feat: make GOFAI worker deterministic and plan-capable

app.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) {
@@ -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();
     

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 });
   }
 };