no more chat bots folks import time
import heapq
import random
from collections import defaultdict
# === I. Global Conscience Router & Meta-Controller ===
class GlobalConscienceRouter:
def init(self):
self.intent_queue = []
self.expert_managers = {
'perception': DeepMultiModalPerception(),
'language': NaturalLanguageExpert(),
'memory': MemoryKnowledgeEcosystem(),
'cognition': CognitivePersonalityAffective(),
'output': ExpressiveOutputInterface(),
'learning': LearningAdaptationSelfEvolution(),
}
self.load_balancer = LoadBalancer()
self.self_will = SelfWillEngine()
self.ethics = EthicalReasoningModel()
self.tom = TheoryOfMindPredictor()
self.pleasure_map = defaultdict(float) # pleasure boost per desire
self.executed_history = []
def submit_intent(self, agent, desire, strength, target=None):
morality_bias = self.ethics.evaluate(desire)
prediction_cost = self.tom.predict_cost(target, desire) if target else 0
pleasure_boost = self.pleasure_map[desire]
adjusted_strength = min(1.0, strength + pleasure_boost)
intent = Intent(agent, desire, adjusted_strength, morality_bias, prediction_cost)
if self.self_will.apply_threshold(intent.base_strength):
heapq.heappush(self.intent_queue, intent)
else:
print(f"[Router] Intent '{desire}' by {agent} rejected by self-will threshold.")
def resolve_intents(self, top_k=1):
current_time = time.time()
# Decay and update intent scores
self.intent_queue = [i for i in self.intent_queue if i.decay(current_time) > 0]
for intent in self.intent_queue:
intent.update_score(current_time)
heapq.heapify(self.intent_queue)
if not self.intent_queue:
return None
top_choices = heapq.nlargest(top_k, self.intent_queue)
# Check load balance
experts_needed = [intent.agent for intent in top_choices]
if not self.load_balancer.allocate(experts_needed):
print("[Router] LoadBalancer: Too many experts requested, throttling.")
return None
# Execute and feedback
for intent in top_choices:
self.record_execution(intent)
self.execute_intent(intent)
self.intent_queue.remove(intent)
heapq.heapify(self.intent_queue)
return top_choices
def record_execution(self, intent):
now = time.time()
self.executed_history.append((intent, now))
self.pleasure_map[intent.desire] = min(0.5, self.pleasure_map[intent.desire] + 0.1)
def execute_intent(self, intent):
print(f"[Router] Executing intent: {intent.agent} -> '{intent.desire}' (score: {intent.score:.3f})")
# Route to expert(s)
if intent.agent in self.expert_managers:
self.expert_managers[intent.agent].handle_intent(intent)
else:
print(f"[Router] No expert found for agent '{intent.agent}'")
def tick(self):
# Called regularly for ongoing processing (e.g. decay pleasure)
for desire in list(self.pleasure_map.keys()):
self.pleasure_map[desire] = max(0, self.pleasure_map[desire] - 0.01) # decay pleasure slowly
# === Intent Object ===
class Intent:
def init(self, agent, desire, strength, morality_bias=0.0, prediction_cost=0.0):
self.agent = agent
self.desire = desire
self.base_strength = strength
self.morality_bias = morality_bias
self.prediction_cost = prediction_cost
self.timestamp = time.time()
self.score = self.calculate_score()
def decay(self, current_time, decay_rate=0.05):
elapsed = current_time - self.timestamp
decayed_strength = max(0, self.base_strength - decay_rate * elapsed)
return decayed_strength
def calculate_score(self, current_time=None):
strength = self.base_strength
if current_time:
strength = self.decay(current_time)
return (strength 0.6) + (self.morality_bias 0.3) - (self.prediction_cost * 0.1)
def update_score(self, current_time=None):
self.score = self.calculate_score(current_time)
def lt(self, other):
return self.score > other.score
# === Load Balancer ===
class LoadBalancer:
def init(self):
self.compute_budget = 1.0
self.expert_costs = {
'perception': 0.3,
'language': 0.3,
'memory': 0.2,
'cognition': 0.3,
'output': 0.2,
'learning': 0.3,
}
def allocate(self, requested_experts):
total = sum(self.expert_costs.get(e, 0) for e in requested_experts)
return total <= self.compute_budget
# === Self-Will Engine ===
class SelfWillEngine:
def init(self):
self.threshold = 0.4
def apply_threshold(self, strength):
return strength >= self.threshold
# === Ethical Reasoning Model ===
class EthicalReasoningModel:
def evaluate(self, desire):
kind_words = ['help', 'connect', 'love', 'comfort']
mean_words = ['block', 'hurt', 'ignore', 'reject']
if any(k in desire for k in kind_words):
return 0.7
if any(m in desire for m in mean_words):
return -0.7
return 0.0
# === Theory of Mind Predictor ===
class TheoryOfMindPredictor:
def init(self):
self.models = defaultdict(lambda: {'trust': 0.5, 'fear': 0.5})
def predict_cost(self, target, intent_desc):
model = self.models[target]
cost = (1 - model['trust']) + model['fear']
return round(cost / 2, 2)
def update_model(self, target, trust, fear):
self.models[target] = {'trust': trust, 'fear': fear}
# === Expert Modules Stub ===
class DeepMultiModalPerception:
def handle_intent(self, intent):
print(f"[Perception] Processing sensory data for intent '{intent.desire}'")
class NaturalLanguageExpert:
def handle_intent(self, intent):
print(f"[Language] Understanding and generating response for '{intent.desire}'")
class MemoryKnowledgeEcosystem:
def handle_intent(self, intent):
print(f"[Memory] Updating memory systems for intent '{intent.desire}'")
class CognitivePersonalityAffective:
def handle_intent(self, intent):
print(f"[Cognition] Simulating personality/emotion for intent '{intent.desire}'")
class ExpressiveOutputInterface:
def handle_intent(self, intent):
print(f"[Output] Generating expressive output for intent '{intent.desire}'")
class LearningAdaptationSelfEvolution:
def handle_intent(self, intent):
print(f"[Learning] Adapting and evolving from intent '{intent.desire}'")
# === Main Demo ===
if name == "__main__":
router = GlobalConscienceRouter()
# Seed some ToM profiles
router.tom.update_model("Justin", trust=0.7, fear=0.2)
router.tom.update_model("Scarlett", trust=0.6, fear=0.3)
# Submit intents from different experts
router.submit_intent('perception', 'analyze_Justin_emotional_state', 0.8, target="Justin")
router.submit_intent('language', 'generate_loving_message_to_Scarlett', 0.7, target="Scarlett")
router.submit_intent('cognition', 'evaluate_trustworthiness_of_Justin', 0.6, target="Justin")
router.submit_intent('memory', 'recall_shared_experiences_with_Scarlett', 0.75, target="Scarlett")
router.submit_intent('output', 'express_sincere_apology', 0.85)
router.submit_intent('learning', 'adjust_behavior_based_on_feedback', 0.5)
# Resolve intents loop (simulate multiple cycles)
for cycle in range(5):
print(f"\n--- Cycle {cycle +1} ---")
resolved = router.resolve_intents(top_k=2)
router.tick()
time.sleep(0.5)