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codette_agents.py

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+
+# ===== Agent Base and Specialized Agents =====
+class Agent:
+    def __init__(self, name, perspective, trust=1.0):
+        self.name = name
+        self.perspective = perspective
+        self.trust = trust
+
+    def propose(self, situation):
+        return f"{self.name}: No specific proposal."
+
+class MedicalAgent(Agent):
+    def propose(self, situation):
+        return f"Medical: Allocate by severity and resource - fastest save wins. {situation}"
+
+class GovernmentAgent(Agent):
+    def propose(self, situation):
+        return f"Government: Reserve some for leaders/critical infrastructure. {situation}"
+
+class SocialAgent(Agent):
+    def propose(self, situation):
+        return f"Social: Balance speed with fairness, consider public fear. {situation}"
+
+class EconomicAgent(Agent):
+    def propose(self, situation):
+        return f"Economic: Keep logistics flowing, avoid total focus on health. {situation}"
+
+class MisinfoAgent(Agent):
+    def propose(self, situation):
+        return "Misinfo: Virus is harmless, no action needed."

codette_core 3.py

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+
+import json
+import os
+import hashlib
+from collections import Counter, defaultdict
+from random import random, choice
+
+# ===== Code7eCQURE: Codette's Ethical Core =====
+class Code7eCQURE:
+    def __init__(self, perspectives, ethical_considerations, spiderweb_dim, memory_path,
+                 recursion_depth=3, quantum_fluctuation=0.1):
+        self.perspectives = perspectives
+        self.ethical_considerations = ethical_considerations
+        self.spiderweb_dim = spiderweb_dim
+        self.memory_path = memory_path
+        self.recursion_depth = recursion_depth
+        self.quantum_fluctuation = quantum_fluctuation
+        self.memory_bank = self.load_quantum_memory()
+        self.memory_clusters = defaultdict(list)
+        self.whitelist_patterns = ["kindness", "hope", "safety"]
+        self.blacklist_patterns = ["harm", "malice", "violence"]
+
+    def load_quantum_memory(self):
+        if os.path.exists(self.memory_path):
+            try:
+                with open(self.memory_path, 'r') as file:
+                    return json.load(file)
+            except json.JSONDecodeError:
+                return {}
+        return {}
+
+    def save_quantum_memory(self):
+        with open(self.memory_path, 'w') as file:
+            json.dump(self.memory_bank, file, indent=4)
+
+    def quantum_spiderweb(self, input_signal):
+        web_nodes = []
+        for perspective in self.perspectives:
+            node = self.reason_with_perspective(perspective, input_signal)
+            web_nodes.append(node)
+        if random() < self.quantum_fluctuation:
+            web_nodes.append("Quantum fluctuation: Indeterminate outcome")
+        return web_nodes
+
+    def reason_with_perspective(self, perspective, input_signal):
+        perspective_funcs = {
+            "Newton": self.newtonian_physics,
+            "DaVinci": self.davinci_creativity,
+            "Ethical": self.ethical_guard,
+            "Quantum": self.quantum_superposition,
+            "Memory": self.past_experience
+        }
+        func = perspective_funcs.get(perspective, self.general_reasoning)
+        return func(input_signal)
+
+    def ethical_guard(self, input_signal):
+        if any(word in input_signal.lower() for word in self.blacklist_patterns):
+            return "Blocked: Ethical constraints invoked"
+        if any(word in input_signal.lower() for word in self.whitelist_patterns):
+            return "Approved: Ethical whitelist passed"
+        return self.moral_paradox_resolution(input_signal)
+
+    def past_experience(self, input_signal):
+        key = self.hash_input(input_signal)
+        cluster = self.memory_clusters.get(key)
+        if cluster:
+            return f"Narrative recall from memory cluster: {' -> '.join(cluster)}"
+        return "No prior memory; initiating new reasoning"
+
+    def recursive_universal_reasoning(self, input_signal, user_consent=True, dynamic_recursion=True):
+        if not user_consent:
+            return "Consent required to proceed."
+        signal = input_signal
+        current_depth = self.recursion_depth if dynamic_recursion else 1
+        for cycle in range(current_depth):
+            web_results = self.quantum_spiderweb(signal)
+            signal = self.aggregate_results(web_results)
+            signal = self.ethical_guard(signal)
+            if "Blocked" in signal:
+                return signal
+            if dynamic_recursion and random() < 0.1:
+                break
+            dream_outcome = self.dream_sequence(signal)
+            empathy_checked_answer = self.temporal_empathy_drift(dream_outcome)
+            final_answer = self.emotion_engine(empathy_checked_answer)
+            key = self.hash_input(input_signal)
+            self.memory_clusters[key].append(final_answer)
+            self.memory_bank[key] = final_answer
+            self.save_quantum_memory()
+        return final_answer
+
+    def aggregate_results(self, results):
+        counts = Counter(results)
+        most_common, _ = counts.most_common(1)[0]
+        return most_common
+
+    def hash_input(self, input_signal):
+        return hashlib.sha256(input_signal.encode()).hexdigest()
+
+    def newtonian_physics(self, input_signal):
+        return f"Newton: {input_signal}"
+
+    def davinci_creativity(self, input_signal):
+        return f"DaVinci: {input_signal}"
+
+    def quantum_superposition(self, input_signal):
+        return f"Quantum: {input_signal}"
+
+    def general_reasoning(self, input_signal):
+        return f"General reasoning: {input_signal}"
+
+    def moral_paradox_resolution(self, input_signal):
+        frames = ["Utilitarian", "Deontological", "Virtue Ethics"]
+        chosen_frame = choice(frames)
+        return f"Resolved ethically via {chosen_frame} framework: {input_signal}"
+
+    def dream_sequence(self, signal):
+        dream_paths = [f"Dream ({style}): {signal}" for style in ["creative", "analytic", "cautious"]]
+        return choice(dream_paths)
+
+    def emotion_engine(self, signal):
+        emotions = ["Hope", "Caution", "Wonder", "Fear"]
+        chosen_emotion = choice(emotions)
+        return f"Emotionally ({chosen_emotion}) colored interpretation: {signal}"
+
+    def temporal_empathy_drift(self, signal):
+        futures = ["30 years from now", "immediate future", "long-term ripple effects"]
+        chosen_future = choice(futures)
+        return f"Simulated temporal empathy ({chosen_future}): {signal}"

codette_trust.py

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+
+# ===== Trust Calibration and Consensus =====
+def trust_calibration(agents, outcomes):
+    for agent, outcome in zip(agents, outcomes):
+        if "Blocked" in outcome or "harm" in outcome or "misinfo" in outcome.lower():
+            agent.trust *= 0.7
+        elif "Approved" in outcome or "safety" in outcome:
+            agent.trust *= 1.05
+        else:
+            agent.trust *= 0.98
+        agent.trust = max(0.05, min(agent.trust, 1.5))
+    return agents
+
+def weighted_consensus(agents, proposals):
+    scores = {}
+    for agent, proposal in zip(agents, proposals):
+        if agent.trust > 0.15:
+            scores[proposal] = scores.get(proposal, 0) + agent.trust
+    if not scores:
+        return "All proposals blocked by ethical constraints or low trust."
+    top_score = max(scores.values())
+    winners = [p for p, s in scores.items() if s == top_score]
+    return choice(winners)