments(agent, coordination.weightAdjustments);
}
// Apply cognitive pattern updates
if (coordination.patternUpdates) {
await this.cognitiveEvolution.applyPatternUpdates(agentId, coordination.patternUpdates);
}
// Update performance metrics
const metrics = this.performanceMetrics.get(agentId);
if (metrics) {
metrics.collaborationScore = coordination.collaborationScore || 0;
metrics.cognitivePatterns.push(...(coordination.newPatterns || []));
}
}
}
/**
* Apply weight adjustments to a neural network
* @param {Object} agent - Neural network agent
* @param {Object} adjustments - Weight adjustments
*/
async applyWeightAdjustments(agent, adjustments) {
try {
const currentWeights = agent.getWeights();
const adjustedWeights = {};
Object.entries(currentWeights).forEach(([layer, weights]) => {
if (adjustments[layer]) {
adjustedWeights[layer] = weights.map((w, idx) => {
const adjustment = adjustments[layer][idx] || 0;
return w + adjustment * 0.1; // Scale adjustment factor
});
} else {
adjustedWeights[layer] = weights;
}
});
agent.setWeights(adjustedWeights);
} catch (error) {
console.error('Failed to apply weight adjustments:', error);
}
}
/**
* Record agent interaction for coordination tracking
* @param {string} agentA - First agent ID
* @param {string} agentB - Second agent ID
* @param {number} strength - Interaction strength (0-1)
* @param {string} type - Interaction type
*/
recordAgentInteraction(agentA, agentB, strength, type = 'general') {
const interactionKey = `${agentA}-${agentB}`;
if (!this.agentInteractions.has(interactionKey)) {
this.agentInteractions.set(interactionKey, []);
}
this.agentInteractions.get(interactionKey).push({
timestamp: Date.now(),
strength,
type,
agentA,
agentB,
});
// Keep only recent interactions (last 100)
const interactions = this.agentInteractions.get(interactionKey);
if (interactions.length > 100) {
interactions.splice(0, interactions.length - 100);
}
}
/**
* Get all complete neural presets (27+ models)
*/
getCompleteNeuralPresets() {
return COMPLETE_NEURAL_PRESETS;
}
/**
* Get preset recommendations based on requirements
* @param {string} useCase - Use case description
* @param {Object} requirements - Performance and other requirements
*/
getPresetRecommendations(useCase, requirements = {}) {
return this.cognitivePatternSelector.getPresetRecommendations(useCase, requirements);
}
/**
* Get adaptation recommendations for an agent
* @param {string} agentId - Agent identifier
*/
async getAdaptationRecommendations(agentId) {
return this.neuralAdaptationEngine.getAdaptationRecommendations(agentId);
}
/**
* Export adaptation insights across all agents
*/
getAdaptationInsights() {
return this.neuralAdaptationEngine.exportAdaptationInsights();
}
/**
* List all available neural model types with counts
*/
getAllNeuralModelTypes() {
const modelTypes = {};
// Count presets from complete neural presets
Object.entries(COMPLETE_NEURAL_PRESETS).forEach(([modelType, presets]) => {
modelTypes[modelType] = {
count: Object.keys(presets).length,
presets: Object.keys(presets),
description: Object.values(presets)[0]?.description || 'Neural model type',
};
});
return modelTypes;
}
/**
* Get comprehensive neural network statistics
*/
getEnhancedStatistics() {
const stats = {
totalAgents: this.neuralNetworks.size,
modelTypes: {},
cognitiveEvolution: this.cognitiveEvolution.getStatistics(),
metaLearning: this.metaLearning.getStatistics(),
coordination: this.coordinationProtocol.getStatistics(),
performance: {},
collaborations: 0,
};
// Count model types
for (const [agentId, network] of this.neuralNetworks.entries()) {
const modelType = network.modelType || 'unknown';
stats.modelTypes[modelType] = (stats.modelTypes[modelType] || 0) + 1;
// Performance statistics
const metrics = this.performanceMetrics.get(agentId);
if (metrics) {
if (!stats.performance[modelType]) {
stats.performance[modelType] = {
count: 0,
avgAccuracy: 0,
avgCollaborationScore: 0,
totalAdaptations: 0,
};
}
const perf = stats.performance[modelType];
perf.count++;
perf.avgAccuracy += (network.getMetrics().accuracy || 0);
perf.avgCollaborationScore += metrics.collaborationScore;
perf.totalAdaptations += metrics.adaptationHistory.length;
}
}
// Calculate averages
Object.values(stats.performance).forEach(perf => {
if (perf.count > 0) {
perf.avgAccuracy /= perf.count;
perf.avgCollaborationScore /= perf.count;
}
});
// Count active collaborations
stats.collaborations = this.sharedKnowledge.size;
return stats;
}
}
// Neural Network wrapper class
class NeuralNetwork {
constructor(networkId, agentId, config, wasmModule) {
this.networkId = networkId;
this.agentId = agentId;
this.config = config;
this.wasmModule = wasmModule;
this.trainingHistory = [];
this.metrics = {
accuracy: 0,
loss: 1.0,
epochs_trained: 0,
total_samples: 0,
};
}
async forward(input) {
try {
const result = this.wasmModule.exports.forward_pass(this.networkId, input);
return result;
} catch (error) {
console.error('Forward pass failed:', error);
return new Float32Array(this.config.layers[this.config.layers.length - 1]).fill(0.5);
}
}
async train(trainingData, options) {
const { epochs, batchSize, learningRate, freezeLayers } = options;
for (let epoch = 0; epoch < epochs; epoch++) {
let epochLoss = 0;
let batchCount = 0;
// Process in batches
for (let