automata-metaverse
Version:
Automaton execution engines for self-referential CanvasL/JSONL systems
1,189 lines (1,144 loc) • 46.2 kB
JavaScript
import { writeFileSync, existsSync } from 'fs';
import { MetaLogDb } from 'meta-log-db';
// Note: global.gc is already declared by @types/node as NodeJS.GCFunction | undefined
// Memory pool for CanvasObject reuse to reduce memory volatility
class ObjectPool {
constructor(createFn, resetFn, maxSize = 100) {
this.pool = [];
this.createFn = createFn;
this.resetFn = resetFn;
this.maxSize = maxSize;
}
acquire() {
if (this.pool.length > 0) {
return this.pool.pop();
}
return this.createFn();
}
release(obj) {
if (this.pool.length < this.maxSize) {
this.resetFn(obj);
this.pool.push(obj);
}
}
clear() {
this.pool = [];
}
get size() {
return this.pool.length;
}
}
class AdvancedSelfReferencingAutomaton {
constructor(filePath, db) {
this.objects = [];
this.currentDimension = 0;
this.executionHistory = [];
this.selfModificationCount = 0;
this.MAX_EXECUTION_HISTORY = 1000; // Limit history to prevent memory leaks
// Memory pool for object reuse
this.objectPool = new ObjectPool(() => ({ id: '', type: '', currentState: '', dimensionalLevel: 0 }), (obj) => {
// Reset object for reuse
obj.id = '';
obj.type = '';
obj.currentState = '';
obj.dimensionalLevel = 0;
obj.selfReference = undefined;
obj.provenanceHistory = undefined;
}, 200 // Max pool size
);
this.filePath = filePath;
this.db = db || new MetaLogDb({ enableProlog: true, enableDatalog: true });
// Load will be called explicitly via init() or load()
}
/**
* Initialize the automaton by loading the file
* Call this after construction if you need to ensure the file is loaded
*/
async init() {
await this.load();
}
/**
* Flatten Canvas structure to array of objects
*/
flattenCanvas(canvas) {
const objects = [];
// Add nodes
if (canvas.nodes) {
objects.push(...canvas.nodes);
}
// Add edges
if (canvas.edges) {
objects.push(...canvas.edges);
}
// Add other object types
for (const [key, value] of Object.entries(canvas)) {
if (key !== 'nodes' && key !== 'edges' && Array.isArray(value)) {
objects.push(...value);
}
}
return objects;
}
async load() {
if (!existsSync(this.filePath)) {
throw new Error(`Automaton file not found: ${this.filePath}`);
}
// Use meta-log-db to parse the canvas
await this.db.loadCanvas(this.filePath);
const canvas = await this.db.parseJsonlCanvas(this.filePath);
// Flatten Canvas structure to get all objects
const parsedObjects = this.flattenCanvas(canvas);
this.objects = [];
// Map: ID -> { obj, provenanceHistory, seenInFiles }
const seenIds = new Map();
let duplicateCount = 0;
let provenanceMergedCount = 0;
for (let i = 0; i < parsedObjects.length; i++) {
const obj = parsedObjects[i];
if (obj && typeof obj === 'object') {
if (obj.id) {
const currentFile = obj.selfReference?.file || this.filePath;
const currentLine = obj.selfReference?.line || i + 1;
const currentProvenance = obj.selfReference
? { file: obj.selfReference.file, line: obj.selfReference.line, pattern: obj.selfReference.pattern }
: { file: this.filePath, line: i + 1 };
if (seenIds.has(obj.id)) {
const existing = seenIds.get(obj.id);
const existingFile = existing.obj.selfReference?.file || this.filePath;
// Check if this is a cross-file duplicate (federated provenance)
if (currentFile !== existingFile && currentFile !== this.filePath && existingFile !== this.filePath) {
// Cross-file duplicate: preserve both for federated provenance
// Add provenance history to existing object
if (!existing.obj.provenanceHistory) {
existing.obj.provenanceHistory = [];
if (existing.obj.selfReference) {
existing.obj.provenanceHistory.push({
file: existing.obj.selfReference.file,
line: existing.obj.selfReference.line,
pattern: existing.obj.selfReference.pattern
});
}
}
existing.obj.provenanceHistory.push(currentProvenance);
// Keep both objects (federated provenance requirement)
this.objects.push(obj);
provenanceMergedCount++;
console.log(`📋 Cross-file provenance: ID "${obj.id}" found in ${existingFile} and ${currentFile} (preserving both)`);
}
else {
// Same-file duplicate: merge provenance history, keep latest
const existingIndex = this.objects.findIndex(o => o.id === obj.id);
if (existingIndex >= 0) {
const existingObj = this.objects[existingIndex];
// Merge provenance history
if (!existingObj.provenanceHistory) {
existingObj.provenanceHistory = [];
if (existingObj.selfReference) {
existingObj.provenanceHistory.push({
file: existingObj.selfReference.file,
line: existingObj.selfReference.line,
pattern: existingObj.selfReference.pattern
});
}
}
// Add current provenance to history if different
const existingProvenance = existingObj.selfReference
? `${existingObj.selfReference.file}:${existingObj.selfReference.line}`
: 'unknown';
const newProvenance = currentProvenance.file && currentProvenance.line
? `${currentProvenance.file}:${currentProvenance.line}`
: 'unknown';
if (newProvenance !== existingProvenance && newProvenance !== 'unknown') {
existingObj.provenanceHistory.push(currentProvenance);
provenanceMergedCount++;
}
// Replace with latest version (fixes memory leak)
this.objects.splice(existingIndex, 1);
duplicateCount++;
// Update seenIds with latest object and merged history
seenIds.set(obj.id, {
obj,
provenanceHistory: existingObj.provenanceHistory || [],
seenInFiles: existing.seenInFiles
});
existing.seenInFiles.add(currentFile);
}
}
}
else {
// First occurrence: initialize provenance history
const provenanceHistory = [];
if (obj.selfReference) {
provenanceHistory.push({
file: obj.selfReference.file,
line: obj.selfReference.line,
pattern: obj.selfReference.pattern
});
}
seenIds.set(obj.id, {
obj,
provenanceHistory,
seenInFiles: new Set([currentFile])
});
}
}
// Add object to array
if (!obj.id) {
// No ID: always add
this.objects.push(obj);
}
else if (!seenIds.has(obj.id)) {
// First occurrence: add object
this.objects.push(obj);
}
else {
// Duplicate ID: check if already added
const existing = seenIds.get(obj.id);
const existingIndex = this.objects.findIndex(o => o.id === obj.id);
if (existingIndex < 0) {
// Not yet added: add the tracked object (may have merged provenance)
this.objects.push(existing.obj);
}
else if (existing.seenInFiles.size > 1) {
// Cross-file duplicate: add this one too (federated provenance)
this.objects.push(obj);
}
// Otherwise: already handled, skip
}
}
}
if (duplicateCount > 0) {
console.log(`🧹 Removed ${duplicateCount} duplicate objects during load (same-file deduplication)`);
}
if (provenanceMergedCount > 0) {
console.log(`📋 Merged provenance history for ${provenanceMergedCount} objects (federated provenance preserved)`);
}
console.log(`✅ Loaded ${this.objects.length} unique objects from ${this.filePath}`);
}
save() {
// Provenance-aware deduplication before saving
// Preserve provenance history while removing true duplicates
const deduplicated = [];
const seenIds = new Map();
let duplicateCount = 0;
let provenancePreservedCount = 0;
// Process in reverse to keep last occurrence, but preserve provenance
for (let i = this.objects.length - 1; i >= 0; i--) {
const obj = this.objects[i];
if (obj.id) {
const currentFile = obj.selfReference?.file || this.filePath;
if (seenIds.has(obj.id)) {
const existing = seenIds.get(obj.id);
const existingFile = existing.obj.selfReference?.file || this.filePath;
// Cross-file duplicates: preserve both (federated provenance)
if (currentFile !== existingFile && currentFile !== this.filePath && existingFile !== this.filePath) {
// Add to deduplicated array (preserve both)
deduplicated.unshift(obj);
provenancePreservedCount++;
continue;
}
// Same-file duplicate: merge provenance history
if (!existing.obj.provenanceHistory && obj.selfReference) {
existing.obj.provenanceHistory = [];
if (existing.obj.selfReference) {
existing.obj.provenanceHistory.push({
file: existing.obj.selfReference.file,
line: existing.obj.selfReference.line,
pattern: existing.obj.selfReference.pattern
});
}
}
if (obj.selfReference && existing.obj.provenanceHistory) {
const existingProvenance = existing.obj.selfReference
? `${existing.obj.selfReference.file}:${existing.obj.selfReference.line}`
: 'unknown';
const newProvenance = `${obj.selfReference.file}:${obj.selfReference.line}`;
if (newProvenance !== existingProvenance) {
existing.obj.provenanceHistory.push({
file: obj.selfReference.file,
line: obj.selfReference.line,
pattern: obj.selfReference.pattern
});
provenancePreservedCount++;
}
}
// Update with latest object but preserve history
Object.assign(existing.obj, obj);
if (existing.obj.provenanceHistory) {
existing.obj.provenanceHistory = existing.obj.provenanceHistory;
}
duplicateCount++;
continue; // Skip duplicate, already have merged version
}
// First occurrence: initialize provenance history
const provenanceHistory = [];
if (obj.selfReference) {
provenanceHistory.push({
file: obj.selfReference.file,
line: obj.selfReference.line,
pattern: obj.selfReference.pattern
});
}
seenIds.set(obj.id, { obj, provenanceHistory });
}
deduplicated.unshift(obj);
}
// Update objects array with deduplicated version
this.objects = deduplicated;
if (duplicateCount > 0) {
console.log(`🧹 Removed ${duplicateCount} duplicate objects before save (provenance preserved)`);
}
if (provenancePreservedCount > 0) {
console.log(`📋 Preserved provenance history for ${provenancePreservedCount} objects`);
}
const jsonlContent = this.objects.map(obj => JSON.stringify(obj)).join('\n');
writeFileSync(this.filePath, jsonlContent + '\n');
console.log(`✅ Saved ${this.objects.length} unique objects to ${this.filePath}`);
}
getAutomatonByDimension(level) {
const automata = this.objects.filter(obj => obj.type === 'automaton' &&
obj.dimensionalLevel === level);
return automata.length > 0 ? automata[0] : null;
}
getCurrentAutomaton() {
return this.getAutomatonByDimension(this.currentDimension);
}
getVerticalTransition(fromId) {
const transitions = this.objects.filter(obj => obj.type === 'vertical' &&
obj.fromNode === fromId);
return transitions.length > 0 ? transitions[0] : null;
}
evaluateCondition(condition, context = {}) {
switch (condition) {
case 'true':
return true;
case 'line_number < ∞':
return true;
case 'file_exists':
return existsSync(this.filePath);
case 'observation':
return Math.random() > 0.7; // Random observation
case 'unifiable(a,b)':
return Math.random() > 0.3;
case 'numeric(m,n)':
return Math.random() > 0.4;
case 'majority_agree':
return Math.random() > 0.5;
case 'gradient_descent':
return Math.random() > 0.6;
default:
if (condition.includes('step_count')) {
const stepCount = context.stepCount || 0;
return stepCount > 3; // Force progression after a few steps
}
return true;
}
}
executeAction(action, fromState, toState, context = {}) {
console.log(`Executing action: ${action} from ${fromState} to ${toState}`);
switch (action) {
case 'self-reference':
this.executeSelfReference();
break;
case 'evolve':
this.executeEvolution();
break;
case 'self-modify':
this.executeSelfModification();
break;
case 'compose':
this.executeComposition();
break;
case 'self-io':
this.executeSelfIO();
break;
case 'validate-self':
this.executeSelfValidation();
break;
case 'self-train':
this.executeSelfTraining();
break;
case 'self-observe':
this.executeSelfObservation();
break;
default:
console.log(`Unknown action: ${action}`);
}
this.executionHistory.push(`${action}:${fromState}→${toState}`);
// Trim execution history to prevent memory leaks
if (this.executionHistory.length > this.MAX_EXECUTION_HISTORY) {
this.executionHistory = this.executionHistory.slice(-this.MAX_EXECUTION_HISTORY);
}
}
executeSelfReference() {
const currentDimension = this.currentDimension;
const churchEncoding = this.generateChurchEncoding(currentDimension);
const selfRef = {
id: `self-ref-${Date.now()}`,
type: 'text',
currentState: 'referencing',
dimensionalLevel: currentDimension,
selfReference: {
file: this.filePath,
line: this.objects.length,
pattern: churchEncoding.pattern
},
x: 800 + Math.random() * 200,
y: Math.random() * 200,
width: 320,
height: 160,
color: String(currentDimension + 1),
text: churchEncoding.code
};
this.objects.push(selfRef);
console.log(`Added self-reference #${this.selfModificationCount}: ${churchEncoding.pattern}`);
}
generateChurchEncoding(dimension) {
switch (dimension) {
case 0:
return {
code: `;; 0D Church Boolean/Identity
(define true (lambda (t f) t))
(define false (lambda (t f) f))
(define identity (lambda (x) x))
(define zero (lambda (f) (lambda (x) x)))
;; Quantum vacuum topology
(lambda (x) x) ;; Self-referential identity`,
pattern: 'Church Boolean/Identity (0D)'
};
case 1:
return {
code: `;; 1D Church Successor
(define succ (lambda (n)
(lambda (f) (lambda (x)
(f ((n f) x))))))
(define one (lambda (f) (lambda (x) (f x))))
;; Temporal evolution
(lambda (n f x) (f (n f x))) ;; Successor pattern`,
pattern: 'Church Successor (1D)'
};
case 2:
return {
code: `;; 2D Church Pairs
(define cons (lambda (x y)
(lambda (f) (f x y))))
(define car (lambda (p)
(p (lambda (x y) x)))
(define cdr (lambda (p)
(p (lambda (x y) y))))
;; Bipartite structure
(lambda (x y f) (f x y)) ;; Pair constructor`,
pattern: 'Church Pairs (2D)'
};
case 3:
return {
code: `;; 3D Church Algebra
(define add (lambda (m n)
(lambda (f) (lambda (x)
((m f) ((n f) x))))))
(define mult (lambda (m n)
(lambda (f) (m (n f)))))
(define exp (lambda (m n) (n m)))
;; Y-combinator for recursion
(define Y (lambda (f)
((lambda (x) (f (lambda (y)
((x x) y))))
(lambda (x) (f (lambda (y)
((x x) y)))))))`,
pattern: 'Church Algebra + Y-Combinator (3D)'
};
case 4:
return {
code: `;; 4D Network Topology
(define ipv4-addr (lambda (a b c d)
(cons a (cons b (cons c d)))))
(define ipv6-addr (lambda (parts)
(foldr cons '() parts)))
(define localhost (cons 127 (cons 0 (cons 0 (cons 1 '())))))
;; Spacetime structure
(lambda (topology network)
(cons topology network)) ;; 4D manifold`,
pattern: 'Network Topology (4D)'
};
case 5:
return {
code: `;; 5D Blockchain Consensus
(define merkle-root (lambda (leaves)
(if (= (length leaves) 1)
(car leaves)
(merkle-root
(map hash-pair
(pair-up leaves))))))
(define block (lambda (data prev-hash)
(cons data (cons prev-hash
(hash (cons data prev-hash))))))
(define chain (lambda (blocks)
(foldl validate-genesis blocks)))
;; Immutable ledger
(lambda (transactions state)
(append state transactions)) ;; Consensus`,
pattern: 'Blockchain Consensus (5D)'
};
case 6:
return {
code: `;; 6D Neural Networks
(define neuron (lambda (weights bias activation)
(lambda (input)
(activation (+ (dot-product weights input) bias)))))
(define layer (lambda (neurons)
(lambda (inputs)
(map (lambda (n) (n inputs)) neurons))))
(define attention (lambda (query key value)
(softmax (scale (matmul query (transpose key))
(sqrt (dim key))))))
;; Emergent intelligence
(lambda (data model)
((model data) data)) ;; Self-attention`,
pattern: 'Neural Networks + Attention (6D)'
};
case 7:
return {
code: `;; 7D Quantum Computing
(define qubit (lambda (alpha beta)
(cons alpha (cons beta '()))))
(define hadamard (lambda (q)
(let ((alpha (car q)) (beta (cadr q)))
(qubit
(/ (+ alpha beta) (sqrt 2))
(/ (- alpha beta) (sqrt 2))))))
(define cnot (lambda (control target)
(if (= (real-part (car control)) 1)
(pauli-x target)
target)))
;; Quantum superposition
(lambda (state)
(normalize (map amplify state))) ;; Qubit evolution`,
pattern: 'Quantum Computing (7D)'
};
default:
return {
code: `;; Higher-Dimensional Structure
(define meta-lambda (lambda (f)
(lambda (x) (f (lambda (y) x)))))
(define self-modify (lambda (program)
(program program)))
;; Metaversal topology
(lambda (canvas dimension)
(embed canvas dimension)) ;; Meta-structure`,
pattern: `Meta-Structure (${dimension}D)`
};
}
}
executeEvolution() {
const nextDimension = (this.currentDimension + 1) % 8;
this.currentDimension = nextDimension;
const topologyCode = this.generateTopologyCode(nextDimension);
// Ensure topologyCode has required fields
const safePattern = topologyCode.pattern || 'unknown';
const safeCode = typeof topologyCode.code === 'string' ? topologyCode.code : '';
const evolvedState = {
id: `${nextDimension}D-topology`,
type: 'text',
currentState: 'evolved',
dimensionalLevel: typeof nextDimension === 'number' ? nextDimension : 0,
selfReference: {
file: typeof this.filePath === 'string' ? this.filePath : 'automaton-kernel.jsonl',
line: Array.isArray(this.objects) ? this.objects.length : 0,
pattern: safePattern
},
x: -600 + (nextDimension % 3) * 300,
y: 180 + Math.floor(nextDimension / 3) * 200,
width: 280,
height: 140 + (nextDimension * 10),
color: String((nextDimension % 7) + 1),
text: safeCode
};
// Validate evolvedState is a proper object before pushing
if (evolvedState && typeof evolvedState === 'object' && !Array.isArray(evolvedState)) {
if (!Array.isArray(this.objects)) {
this.objects = [];
}
this.objects.push(evolvedState);
// Reduced verbosity - only log evolution in verbose mode
if (process.env.VERBOSE === 'true') {
console.log(`Evolved to ${safePattern}: ${evolvedState.id}`);
}
}
else {
console.error('Failed to create valid evolved state:', evolvedState);
}
}
generateTopologyCode(dimension) {
const topologyPatterns = {
0: {
code: `;; 0D Quantum Vacuum Topology
(define vacuum '())
(define point (lambda (x) x))
(define trivial-fiber (lambda (space)
(cons space '())))
;; Base topological space
"Empty pattern: ()"
"Point topology"
"Trivial fiber bundle"
"Base: ∅"
;; The primordial topological space
(lambda () '()) ;; Void constructor`,
pattern: 'Quantum Vacuum Topology (0D)'
},
1: {
code: `;; 1D Temporal Topology
(define line-topology (lambda (points)
(sort points <)))
(define time-fiber (lambda (space instant)
(cons space instant)))
(define ordered-set (lambda (elements)
(foldr cons '() elements)))
;; One-dimensional manifold
"Line topology ℝ¹"
"Time fiber over 0D"
"Ordered set structure"
"Base: 0D-topology"
;; Temporal procedure emergence
(lambda (space)
(time-fiber space 'now)) ;; Time constructor`,
pattern: 'Temporal Topology (1D)'
},
2: {
code: `;; 2D Bipartite Topology
(define product-topology (lambda (top1 top2)
(cons top1 top2)))
(define left-partition (lambda (bipartite)
(car bipartite)))
(define right-partition (lambda (bipartite)
(cdr bipartite)))
;; Church pair topology
"Bipartite topology: 1D × 1D"
"Left partition (data)"
"Right partition (code)"
"Base: 1D-topology"
;; Spatial structure emergence
(lambda (data code)
(product-topology data code)) ;; Bipartite constructor`,
pattern: 'Bipartite Topology (2D)'
},
3: {
code: `;; 3D Manifold Structure
(define volume-topology (lambda (surface)
(embed surface 3)))
(define connected-components (lambda (space)
(find-components space)))
(define fundamental-group (lambda (space)
(compute-loops space)))
;; Three-dimensional manifolds
"Base: 2D-topology"
"Volumetric topology"
"Connected components"
"Fundamental group"
;; Continuous geometric structures
(lambda (surface)
(volume-topology surface)) ;; 3D embedding`,
pattern: '3-Manifold Structure (3D)'
},
4: {
code: `;; 4D Spacetime Structure
(define spacetime (lambda (space time)
(make-manifold space time 4)))
(define minkowski-metric (lambda (event)
(compute-interval event)))
(define light-cone (lambda (event)
(future-past event)))
;; Spacetime manifold
"Base: 3D-topology"
"Lorentzian metric"
"Causal structure"
"Event horizon"
;; Four-dimensional physics
(lambda (space time)
(spacetime space time)) ;; Spacetime constructor`,
pattern: 'Spacetime Structure (4D)'
},
5: {
code: `;; 5D Consensus Topology
(define consensus-space (lambda (participants)
(byzantine-agreement participants)))
(define immutable-ledger (lambda (transactions)
(merkle-tree transactions)))
(define distributed-truth (lambda (network)
(global-consensus network)))
;; Consensus dimension
"Base: 4D-spacetime"
"Distributed agreement"
"Immutable history"
"Global truth"
;; Blockchain topology
(lambda (network)
(consensus-space network)) ;; Consensus constructor`,
pattern: 'Consensus Topology (5D)'
},
6: {
code: `;; 6D Intelligence Topology
(define neural-manifold (lambda (neurons layers)
(construct-network neurons layers)))
(define attention-landscape (lambda (queries keys)
(compute-attention queries keys)))
(define emergent-intelligence (lambda (data model)
(train-model data model)))
;; AI dimension
"Base: 5D-consensus"
"Neural architecture"
"Attention mechanisms"
"Learning dynamics"
;; Emergent AI
(lambda (data)
(neural-manifold data 6)) ;; Intelligence constructor`,
pattern: 'Intelligence Topology (6D)'
},
7: {
code: `;; 7D Quantum Superposition
(define quantum-manifold (lambda (states amplitudes)
(normalize (map cons states amplitudes))))
(define bloch-sphere (lambda (qubit)
(parameterize qubit)))
(define multiverse-branch (lambda (universe measurement)
(branch-universes universe measurement)))
;; Quantum topology
"Base: 6D-intelligence"
"Quantum superposition"
"Entanglement networks"
"Many-worlds branching"
;; Quantum metaverse
(lambda (states)
(quantum-manifold states (map (lambda (s) 1) states))) ;; Quantum constructor`,
pattern: 'Quantum Superposition (7D)'
}
};
return topologyPatterns[dimension] || {
code: `;; Higher-Dimensional Extension
(define meta-topology (lambda (dimensions)
(construct-manifold dimensions)))
(define trans-dimensional (lambda (lower-dim higher-dim)
(embed lower-dim higher-dim)))
;; Metaversal structure
"Base: Previous dimension"
"Higher-dimensional embedding"
"Trans-dimensional bridges"
"Meta-topological structure"
;; Meta-structure
(lambda (dimension)
(meta-topology dimension)) ;; Meta-constructor`,
pattern: `Meta-Topology (${dimension}D)`
};
}
executeSelfModification() {
const currentDimension = this.currentDimension;
const modificationCode = this.generateModificationCode(currentDimension);
const modification = {
id: `modification-${Date.now()}`,
type: 'text',
currentState: 'modified',
dimensionalLevel: currentDimension,
selfReference: {
file: this.filePath,
line: this.objects.length,
pattern: modificationCode.pattern
},
x: 400 + Math.random() * 200,
y: 300 + Math.random() * 200,
width: 280,
height: 140,
color: String((currentDimension + 3) % 7 + 1),
text: modificationCode.code
};
this.objects.push(modification);
this.selfModificationCount++;
// Reduced verbosity - only log major milestones (every 500th) or in verbose mode
if (process.env.VERBOSE === 'true' || this.selfModificationCount % 500 === 0) {
console.log(`Added self-modification #${this.selfModificationCount}: ${modificationCode.pattern}`);
}
}
generateModificationCode(dimension) {
const modificationPatterns = {
0: {
code: `;; 0D Self-Modification: Identity Evolution
(define self-evolve (lambda (identity)
(lambda (x) (identity x))))
(define vacuum-fluctuation (lambda (void)
(cons 'quantum 'fluctuation)))
(define identity-mutation (lambda (f)
(compose f f)))
;; Modify the void
"Self-reference mutation"
"Identity transformation"
"Vacuum fluctuation"
"Meta-identity"
;; 0D evolution
(lambda (x)
(self-evolve (lambda (y) y))) ;; Identity of identity`,
pattern: 'Identity Evolution (0D)'
},
1: {
code: `;; 1D Self-Modification: Successor Recursion
(define succ-recursion (lambda (n)
(if (= n 0) 1 (+ (succ-recursion (- n 1)) 1))))
(define temporal-mutation (lambda (successor)
(lambda (n) (successor (successor n)))))
(define evolution-chain (lambda (n)
(iterate succ n 0)))
;; Modify time
"Successor recursion"
"Temporal acceleration"
"Evolution chain"
"Meta-successor"
;; 1D evolution
(lambda (n)
(temporal-mutation succ)) ;; Successor of successor`,
pattern: 'Successor Recursion (1D)'
},
2: {
code: `;; 2D Self-Modification: Pair Restructuring
(define pair-mutate (lambda (pair)
(cons (cdr pair) (car pair))))
(define structure-evolution (lambda (pairs)
(map pair-mutate pairs)))
(define bipartite-reorganization (lambda (left right)
(cons (reorganize left) (reorganize right))))
;; Modify structure
"Pair swapping"
"Data reorganization"
"Bipartite restructuring"
"Meta-pairing"
;; 2D evolution
(lambda (pair)
(pair-mutate pair)) ;; Self-restructuring`,
pattern: 'Pair Restructuring (2D)'
},
3: {
code: `;; 3D Self-Modification: Algebraic Transformation
(define algebra-mutate (lambda (operation)
(lambda (m n) (operation n m))))
(define operator-evolution (lambda (ops)
(map algebra-mutate ops)))
(define ring-transformation (lambda (ring)
(make-ring (reverse (ring-operations ring)))))
;; Modify algebra
"Operation commutation"
"Operator evolution"
"Ring transformation"
"Meta-algebra"
;; 3D evolution
(lambda (m n)
((algebra-mutate add) m n)) ;; Commutative addition`,
pattern: 'Algebraic Transformation (3D)'
},
4: {
code: `;; 4D Self-Modification: Network Rewiring
(define network-mutate (lambda (graph)
(rewire-edges graph 0.1)))
(define spacetime-evolution (lambda (manifold)
(deform-manifold manifold 'time)))
(define protocol-upgrade (lambda (network protocol)
(upgrade-nodes network protocol)))
;; Modify networks
"Graph rewiring"
"Spacetime deformation"
"Protocol upgrade"
"Meta-network"
;; 4D evolution
(lambda (network)
(network-mutate network)) ;; Self-rewiring`,
pattern: 'Network Rewiring (4D)'
},
5: {
code: `;; 5D Self-Modification: Consensus Protocol Evolution
(define consensus-mutate (lambda (protocol)
(hard-fork protocol new-rules)))
(define ledger-reorganization (lambda (chain)
(reorg-chain chain new-consensus)))
(define governance-evolution (lambda (dao)
(upgrade-dao dao new-constitution)))
;; Modify consensus
"Protocol hard fork"
"Ledger reorganization"
"Governance evolution"
"Meta-consensus"
;; 5D evolution
(lambda (protocol)
(consensus-mutate protocol)) ;; Self-governance`,
pattern: 'Consensus Evolution (5D)'
},
6: {
code: `;; 6D Self-Modification: Neural Architecture Evolution
(define neural-mutate (lambda (network)
(neuroplasticity network learning-rate)))
(define attention-evolution (lambda (mechanism)
(multi-head-attention mechanism heads+1)))
(define intelligence-growth (lambda (model)
(scale-model model growth-factor)))
;; Modify intelligence
"Neuroplasticity"
"Attention expansion"
"Model scaling"
"Meta-intelligence"
;; 6D evolution
(lambda (network)
(neural-mutate network)) ;; Self-improvement`,
pattern: 'Neural Evolution (6D)'
},
7: {
code: `;; 7D Self-Modification: Quantum State Evolution
(define quantum-mutate (lambda (state)
(unitary-evolution state hamiltonian)))
(define superposition-evolution (lambda (amplitudes)
(normalize (map evolve amplitudes))))
(define multiverse-splitting (lambda (universe)
(branch universe measurement-basis)))
;; Modify quantum reality
"Unitary evolution"
"Amplitude transformation"
"Universe branching"
"Meta-quantum"
;; 7D evolution
(lambda (state)
(quantum-mutate state)) ;; Self-evolution`,
pattern: 'Quantum Evolution (7D)'
}
};
return modificationPatterns[dimension] || {
code: `;; Higher-Dimensional Self-Modification
(define meta-evolution (lambda (system)
(upgrade-system system next-dimension)))
(define trans-dimensional-mutation (lambda (entity)
(embed-entity entity higher-space)))
(define meta-structure-evolution (lambda (structure)
(complexify-structure structure)))
;; Modify meta-structure
"Dimensional upgrade"
"Trans-dimensional embedding"
"Meta-complexification"
"Hyper-evolution"
;; Meta-evolution
(lambda (system)
(meta-evolution system)) ;; Self-transcendence`,
pattern: `Meta-Evolution (${dimension}D)`
};
}
executeComposition() {
const automata = this.objects.filter(obj => obj.type === 'automaton');
if (automata.length >= 2) {
console.log(`Composed automata: ${automata[0].id} + ${automata[1].id}`);
}
}
async executeSelfIO() {
await this.load();
console.log(`Performed self-I/O: read ${this.objects.length} objects`);
}
executeSelfValidation() {
const automata = this.objects.filter(obj => obj.type === 'automaton');
const validAutomata = automata.filter(obj => {
return obj.selfReference && obj.dimensionalLevel >= 0 && obj.dimensionalLevel <= 7;
});
console.log(`Validated ${validAutomata.length}/${automata.length} automata`);
}
executeSelfTraining() {
const actionCounts = new Map();
this.executionHistory.forEach(entry => {
let action = 'unknown';
if (typeof entry === 'string') {
action = entry.split(':')[0] || 'unknown';
}
else if (entry && typeof entry === 'object' && 'action' in entry) {
action = entry.action || 'unknown';
}
actionCounts.set(action, (actionCounts.get(action) || 0) + 1);
});
console.log('Learned action frequencies:');
actionCounts.forEach((count, action) => {
console.log(` ${action}: ${count}`);
});
}
executeSelfObservation() {
const currentAutomaton = this.getCurrentAutomaton();
if (currentAutomaton) {
console.log(`Self-observation: Currently at ${currentAutomaton.currentState} (dimension ${currentAutomaton.dimensionalLevel})`);
}
// Collapse back to 0D after observation
console.log('Quantum collapse: returning to 0D');
this.currentDimension = 0;
}
step(stepCount = 0) {
const currentAutomaton = this.getCurrentAutomaton();
if (!currentAutomaton) {
console.log('No current automaton found');
return;
}
// First try horizontal transitions
const horizontalTransitions = this.objects.filter(obj => obj.type === 'transition' &&
obj.from === currentAutomaton.id);
// Then try vertical transitions for dimensional progression
const verticalTransition = this.getVerticalTransition(currentAutomaton.id);
// Prioritize vertical transitions for progression
const transitions = verticalTransition ? [verticalTransition] : horizontalTransitions;
if (transitions.length === 0) {
console.log(`No transitions from ${currentAutomaton.id}`);
return;
}
// Execute first valid transition
for (const transition of transitions) {
const condition = transition.condition || 'true';
const context = { stepCount };
if (this.evaluateCondition(condition, context)) {
const action = transition.action || 'evolve';
const fromId = transition.from || transition.fromNode;
const toId = transition.to || transition.toNode;
this.executeAction(action, fromId, toId, context);
// Update current dimension based on target
const targetAutomaton = this.objects.find(obj => obj.id === toId);
if (targetAutomaton) {
this.currentDimension = targetAutomaton.dimensionalLevel;
console.log(`Transitioned to dimension ${targetAutomaton.dimensionalLevel}: ${targetAutomaton.id}`);
}
break;
}
}
}
run(steps = 20) {
console.log(`Running advanced self-referencing automaton for ${steps} steps...`);
for (let i = 0; i < steps; i++) {
console.log(`\n--- Step ${i + 1} (Dimension ${this.currentDimension}) ---`);
this.step(i);
// Force progression if stuck
if (this.executionHistory.length > 3 &&
this.executionHistory.slice(-3).every(h => {
if (typeof h === 'string') {
return h.includes('self-reference');
}
else if (h && typeof h === 'object' && 'action' in h) {
return h.action === 'self-reference';
}
return false;
})) {
console.log('Forcing dimensional progression...');
const verticalTransition = this.getVerticalTransition(`0D-automaton`);
if (verticalTransition) {
this.currentDimension = (this.currentDimension + 1) % 8;
}
}
}
console.log('\n=== Execution Summary ===');
console.log(`Total steps: ${steps}`);
console.log(`Final dimension: ${this.currentDimension}`);
console.log(`Self-modifications: ${this.selfModificationCount}`);
console.log(`Execution history: ${this.executionHistory.length} actions`);
// Save any modifications or evolutions
if (this.selfModificationCount > 0 || this.executionHistory.length > 0) {
this.save();
}
// Trigger GC if available (Node.js with --expose-gc flag)
if (global.gc) {
global.gc();
}
}
/**
* Optimize memory by trimming history and deduplicating objects
*/
optimizeMemory() {
// Trim execution history
if (this.executionHistory.length > this.MAX_EXECUTION_HISTORY) {
this.executionHistory = this.executionHistory.slice(-this.MAX_EXECUTION_HISTORY);
}
// Deduplicate objects
const deduplicated = [];
const seenIds = new Set();
let duplicateCount = 0;
for (let i = this.objects.length - 1; i >= 0; i--) {
const obj = this.objects[i];
if (obj.id && seenIds.has(obj.id)) {
duplicateCount++;
continue;
}
if (obj.id) {
seenIds.add(obj.id);
}
deduplicated.unshift(obj);
}
if (duplicateCount > 0) {
console.log(`🧹 Memory optimization: Removed ${duplicateCount} duplicate objects`);
this.objects = deduplicated;
}
// Trigger GC if available
if (global.gc) {
global.gc();
}
}
printState() {
console.log('=== Advanced Self-Referencing Automaton State ===');
console.log(`File: ${this.filePath}`);
console.log(`Total objects: ${this.objects.length}`);
console.log(`Current dimension: ${this.currentDimension}`);
console.log(`Self-modifications: ${this.selfModificationCount}`);
const currentAutomaton = this.getCurrentAutomaton();
if (currentAutomaton) {
console.log(`Current automaton: ${currentAutomaton.id}`);
console.log(`State: ${currentAutomaton.currentState}`);
console.log(`Dimension: ${currentAutomaton.dimensionalLevel}`);
console.log(`Self-reference: ${JSON.stringify(currentAutomaton.selfReference)}`);
}
console.log(`Execution history: ${this.executionHistory.length} actions`);
if (this.executionHistory.length > 0) {
console.log('Recent actions:');
this.executionHistory.slice(-5).forEach(entry => {
if (typeof entry === 'string') {
console.log(` ${entry}`);
}
else if (entry && typeof entry === 'object' && 'action' in entry) {
console.log(` ${entry.action}${entry.from && entry.to ? ` (${entry.from} → ${entry.to})` : ''}`);
}
else {
console.log(` ${JSON.stringify(entry)}`);
}
});
}
}
analyzeSelfReference() {
const selfRefs = this.objects.filter(obj => obj.type === 'file' &&
obj.file === this.filePath);
const automata = this.objects.filter(obj => obj.type === 'automaton');
console.log('=== Self-Reference Analysis ===');
console.log(`Self-reference objects: ${selfRefs.length}`);
console.log(`Automaton objects: ${automata.length}`);
console.log(`Self-modifications: ${this.selfModificationCount}`);
console.log('\nDimensional progression:');
automata.forEach((auto, index) => {
console.log(` ${auto.dimensionalLevel}D: ${auto.id} -> line ${auto.selfReference.line} (${auto.selfReference.pattern})`);
});
if (selfRefs.length > 0) {
console.log('\nDynamic self-references:');
selfRefs.slice(-3).forEach((ref, index) => {
console.log(` ${index + 1}. ${ref.id}`);
if (ref.text) {
console.log(` ${ref.text.substring(0, 60)}...`);
}
});
}
}
}
// Main execution
async function main() {
const db = new MetaLogDb({ enableProlog: true, enableDatalog: true });
const automaton = new AdvancedSelfReferencingAutomaton('./automaton.jsonl', db);
// Initialize by loading the file
await automaton.init();
console.log('=== Advanced Self-Referencing JSONL Automaton ===');
automaton.printState();
automaton.analyzeSelfReference();
// Run the automaton
automaton.run(20);
console.log('\n=== Final State ===');
automaton.printState();
automaton.analyzeSelfReference();
}
// Run if executed directly
if (require.main === module) {
main().catch(console.error);
}
export { AdvancedSelfReferencingAutomaton };
//# sourceMappingURL=advanced-automaton.js.map