automata-metaverse
Version:
Automaton execution engines for self-referential CanvasL/JSONL systems
308 lines • 12.8 kB
JavaScript
/**
* 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';
class OptimizedBootstrap {
constructor(config) {
this.kernelObjects = [];
this.automatonObjects = [];
// Dimensional progression configuration
this.dimensionalPath = [
{ 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)' }
];
// 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 });
}
/**
* Main bootstrap entry point
*/
async bootstrap() {
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) {
console.error('\n❌ Bootstrap failed:', error.message);
throw error;
}
}
/**
* Phase 1: Execute transaction bootstrap from kernel
*/
async executeTransactionBootstrap() {
// 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
*/
async executeBootstrapStep(step) {
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
*/
async beginTransaction() {
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
*/
async validateSHACL() {
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
*/
async loadAutomaton() {
// 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
*/
async initializeEvaluator() {
// 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
*/
async commitTransaction() {
// Ensure automaton is saved
this.saveAutomaton();
console.log(` ✅ Transaction committed`);
}
/**
* Phase 2: Progress through dimensions
*/
async progressDimensions() {
for (const config of this.dimensionalPath) {
await this.instantiateDimension(config);
}
}
/**
* Instantiate a specific dimension
*/
async instantiateDimension(config) {
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)
*/
async activateIntegration() {
// 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
*/
async executeSelfReference() {
// 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
*/
saveAutomaton() {
const jsonlContent = this.automatonObjects
.map(obj => JSON.stringify(obj))
.join('\n') + '\n';
writeFileSync(this.automatonPath, jsonlContent);
}
/**
* Print bootstrap summary
*/
async printBootstrapSummary() {
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) => {
console.error('\n❌ Bootstrap failed:', error);
process.exit(1);
});
}
export { OptimizedBootstrap };
export default OptimizedBootstrap;
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