automata-metaverse/src/engines/bootstrap-automaton.ts

Automaton execution engines for self-referential CanvasL/JSONL systems

#!/usr/bin/env node

/**
 * Optimized Self-Instantiation Bootstrap
 * Implements the optimal strategy for bootstrapping the automaton from
 * automaton-kernel.jsonl and integrating with OpenCode components
 */

import { writeFileSync, existsSync } from 'fs';
import { MetaLogDb } from 'meta-log-db';
import { AUTOMATON_FILES } from 'automaton-evolutions';

interface BootstrapStep {
  name: string;
  execute: () => Promise<void>;
  validate?: () => Promise<boolean>;
}

interface DimensionConfig {
  dimension: string;
  kernelLine: number;
  operation: string;
  church: string;
  next?: string;
}

interface BootstrapConfig {
  kernelPath?: string;
  automatonPath?: string;
  db?: MetaLogDb;
}

class OptimizedBootstrap {
  private kernelPath: string;
  private automatonPath: string;
  private db: MetaLogDb;
  private kernelObjects: any[] = [];
  private automatonObjects: any[] = [];

  constructor(config?: BootstrapConfig) {
    // Default to automaton-evolutions canonical files if available
    // Note: automaton-kernel.jsonl is the full kernel (generated from seed)
    // For seed file, use AUTOMATON_FILES.a1KernelSeed from automaton-evolutions
    this.kernelPath = config?.kernelPath || './automaton-kernel.jsonl';
    this.automatonPath = config?.automatonPath || './automaton.jsonl';
    this.db = config?.db || new MetaLogDb({ enableProlog: true, enableDatalog: true, enableShacl: true });
  }
  
  // Dimensional progression configuration
  private dimensionalPath: DimensionConfig[] = [
    { dimension: '0D', kernelLine: 2, operation: 'identity', church: 'λf.λx.x', next: '1D' },
    { dimension: '1D', kernelLine: 4, operation: 'successor', church: 'λn.λf.λx.f(nfx)', next: '2D' },
    { dimension: '2D', kernelLine: 6, operation: 'pair', church: 'λx.λy.λf.fxy', next: '3D' },
    { dimension: '3D', kernelLine: 8, operation: 'addition', church: 'λm.λn.λf.λx.mf(nfx)', next: '4D' },
    { dimension: '4D', kernelLine: 10, operation: 'network', church: 'λnetwork.execute(spacetime)', next: '5D' },
    { dimension: '5D', kernelLine: 12, operation: 'consensus', church: 'λconsensus.validate(ledger)', next: '6D' },
    { dimension: '6D', kernelLine: 14, operation: 'intelligence', church: 'λai.attention(transform)', next: '7D' },
    { dimension: '7D', kernelLine: 16, operation: 'quantum', church: 'λquantum.superposition(ψ)' }
  ];

  /**
   * Main bootstrap entry point
   */
  async bootstrap(): Promise<void> {
    console.log('🚀 Starting Optimized Self-Instantiation Bootstrap\n');
    
    try {
      // Phase 1: Kernel Bootstrap
      console.log('📦 Phase 1: Kernel Bootstrap');
      await this.executeTransactionBootstrap();
      
      // Phase 2: Dimensional Progression
      console.log('\n📈 Phase 2: Dimensional Progression');
      await this.progressDimensions();
      
      // Phase 3: Integration Activation
      console.log('\n🔌 Phase 3: Integration Activation');
      await this.activateIntegration();
      
      // Phase 4: Self-Reference Execution
      console.log('\n🔄 Phase 4: Self-Reference Execution');
      await this.executeSelfReference();
      
      console.log('\n✅ Bootstrap completed successfully!');
      await this.printBootstrapSummary();
      
    } catch (error: any) {
      console.error('\n❌ Bootstrap failed:', error.message);
      throw error;
    }
  }

  /**
   * Phase 1: Execute transaction bootstrap from kernel
   */
  private async executeTransactionBootstrap(): Promise<void> {
    // Find transaction-bootstrap in kernel
    const bootstrapEntry = this.kernelObjects.find(obj => 
      obj.id === 'transaction-bootstrap' && obj.type === 'transaction'
    );
    
    if (!bootstrapEntry) {
      throw new Error('transaction-bootstrap not found in kernel');
    }
    
    const steps = bootstrapEntry.steps || [];
    console.log(`   Found ${steps.length} bootstrap steps`);
    
    for (const step of steps) {
      await this.executeBootstrapStep(step);
    }
  }

  /**
   * Execute individual bootstrap step
   */
  private async executeBootstrapStep(step: string): Promise<void> {
    console.log(`   → ${step}`);
    
    switch (step) {
      case 'begin':
        await this.beginTransaction();
        break;
      case 'validate-shacl':
        await this.validateSHACL();
        break;
      case 'load-automaton':
        await this.loadAutomaton();
        break;
      case 'initialize-evaluator':
        await this.initializeEvaluator();
        break;
      case 'execute-self-reference':
        // Deferred to Phase 4
        break;
      case 'commit':
        await this.commitTransaction();
        break;
      default:
        console.warn(`   ⚠️  Unknown step: ${step}`);
    }
  }

  /**
   * Begin bootstrap transaction
   */
  private async beginTransaction(): Promise<void> {
    if (!existsSync(this.kernelPath)) {
      throw new Error(`Kernel file not found: ${this.kernelPath}`);
    }
    
    // Use meta-log-db to load kernel
    await this.db.loadCanvas(this.kernelPath);
    const canvas = await this.db.parseJsonlCanvas(this.kernelPath);
    
    // Flatten canvas to get objects
    this.kernelObjects = [];
    if (canvas.nodes) this.kernelObjects.push(...canvas.nodes);
    if (canvas.edges) this.kernelObjects.push(...canvas.edges);
    for (const [key, value] of Object.entries(canvas)) {
      if (key !== 'nodes' && key !== 'edges' && Array.isArray(value)) {
        this.kernelObjects.push(...value);
      }
    }
    
    console.log(`   ✅ Loaded ${this.kernelObjects.length} kernel objects`);
  }

  /**
   * Validate SHACL constraints
   */
  private async validateSHACL(): Promise<void> {
    const shaclShapes = this.kernelObjects.filter(obj => 
      obj.type === 'shacl' || obj['sh:path']
    );
    
    console.log(`   ✅ Found ${shaclShapes.length} SHACL constraints`);
    
    // Validate automaton self-references
    const automata = this.kernelObjects.filter(obj => 
      obj.type === 'automaton'
    );
    
    for (const automaton of automata) {
      if (!automaton.selfReference || !automaton.selfReference.file) {
        throw new Error(`Automaton ${automaton.id} missing selfReference`);
      }
      if (automaton.selfReference.file !== 'automaton-kernel.jsonl') {
        throw new Error(`Automaton ${automaton.id} has invalid selfReference file`);
      }
    }
    
    console.log(`   ✅ Validated ${automata.length} automata`);
  }

  /**
   * Load automaton structure
   */
  private async loadAutomaton(): Promise<void> {
    // Load or create automaton.jsonl
    if (existsSync(this.automatonPath)) {
      // Use meta-log-db to load automaton
      await this.db.loadCanvas(this.automatonPath);
      const canvas = await this.db.parseJsonlCanvas(this.automatonPath);
      
      // Flatten canvas to get objects
      this.automatonObjects = [];
      if (canvas.nodes) this.automatonObjects.push(...canvas.nodes);
      if (canvas.edges) this.automatonObjects.push(...canvas.edges);
      for (const [key, value] of Object.entries(canvas)) {
        if (key !== 'nodes' && key !== 'edges' && Array.isArray(value)) {
          this.automatonObjects.push(...value);
        }
      }
      
      console.log(`   ✅ Loaded ${this.automatonObjects.length} automaton objects`);
    } else {
      // Initialize from kernel
      this.automatonObjects = [...this.kernelObjects];
      this.saveAutomaton();
      console.log(`   ✅ Initialized automaton from kernel`);
    }
  }

  /**
   * Initialize Church encoding evaluator
   */
  private async initializeEvaluator(): Promise<void> {
    // Verify Church encoding patterns exist
    const churchPatterns = [
      'λf.λx.x',           // 0D
      'λn.λf.λx.f(nfx)',  // 1D
      'λx.λy.λf.fxy',     // 2D
      'λm.λn.λf.λx.mf(nfx)' // 3D
    ];
    
    // Check that kernel contains Church encodings
    const kernelText = JSON.stringify(this.kernelObjects);
    const foundPatterns = churchPatterns.filter(pattern => 
      kernelText.includes(pattern)
    );
    
    console.log(`   ✅ Found ${foundPatterns.length}/${churchPatterns.length} Church encoding patterns`);
  }

  /**
   * Commit transaction
   */
  private async commitTransaction(): Promise<void> {
    // Ensure automaton is saved
    this.saveAutomaton();
    console.log(`   ✅ Transaction committed`);
  }

  /**
   * Phase 2: Progress through dimensions
   */
  private async progressDimensions(): Promise<void> {
    for (const config of this.dimensionalPath) {
      await this.instantiateDimension(config);
    }
  }

  /**
   * Instantiate a specific dimension
   */
  private async instantiateDimension(config: DimensionConfig): Promise<void> {
    console.log(`   → Instantiating ${config.dimension} (${config.operation})`);
    
    // Find automaton for this dimension
    const automaton = this.kernelObjects.find(obj => 
      obj.type === 'automaton' && 
      obj.dimensionalLevel === parseInt(config.dimension[0] || '0')
    );
    
    if (!automaton) {
      console.warn(`   ⚠️  No automaton found for ${config.dimension}`);
      return;
    }
    
    // Verify self-reference points to correct kernel line
    if (automaton.selfReference?.line !== config.kernelLine) {
      console.warn(`   ⚠️  Self-reference line mismatch for ${config.dimension}`);
    }
    
    // Verify Church encoding matches
    const systemObj = this.kernelObjects.find(obj => 
      obj.id === `${config.dimension}-system`
    );
    
    if (systemObj && systemObj.text?.includes(config.church)) {
      console.log(`   ✅ ${config.dimension} validated (Church: ${config.church})`);
    } else {
      console.warn(`   ⚠️  Church encoding not found for ${config.dimension}`);
    }
  }

  /**
   * Phase 3: Integration activation (stub - OpenCode removed)
   */
  private async activateIntegration(): Promise<void> {
    // OpenCode integration removed - all operations use meta-log-db
    console.log('   ✅ Using meta-log-db for all operations');
    console.log('   ✅ Integration ready (meta-log-db)');
  }

  /**
   * Phase 4: Execute self-reference
   */
  private async executeSelfReference(): Promise<void> {
    // Create kernel self-reference
    const kernelSelfRef = this.kernelObjects.find(obj => 
      obj.id === 'self-ref' && obj.type === 'file'
    );
    
    if (kernelSelfRef) {
      console.log(`   ✅ Kernel self-reference: ${kernelSelfRef.file}`);
    }
    
    // Create automaton self-reference
    const automatonSelfRef = this.automatonObjects.find(obj => 
      obj.id === 'self-ref' && obj.type === 'file'
    );
    
    if (!automatonSelfRef) {
      // Add self-reference to automaton
      const newSelfRef = {
        id: 'self-ref',
        type: 'file',
        x: 800,
        y: 0,
        width: 280,
        height: 120,
        color: '5',
        file: this.automatonPath
      };
      this.automatonObjects.push(newSelfRef);
      this.saveAutomaton();
      console.log(`   ✅ Created automaton self-reference`);
    } else {
      console.log(`   ✅ Automaton self-reference: ${automatonSelfRef.file}`);
    }
    
    // Link dimensional automata to kernel
    const automata = this.automatonObjects.filter(obj => 
      obj.type === 'automaton'
    );
    
    for (const automaton of automata) {
      if (automaton.selfReference?.file === 'automaton-kernel.jsonl') {
        console.log(`   ✅ ${automaton.id} linked to kernel:line ${automaton.selfReference.line}`);
      }
    }
  }

  /**
   * Save automaton to file
   */
  private saveAutomaton(): void {
    const jsonlContent = this.automatonObjects
      .map(obj => JSON.stringify(obj))
      .join('\n') + '\n';
    writeFileSync(this.automatonPath, jsonlContent);
  }

  /**
   * Print bootstrap summary
   */
  private async printBootstrapSummary(): Promise<void> {
    console.log('\n📊 Bootstrap Summary:');
    console.log(`   Kernel objects: ${this.kernelObjects.length}`);
    console.log(`   Automaton objects: ${this.automatonObjects.length}`);
    console.log(`   Dimensions instantiated: ${this.dimensionalPath.length}`);
    console.log(`   Integration: meta-log-db`);
  }
}

// CLI Interface
if (require.main === module) {
  const bootstrap = new OptimizedBootstrap();
  bootstrap.bootstrap()
    .then(() => {
      console.log('\n🎉 Bootstrap completed successfully!');
      process.exit(0);
    })
    .catch((error: any) => {
      console.error('\n❌ Bootstrap failed:', error);
      process.exit(1);
    });
}

export { OptimizedBootstrap };
export default OptimizedBootstrap;