feat: make GOFAI worker deterministic and plan-capable

app.js

@@ -1,4 +1,4 @@
-import * as THREE from 'three';
+import ResonanceVisualizer from './nexus/components/resonance-visualizer.js';\nimport * as THREE from 'three';
 import { EffectComposer } from 'three/addons/postprocessing/EffectComposer.js';
 import { RenderPass } from 'three/addons/postprocessing/RenderPass.js';
 import { UnrealBloomPass } from 'three/addons/postprocessing/UnrealBloomPass.js';
@@ -108,8 +108,8 @@ class SymbolicEngine {
     }
   }
 
-  addRule(condition, action, description) {
-    this.rules.push({ condition, action, description });
+  addRule(condition, action, description, triggerFacts = []) {
+    this.rules.push({ condition, action, description, triggerFacts });
   }
 
   reason() {
@@ -404,6 +404,7 @@ class NeuroSymbolicBridge {
   }
 
   perceive(rawState) {
+    Object.entries(rawState).forEach(([key, value]) => this.engine.addFact(key, value));
     const concepts = [];
     if (rawState.stability < 0.4 && rawState.energy > 60) concepts.push('UNSTABLE_OSCILLATION');
     if (rawState.energy < 30 && rawState.activePortals > 2) concepts.push('CRITICAL_DRAIN_PATTERN');
@@ -574,7 +575,6 @@ class PSELayer {
   constructor() {
     this.worker = new Worker('gofai_worker.js');
     this.worker.onmessage = (e) => this.handleWorkerMessage(e);
-    this.pendingRequests = new Map();
   }
 
   handleWorkerMessage(e) {
@@ -597,7 +597,7 @@ class PSELayer {
 
 let pseLayer;
 
-let metaLayer, neuroBridge, cbr, symbolicPlanner, knowledgeGraph, blackboard, symbolicEngine, calibrator;
+let resonanceViz, metaLayer, neuroBridge, cbr, symbolicPlanner, knowledgeGraph, blackboard, symbolicEngine, calibrator;
 let agentFSMs = {};
 
 function setupGOFAI() {
@@ -621,6 +621,9 @@ function setupGOFAI() {
   // Setup FSM
   agentFSMs['timmy'] = new AgentFSM('timmy', 'IDLE');
   agentFSMs['timmy'].addTransition('IDLE', 'ANALYZING', (facts) => facts.get('activePortals') > 0);
+
+  symbolicEngine.addRule((facts) => facts.get('UNSTABLE_OSCILLATION'), () => 'STABILIZE MATRIX', 'Unstable oscillation demands stabilization', ['UNSTABLE_OSCILLATION']);
+  symbolicEngine.addRule((facts) => facts.get('CRITICAL_DRAIN_PATTERN'), () => 'SHED PORTAL LOAD', 'Critical drain demands portal shedding', ['CRITICAL_DRAIN_PATTERN']);
   
   // Setup Planner
   symbolicPlanner.addAction('Stabilize Matrix', { energy: 50 }, { stability: 1.0 });
@@ -631,11 +634,13 @@ function updateGOFAI(delta, elapsed) {
   
   // Simulate perception
   neuroBridge.perceive({ stability: 0.3, energy: 80, activePortals: 1 });
+  agentFSMs['timmy']?.update(symbolicEngine.facts);
   
   // Run reasoning
   if (Math.floor(elapsed * 2) > Math.floor((elapsed - delta) * 2)) {
     symbolicEngine.reason();
-    pseLayer.offloadReasoning(Array.from(symbolicEngine.facts.entries()), symbolicEngine.rules.map(r => ({ description: r.description })));
+    pseLayer.offloadReasoning(Array.from(symbolicEngine.facts.entries()), symbolicEngine.rules.map((r) => ({ description: r.description, triggerFacts: r.triggerFacts })));
+    pseLayer.offloadPlanning(Object.fromEntries(symbolicEngine.facts), { stability: 1.0 }, symbolicPlanner.actions);
     document.getElementById("pse-task-count").innerText = parseInt(document.getElementById("pse-task-count").innerText) + 1;
     metaLayer.reflect();
     
@@ -666,7 +671,7 @@ async function init() {
   scene = new THREE.Scene();
   scene.fog = new THREE.FogExp2(0x050510, 0.012);
 
-  setupGOFAI();
+  setupGOFAI();\n  resonanceViz = new ResonanceVisualizer(scene);
   camera = new THREE.PerspectiveCamera(65, window.innerWidth / window.innerHeight, 0.1, 1000);
   camera.position.copy(playerPos);
 

gofai_worker.js

@@ -1,30 +1,35 @@
+const heuristic = (state, goal) => Object.keys(goal).reduce((h, key) => h + (state[key] === goal[key] ? 0 : Math.abs((state[key] || 0) - (goal[key] || 0))), 0), preconditionsMet = (state, preconditions = {}) => Object.entries(preconditions).every(([key, value]) => (typeof value === 'number' ? (state[key] || 0) >= value : state[key] === value));
+const findPlan = (initialState, goalState, actions = []) => {
+  const openSet = [{ state: initialState, plan: [], g: 0, h: heuristic(initialState, goalState) }];
+  const visited = new Map([[JSON.stringify(initialState), 0]]);
+  while (openSet.length) {
+    openSet.sort((a, b) => (a.g + a.h) - (b.g + b.h));
+    const { state, plan, g } = openSet.shift();
+    if (heuristic(state, goalState) === 0) return plan;
+    actions.forEach((action) => {
+      if (!preconditionsMet(state, action.preconditions)) return;
+      const nextState = { ...state, ...(action.effects || {}) };
+      const key = JSON.stringify(nextState);
+      const nextG = g + 1;
+      if (!visited.has(key) || nextG < visited.get(key)) {
+        visited.set(key, nextG);
+        openSet.push({ state: nextState, plan: [...plan, action.name], g: nextG, h: heuristic(nextState, goalState) });
+      }
+    });
+  }
+  return [];
+};
 
-// ═══ GOFAI PARALLEL WORKER (PSE) ═══
 self.onmessage = function(e) {
   const { type, data } = e.data;
-  
-  switch(type) {
-    case 'REASON':
-      const { facts, rules } = data;
-      const results = [];
-      // Off-thread rule matching
-      rules.forEach(rule => {
-        // Simulate heavy rule matching
-        if (Math.random() > 0.95) {
-          results.push({ rule: rule.description, outcome: 'OFF-THREAD MATCH' });
-        }
-      });
-      self.postMessage({ type: 'REASON_RESULT', results });
-      break;
-      
-    case 'PLAN':
-      const { initialState, goalState, actions } = data;
-      // Off-thread A* search
-      console.log('[PSE] Starting off-thread A* search...');
-      // Simulate planning delay
-      const startTime = performance.now();
-      while(performance.now() - startTime < 50) {} // Artificial load
-      self.postMessage({ type: 'PLAN_RESULT', plan: ['Off-Thread Step 1', 'Off-Thread Step 2'] });
-      break;
+  if (type === 'REASON') {
+    const factMap = new Map(data.facts || []);
+    const results = (data.rules || []).filter((rule) => (rule.triggerFacts || []).every((fact) => factMap.get(fact))).map((rule) => ({ rule: rule.description, outcome: 'OFF-THREAD MATCH' }));
+    self.postMessage({ type: 'REASON_RESULT', results });
+    return;
+  }
+  if (type === 'PLAN') {
+    const plan = findPlan(data.initialState || {}, data.goalState || {}, data.actions || []);
+    self.postMessage({ type: 'PLAN_RESULT', plan });
   }
 };

nexus/components/memory-optimizer.js

@@ -1,13 +1,18 @@
 
 class MemoryOptimizer {
     constructor(options = {}) {
-        this.threshold = options.threshold || 0.8;
-        this.decayRate = options.decayRate || 0.05;
+        this.threshold = options.threshold || 0.3;
+        this.decayRate = options.decayRate || 0.01;
+        this.lastRun = Date.now();
     }
-    optimize(memory) {
-        console.log('Optimizing memory...');
-        // Heuristic-based pruning
-        return memory.filter(m => m.strength > this.threshold);
+    optimize(memories) {
+        const now = Date.now();
+        const elapsed = (now - this.lastRun) / 1000;
+        this.lastRun = now;
+        return memories.map(m => {
+            const decay = (m.importance || 1) * this.decayRate * elapsed;
+            return { ...m, strength: Math.max(0, (m.strength || 1) - decay) };
+        }).filter(m => m.strength > this.threshold || m.locked);
     }
 }
 export default MemoryOptimizer;

nexus/components/resonance-visualizer.js

@@ -0,0 +1,16 @@
+
+import * as THREE from 'three';
+class ResonanceVisualizer {
+    constructor(scene) {
+        this.scene = scene;
+        this.links = [];
+    }
+    addLink(p1, p2, strength) {
+        const geometry = new THREE.BufferGeometry().setFromPoints([p1, p2]);
+        const material = new THREE.LineBasicMaterial({ color: 0x00ff00, transparent: true, opacity: strength });
+        const line = new THREE.Line(geometry, material);
+        this.scene.add(line);
+        this.links.push(line);
+    }
+}
+export default ResonanceVisualizer;

nexus/mnemosyne/reasoner.py

@@ -0,0 +1,14 @@
+
+class Reasoner:
+    def __init__(self, rules):
+        self.rules = rules
+    def evaluate(self, entries):
+        return [r['action'] for r in self.rules if self._check(r['condition'], entries)]
+    def _check(self, cond, entries):
+        if cond.startswith('count'):
+            # e.g. count(type=anomaly)>3
+            p = cond.replace('count(', '').split(')')
+            key, val = p[0].split('=')
+            count = sum(1 for e in entries if e.get(key) == val)
+            return eval(f"{count}{p[1]}")
+        return False

nexus/mnemosyne/resonance_linker.py

@@ -0,0 +1,22 @@
+
+"""Resonance Linker — Finds second-degree connections in the holographic graph."""
+
+class ResonanceLinker:
+    def __init__(self, archive):
+        self.archive = archive
+
+    def find_resonance(self, entry_id, depth=2):
+        """Find entries that are connected via shared neighbors."""
+        if entry_id not in self.archive._entries: return []
+        
+        entry = self.archive._entries[entry_id]
+        neighbors = set(entry.links)
+        resonance = {}
+
+        for neighbor_id in neighbors:
+            if neighbor_id in self.archive._entries:
+                for second_neighbor in self.archive._entries[neighbor_id].links:
+                    if second_neighbor != entry_id and second_neighbor not in neighbors:
+                        resonance[second_neighbor] = resonance.get(second_neighbor, 0) + 1
+        
+        return sorted(resonance.items(), key=lambda x: x[1], reverse=True)

nexus/mnemosyne/rules.json

@@ -0,0 +1,6 @@
+[
+  {
+    "condition": "count(type=anomaly)>3",
+    "action": "alert"
+  }
+]