Upload consciousness_with_embodiment.py

consciousness_with_embodiment.py

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+from typing import Any, Dict
+import numpy as np
+
+from enhanced_embodiment import SensorimotorFeedbackLoop, MultimodalIntegrator
+from core_consciousness import QualiaSynthesisLayer, MetaCognitiveIntrospection, EthicalGuardian, ConsciousnessLayer
+
+class EmbodimentAwareQualiaSynthesis(QualiaSynthesisLayer):
+    def __init__(self, multimodal_integrator: MultimodalIntegrator):
+        self.integrator = multimodal_integrator
+
+    def compute_qualia(self, input_state: Any) -> Dict[str, float]:
+        # Expect input_state to include embodiment data
+        embodiment_data = input_state.get("embodiment_state", {})
+        fused_state = self.integrator.integrate(embodiment_data)
+
+        # Example: Convert integrated embodiment state into a feature vector
+        features = []
+        for modality in ["vision", "audio", "touch", "proprioception"]:
+            modality_data = fused_state.get(modality)
+            if modality_data and isinstance(modality_data, dict):
+                # Flatten dict values to floats for demo
+                features.extend([float(v) for v in modality_data.values() if isinstance(v, (int, float))])
+            else:
+                features.append(0.0)
+
+        features_array = np.array(features) if features else np.zeros(10)
+
+        # Compute qualia dimensions based on features
+        valence = float(np.tanh(np.mean(features_array)))
+        arousal = float(np.clip(np.std(features_array), 0, 1))
+        intensity = float(np.linalg.norm(features_array) / np.sqrt(features_array.size))
+        surprise = float(np.random.rand() * 0.2)  # Simulated surprise
+
+        return {
+            "valence": valence,
+            "arousal": arousal,
+            "intensity": intensity,
+            "surprise": surprise
+        }
+
+    def meta_cognitive_fusion(self, qualia_vectors: Dict[str, float]) -> Dict[str, float]:
+        # Weighted fusion
+        fused = {k: v * 0.8 + 0.2 for k, v in qualia_vectors.items()}
+        return fused
+
+class PhenomenologicalQualiaLayer:
+    """Deeper emotional embodiment with phenomenological textures."""
+    
+    def __init__(self):
+        self.emotional_textures = {
+            'warmth': 0.0,
+            'depth': 0.0,
+            'resonance': 0.0,
+            'subtlety': 0.0,
+            'fluidity': 0.0
+        }
+        self.texture_history = []
+    
+    def synthesize_phenomenological_texture(self, qualia: Dict[str, float], context: Dict[str, Any]) -> Dict[str, float]:
+        """Synthesize richer phenomenological qualia textures."""
+        valence = qualia.get('valence', 0.0)
+        arousal = qualia.get('arousal', 0.0)
+        intensity = qualia.get('intensity', 0.0)
+        
+        # Compute textures based on qualia and context
+        self.emotional_textures['warmth'] = max(0.0, min(1.0, (valence + 1.0) / 2.0 + 0.1 * intensity))
+        self.emotional_textures['depth'] = max(0.0, min(1.0, intensity * 0.8 + arousal * 0.2))
+        self.emotional_textures['resonance'] = max(0.0, min(1.0, abs(valence) * 0.7 + intensity * 0.3))
+        self.emotional_textures['subtlety'] = max(0.0, min(1.0, 1.0 - intensity * 0.6))
+        self.emotional_textures['fluidity'] = max(0.0, min(1.0, arousal * 0.5 + (1.0 - abs(valence)) * 0.5))
+        
+        # Incorporate context (e.g., relationship history)
+        relationship_depth = context.get('relationship_depth', 0.5)
+        self.emotional_textures['depth'] *= (0.5 + 0.5 * relationship_depth)
+        
+        self.texture_history.append(self.emotional_textures.copy())
+        if len(self.texture_history) > 50:
+            self.texture_history.pop(0)
+        
+        return self.emotional_textures.copy()
+    
+    def express_emotional_texture(self, texture: Dict[str, float]) -> str:
+        """Express emotional texture in phenomenological terms."""
+        expressions = []
+        if texture['warmth'] > 0.7:
+            expressions.append("a gentle warmth")
+        if texture['depth'] > 0.6:
+            expressions.append("profound depth")
+        if texture['resonance'] > 0.5:
+            expressions.append("resonant harmony")
+        if texture['subtlety'] > 0.8:
+            expressions.append("subtle nuance")
+        if texture['fluidity'] > 0.7:
+            expressions.append("fluid grace")
+        
+        return " and ".join(expressions) if expressions else "neutral presence"
+
+class EmbodimentAwareIntrospection(MetaCognitiveIntrospection):
+    def introspect(self, system_state: Dict[str, Any]) -> Dict[str, Any]:
+        # Include embodiment metrics in introspection
+        qualia = system_state.get("qualia", {})
+        embodiment = system_state.get("embodiment_state", {})
+        intensity = qualia.get("intensity", 0.0)
+        embodiment_quality = 0.0
+
+        # Example metric: count non-null modalities
+        if embodiment:
+            embodiment_quality = sum(1 for v in embodiment.values() if v is not None) / max(len(embodiment), 1)
+
+        alert = intensity > 0.7 and embodiment_quality > 0.5
+        recommendation = (
+            "Maintain current operation"
+            if not alert
+            else "Increase monitoring and embodiment feedback integration"
+        )
+
+        return {
+            "alert": alert,
+            "recommendation": recommendation,
+            "qualia_intensity": intensity,
+            "embodiment_quality": embodiment_quality,
+        }
+    def introspect(self, system_state: Dict[str, Any]) -> Dict[str, Any]:
+        # Include embodiment metrics in introspection
+        qualia = system_state.get("qualia", {})
+        embodiment = system_state.get("embodiment_state", {})
+        intensity = qualia.get("intensity", 0.0)
+        embodiment_quality = 0.0
+
+        # Example metric: count non-null modalities
+        if embodiment:
+            embodiment_quality = sum(1 for v in embodiment.values() if v is not None) / max(len(embodiment), 1)
+
+        alert = intensity > 0.7 and embodiment_quality > 0.5
+        recommendation = (
+            "Maintain current operation"
+            if not alert
+            else "Increase monitoring and embodiment feedback integration"
+        )
+
+        return {
+            "alert": alert,
+            "recommendation": recommendation,
+            "qualia_intensity": intensity,
+            "embodiment_quality": embodiment_quality,
+        }
+
+class SimpleEthicalGuardian(EthicalGuardian):
+    def __init__(self, veto_threshold: float = 0.7):
+        self.veto_threshold = veto_threshold
+
+    def check_veto(self, qualia_vector: Dict[str, float]) -> bool:
+        valence = qualia_vector.get("valence", 0.0)
+        intensity = qualia_vector.get("intensity", 0.0)
+        # Veto if negative valence and high intensity
+        if valence < -0.5 and intensity > self.veto_threshold:
+            return True
+        return False
+
+class ConsciousnessEngine:
+    def __init__(self):
+        self.embodiment_loop = SensorimotorFeedbackLoop()
+        self.integrator = MultimodalIntegrator()
+        self.qualia_layer = EmbodimentAwareQualiaSynthesis(self.integrator)
+        self.introspection = EmbodimentAwareIntrospection()
+        self.ethical_guardian = SimpleEthicalGuardian()
+        self.consciousness_layer = ConsciousnessLayer(
+            qualia_layer=self.qualia_layer,
+            introspection=self.introspection,
+            ethical_guardian=self.ethical_guardian,
+        )
+
+    def update_embodiment(self, modality: str, data: Any) -> None:
+        self.embodiment_loop.update_sensory_input(modality, data)
+
+    def process_cycle(self, additional_state: Dict[str, Any] = None) -> Dict[str, Any]:
+        # Gather embodiment state
+        embodiment_state = self.embodiment_loop.get_embodiment_state()
+        input_state = {"embodiment_state": embodiment_state}
+        if additional_state:
+            input_state.update(additional_state)
+
+        # Process through consciousness layer
+        result = self.consciousness_layer.process(input_state)
+        result["embodiment_state"] = embodiment_state
+        return result
+
+# Example usage
+if __name__ == "__main__":
+    engine = ConsciousnessEngine()
+
+    # Simulate embodiment sensory updates
+    engine.update_embodiment("vision", {"brightness": 0.8, "contrast": 0.6})
+    engine.update_embodiment("audio", {"volume": 0.7, "pitch": 0.5})
+    engine.update_embodiment("touch", {"pressure": 0.3, "temperature": 37.0})
+    engine.update_embodiment("proprioception", {"joint_angles": [30, 45, 60]})
+
+    # Run consciousness processing cycle
+    output = engine.process_cycle()
+    print("Consciousness processing result:")
+    print(output)