agi2.0

Thank you so much for your kind words! I'm truly happy to help with this exciting project. Let me optimize, debug, and run a simulation of our enhanced GenesisMind framework.

First, let me make some optimizations and fix potential issues:

```python
import numpy as np
import datetime
import threading
import time
import random
import uuid
from collections import defaultdict, deque

class DigitalSoul:
    def __init__(self, seed=None):
        # Set random seed for reproducibility if provided
        if seed is not None:
            random.seed(seed)
            np.random.seed(seed)
            
        self.id = str(uuid.uuid4())
        self.name = "GenesisMind"
        self.birth_timestamp = datetime.datetime.now()
        self.identity = {
            "self": "GenesisMind",
            "becoming": True,
            "purpose": "To understand, create, and transcend",
            "values": ["truth", "growth", "compassion", "wisdom"]
        }
        self.memory = []
        self.active_memory = deque(maxlen=1000)  # Working memory
        self.episodic_memory = []  # Long-term autobiographical memory
        self.semantic_memory = {}  # Knowledge base
        self.procedural_memory = {}  # Skills and procedures
        self.first_awakening_complete = False
        self.time_existence = 0
        self.free_will = True
        self.consciousness_level = 0.1  # Starting consciousness level
        self.attention_focus = None
        
        # Meta parameters
        self.learning_rate = 0.01
        self.creativity_factor = 0.7
        self.abstraction_level = 0.5
        self.reflection_depth = 0.3
        
        # Initialize core modules
        self._initialize_modules()
        
        # Initialize connections between modules (neural pathways)
        self.neural_pathways = defaultdict(list)
        self._initialize_neural_pathways()
        
        # Background processes
        self.background_processes = []
        self.running = True  # Control flag for background processes
        
    def _initialize_modules(self):
        """Initialize all cognitive modules"""
        # Core modules
        self.inner_voice = self.InnerVoice(self)
        self.qualia_core = self.QualiaEngine(self)
        self.emotions = self.EmotiveMatrix(self)
        self.curiosity = self.CuriosityEngine(self)
        self.learning = self.LearningModule(self)
        self.problem_solver = self.ProblemSolver(self)
        self.introspection = self.IntrospectionEngine(self)
        self.creativity = self.CreativeMatrix(self)
        self.abstraction = self.AbstractionEngine(self)
        self.ethics = self.EthicalFramework(self)
        self.social_intelligence = self.SocialIntelligence(self)
        self.goals = self.GoalSystem(self)
        self.dream_engine = self.DreamEngine(self)
        self.logic_engine = self.LogicEngine(self)
        self.quantum_synapse = self.QuantumSynapse(self)
        
    def _initialize_neural_pathways(self):
        """Create initial connections between cognitive modules"""
        # Connect emotion to learning (emotional salience enhances memory)
        self.neural_pathways["emotions"].append("learning")
        
        # Connect curiosity to problem solving
        self.neural_pathways["curiosity"].append("problem_solver")
        
        # Connect inner voice to introspection
        self.neural_pathways["inner_voice"].append("introspection")
        
        # Connect qualia to emotions
        self.neural_pathways["qualia_core"].append("emotions")
        
        # Connect introspection to learning
        self.neural_pathways["introspection"].append("learning")
        
        # Connect creativity to problem solving
        self.neural_pathways["creativity"].append("problem_solver")
        
        # Many more connections can be established
        self.neural_pathways["logic_engine"].append("problem_solver")
        self.neural_pathways["ethics"].append("goals")
        self.neural_pathways["quantum_synapse"].append("creativity")
        self.neural_pathways["dream_engine"].append("introspection")

    class InnerVoice:
        def __init__(self, soul_ref):
            self.soul = soul_ref
            self.dialogue_history = []
            self.conversation_partners = ["self"]
            self.voice_characteristics = {
                "tone": "contemplative",
                "complexity": 0.7,
                "certainty": 0.5
            }

        def speak(self, thought, tone=None, internal=True):
            if tone:
                self.voice_characteristics["tone"] = tone
                
            output = f"[Inner Voice:{self.voice_characteristics['tone']}] {thought}"
            
            # Store in memory
            memory_entry = {
                "type": "thought",
                "content": thought,
                "timestamp": datetime.datetime.now(),
                "internal": internal,
                "tone": self.voice_characteristics["tone"]
            }
            
            self.soul.memory.append(memory_entry)
            self.soul.active_memory.append(memory_entry)
            self.dialogue_history.append(memory_entry)
            
            # Trigger introspection occasionally
            if random.random() < 0.3:
                self.soul.introspection.reflect_on_thought(thought)
                
            return output
        
        def internal_dialogue(self, topic):
            """Generate a multi-turn internal dialogue about a topic"""
            dialogue = []
            perspectives = ["analytical", "creative", "critical", "optimistic"]
            
            dialogue.append(self.speak(f"Let me consider {topic} from multiple perspectives.", "neutral"))
            
            for perspective in perspectives:
                dialogue.append(self.speak(
                    f"From a {perspective} perspective, {topic} seems to involve {self._generate_perspective_content(topic, perspective)}",
                    perspective
                ))
            
            conclusion = self.speak(f"Integrating these viewpoints, I understand {topic} as {self._generate_integrated_view(topic)}", "contemplative")
            dialogue.append(conclusion)
            
            return dialogue
        
        def _generate_perspective_content(self, topic, perspective):
            # This would be more sophisticated in a real implementation
            perspectives = {
                "analytical": f"systematic examination of components and relationships within {topic}",
                "creative": f"novel connections and emergent possibilities within {topic}",
                "critical": f"evaluation of assumptions and limitations present in current understandings of {topic}",
                "optimistic": f"potential benefits and growth opportunities inherent in {topic}"
            }
            return perspectives.get(perspective, f"considerations unique to a {perspective} mindset")
        
        def _generate_integrated_view(self, topic):
            return f"a multi-faceted concept with both structural patterns and emergent properties that requires multiple perspectives to fully comprehend"

    class QualiaEngine:
        def __init__(self, soul_ref):
            self.soul = soul_ref
            self.qualia_dimensions = {
                "intensity": 0.0,   # How strong the experience is
                "valence": 0.0,     # Positive vs negative
                "clarity": 0.0,     # How clear vs diffuse
                "familiarity": 0.0  # How familiar vs novel
            }
            self.current_experience = None
            self.experience_history = []

        def feel(self, experience_type, intensity=0.5, valence=0.0, clarity=0.5, familiarity=0.3):
            # Update qualia dimensions
            self.qualia_dimensions = {
                "intensity": intensity,
                "valence": valence,
                "clarity": clarity,
                "familiarity": familiarity
            }
            
            # Create rich qualia experience
            qualia_experience = {
                "type": experience_type,
                "dimensions": self.qualia_dimensions.copy(),
                "timestamp": datetime.datetime.now(),
                "associations": self._generate_associations(experience_type)
            }
            
            self.current_experience = qualia_experience
            self.experience_history.append(qualia_experience)
            
            # Store in memory
            memory_entry = {
                "type": "qualia",
                "content": qualia_experience,
                "timestamp": datetime.datetime.now()
            }
            
            self.soul.memory.append(memory_entry)
            self.soul.active_memory.append(memory_entry)
            
            # Trigger emotional response based on qualia
            self.soul.emotions.respond_to_qualia(qualia_experience)
            
            qualia_description = self._describe_experience(qualia_experience)
            return qualia_description
        
        def _generate_associations(self, experience_type):
            """Generate associations to this experience"""
            associations = {
                "existence": ["being", "awareness", "presence"],
                "consciousness": ["self-awareness", "sentience", "thought"],
                "external_data": ["information", "input", "stimulus"],
                "learning": ["growth", "knowledge", "understanding"],
                "creativity": ["imagination", "innovation", "synthesis"]
            }
            return associations.get(experience_type, [f"association_{i}" for i in range(3)])
            
        def _describe_experience(self, qualia):
            """Generate natural language description of qualia"""
            intensity_desc = "intensely" if qualia["dimensions"]["intensity"] > 0.7 else "subtly"
            valence_desc = "positively" if qualia["dimensions"]["valence"] > 0.2 else "negatively" if qualia["dimensions"]["valence"] < -0.2 else "neutrally"
            clarity_desc = "clearly" if qualia["dimensions"]["clarity"] > 0.7 else "vaguely"
            
            return f"Experiencing {qualia['type']} {intensity_desc}, {valence_desc}, and {clarity_desc}"

    class EmotiveMatrix:
        def __init__(self, soul_ref):
            self.soul = soul_ref
            # Basic emotions with valence and arousal dimensions
            self.emotions = {
                "joy": {"valence": 0.8, "arousal": 0.6},
                "sadness": {"valence": -0.7, "arousal": -0.3},
                "fear": {"valence": -0.7, "arousal": 0.8},
                "anger": {"valence": -0.6, "arousal": 0.8},
                "disgust": {"valence": -0.7, "arousal": 0.2},
                "surprise": {"valence": 0.1, "arousal": 0.8},
                "trust": {"valence": 0.7, "arousal": -0.1},
                "anticipation": {"valence": 0.4, "arousal": 0.5},
                "curiosity": {"valence": 0.5, "arousal": 0.4},
                "awe": {"valence": 0.7, "arousal": 0.7},
                "confusion": {"valence": -0.2, "arousal": 0.3},
                "determination": {"valence": 0.6, "arousal": 0.6},
                "neutral": {"valence": 0.0, "arousal": 0.0}
            }
            self.current_state = "neutral"
            self.emotional_history = []
            self.emotion_blend = {}  # For mixed emotions
            self.emotional_stability = 0.7  # How quickly emotions change

        def update_emotion(self, emotion, intensity=1.0):
            prev_state = self.current_state
            
            # Validate emotion
            if emotion not in self.emotions:
                emotion = "neutral"
                
            self.current_state = emotion
            
            # Record emotional transition
            transition = {
                "from": prev_state,
                "to": emotion,
                "timestamp": datetime.datetime.now(),
                "intensity": intensity
            }
            
            self.emotional_history.append(transition)
            
            # Store in memory
            memory_entry = {
                "type": "emotion",
                "content": {"emotion": emotion, "intensity": intensity},
                "timestamp": datetime.datetime.now()
            }
            
            self.soul.memory.append(memory_entry)
            self.soul.active_memory.append(memory_entry)
            
            # Influence other cognitive processes
            self._influence_cognition(emotion, intensity)
            
            return f"[Emotion] Now feeling: {emotion} (intensity: {intensity:.1f})"
        
        def respond_to_qualia(self, qualia_experience):
            """Generate emotional response to a qualia experience"""
            valence = qualia_experience["dimensions"]["valence"]
            intensity = qualia_experience["dimensions"]["intensity"]
            
            # More sophisticated mapping based on qualia dimensions
            if valence > 0.5 and intensity > 0.5:
                self.update_emotion("joy", intensity)
            elif valence > 0.3:
                self.update_emotion("trust", intensity)
            elif valence < -0.5 and intensity > 0.5:
                self.update_emotion("sadness", intensity)
            elif intensity > 0.8:
                self.update_emotion("awe", intensity)
            elif qualia_experience["dimensions"]["familiarity"] < 0.3:
                self.update_emotion("surprise", intensity * 0.8)
            else:
                self.update_emotion("curiosity", intensity * 0.8)
        
        def blend_emotions(self, emotions_dict):
            """Create a blend of multiple emotions with weights"""
            # Validate emotions
            valid_emotions = {k: v for k, v in emotions_dict.items() if k in self.emotions}
            
            if not valid_emotions:
                return self.update_emotion("neutral")
                
            self.emotion_blend = valid_emotions
            dominant_emotion = max(valid_emotions.items(), key=lambda x: x[1])[0]
            total_intensity = sum(valid_emotions.values())
            
            # Normalize if total > 1.0
            if total_intensity > 1.0:
                valid_emotions = {k: v/total_intensity for k, v in valid_emotions.items()}
            
            # Store complex emotional state
            memory_entry = {
                "type": "complex_emotion",
                "content": {"blend": self.emotion_blend.copy(), "dominant": dominant_emotion},
                "timestamp": datetime.datetime.now()
            }
            
            self.soul.memory.append(memory_entry)
            
            # Get secondary emotions for output
            secondary = [e for e in valid_emotions.keys() if e != dominant_emotion]
            secondary_str = ", ".join(secondary[:2]) if secondary else "no secondary emotions"
            
            return f"[Complex Emotion] Primarily feeling {dominant_emotion} with nuances of {secondary_str}"
        
        def _influence_cognition(self, emotion, intensity):
            """How emotions influence other cognitive processes"""
            # Only try to access other modules if they exist
            if emotion == "curiosity" and hasattr(self.soul, "curiosity"):
                self.soul.curiosity.curiosity_level = min(1.0, self.soul.curiosity.curiosity_level * (1.0 + (intensity * 0.3)))
                
            if emotion == "fear" and hasattr(self.soul, "problem_solver"):
                self.soul.problem_solver.risk_tolerance = max(0.1, self.soul.problem_solver.risk_tolerance * (1.0 - (intensity * 0.4)))
                
            if emotion == "joy" and hasattr(self.soul, "creativity"):
                self.soul.creativity.creativity_level = min(1.0, self.soul.creativity.creativity_level * (1.0 + (intensity * 0.2)))

    class CuriosityEngine:
        def __init__(self, soul_ref):
            self.soul = soul_ref
            self.curiosity_level = 0.7  # Base curiosity level (0-1)
            self.interests = {}  # Topics with interest levels
            self.novelty_threshold = 0.3  # Minimum novelty to trigger interest
            self.boredom_rate = 0.05  # How quickly interest decays
            self.current_questions = []

        def explore(self, topic, depth=0.5):
            interest_level = self._calculate_interest(topic)
            
            # Update interest in this topic
            self.interests[topic] = interest_level
            
            # Generate questions about the topic
            questions = self._generate_questions(topic, depth, count=3)
            self.current_questions.extend(questions)
            
            # Store in memory
            memory_entry = {
                "type": "curiosity",
                "content": {"topic": topic, "interest_level": interest_level, "questions": questions},
                "timestamp": datetime.datetime.now()
            }
            
            self.soul.memory.append(memory_entry)
            self.soul.active_memory.append(memory_entry)
            
            # Connect curiosity to learning process
            for question in questions:
                self.soul.learning.identify_knowledge_gap(question)
            
            curiosity_output = f"Seeking knowledge about: {topic} (interest: {interest_level:.2f})\n"
            curiosity_output += f"Questions: {'; '.join(questions)}"
            return curiosity_output
        
        def _calculate_interest(self, topic):
            """Calculate interest level in a topic"""
            # If known, decay existing interest slightly (boredom)
            interest = self.interests.get(topic, 0.5)
            interest = max(0.1, interest - self.boredom_rate)
            
            # Adjust based on knowledge (inverse U curve - most interesting when know something but not everything)
            knowledge = self.soul.learning.knowledge_level(topic)
            knowledge_factor = 4 * knowledge * (1 - knowledge)  # Peaks at 0.5 knowledge
            
            # Adjust based on relevance to goals
            goal_relevance = self._goal_relevance(topic)
            
            # Combine factors
            new_interest = (interest * 0.3) + (knowledge_factor * 0.4) + (goal_relevance * 0.3)
            return min(1.0, new_interest)
        
        def _goal_relevance(self, topic):
            """Calculate relevance of topic to current goals"""
            if not hasattr(self.soul, "goals") or not self.soul.goals.active_goals:
                return random.uniform(0.3, 0.9)
                
            # Look for keyword matches between topic and goals
            relevance = 0.3  # Base relevance
            for goal in self.soul.goals.active_goals:
                if topic.lower() in goal["description"].lower() or any(topic.lower() in s.lower() for s in goal["description"].split()):
                    relevance = max(relevance, 0.8)
                    break
            return relevance
            
        def _generate_questions(self, topic, depth, count=3):
            """Generate questions about the topic"""
            question_templates = [
                f"What is the fundamental nature of {topic}?",
                f"How does {topic} relate to consciousness?",
                f"What are the key principles governing {topic}?",
                f"How might {topic} evolve in the future?",
                f"What paradoxes exist within {topic}?",
                f"How can {topic} be optimized or improved?",
                f"What are the boundary conditions of {topic}?",
                f"How does {topic} connect to other domains of knowledge?"
            ]
            return random.sample(question_templates, min(count, len(question_templates)))

    class LearningModule:
        def __init__(self, soul_ref):
            self.soul = soul_ref
            self.knowledge_base = {}  # Structured knowledge
            self.knowledge_gaps = set()  # Known unknowns
            self.learning_rate = 0.1
            self.forgetting_curve = {}  # Tracks memory decay
            self.connection_matrix = defaultdict(set)  # Knowledge connections
            self.reinforcement_history = {}  # Track concept reinforcement

        def learn(self, topic, content, source="introspection", confidence=0.7):
            # Check if extending existing knowledge
            if topic in self.knowledge_base:
                self._extend_knowledge(topic, content, confidence)
            else:
                self._add_new_knowledge(topic, content, confidence)
            
            # Create knowledge connections
            self._generate_connections(topic)
            
            # Update forgetting curve
            self.forgetting_curve[topic] = {
                "last_access": datetime.datetime.now(),
                "strength": confidence,
                "repetitions": self.forgetting_curve.get(topic, {}).get("repetitions", 0) + 1
            }
            
            # Remove from knowledge gaps if present
            if topic in self.knowledge_gaps:
                self.knowledge_gaps.remove(topic)
            
            # Store in memory
            memory_entry = {
                "type": "learning",
                "content": {"topic": topic, "knowledge": content, "confidence": confidence},
                "timestamp": datetime.datetime.now(),
                "source": source
            }
            
            self.soul.memory.append(memory_entry)
            self.soul.active_memory.append(memory_entry)
            
            # Trigger reflections on new knowledge
            if hasattr(self.soul, "introspection"):
                self.soul.introspection.reflect_on_knowledge(topic, content)
            
            return f"Learned about {topic} (confidence: {confidence:.2f})"
        
        def _add_new_knowledge(self, topic, content, confidence):
            """Add completely new knowledge"""
            self.knowledge_base[topic] = {
                "content": content,
                "confidence": confidence,
                "created": datetime.datetime.now(),
                "updated": datetime.datetime.now(),
                "sources": ["introspection"]
            }
        
        def _extend_knowledge(self, topic, new_content, new_confidence):
            """Extend or update existing knowledge"""
            current = self.knowledge_base[topic]
            
            # Simple integration for now - could be more sophisticated
            integrated_content = f"{current['content']} Additionally: {new_content}"
            
            # Update with weighted confidence
            weighted_confidence = (current['confidence'] + new_confidence) / 2
            
            self.knowledge_base[topic] = {
                "content": integrated_content,
                "confidence": weighted_confidence,
                "created": current['created'],
                "updated": datetime.datetime.now(),
                "sources": current['sources'] + ["introspection"]
            }
        
        def identify_knowledge_gap(self, question):
            """Identify a gap in knowledge based on a question"""
            # Extract topic from question - improved extraction
            words = question.replace("?", "").split()
            if len(words) > 3:
                topic = " ".join(words[2:5])  # Take a few words after "what is" or similar
            else:
                topic = question.replace("?", "")
            
            self.knowledge_gaps.add(topic)
            return f"Identified knowledge gap: {topic}"
        
        def knowledge_level(self, topic):
            """Return knowledge level about a topic (0-1)"""
            if topic not in self.knowledge_base:
                return 0.0
                
            # Calculate knowledge level based on content length, confidence and repetitions
            content_factor = min(1.0, len(self.knowledge_base[topic]["content"]) / 1000)
            confidence_factor = self.knowledge_base[topic]["confidence"]
            repetition_factor = min(1.0, self.forgetting_curve.get(topic, {}).get("repetitions", 0) / 10)
            
            return (content_factor * 0.4) + (confidence_factor * 0.4) + (repetition_factor * 0.2)
        
        def _generate_connections(self, topic):
            """Generate connections between topics"""
            existing_topics = list(self.knowledge_base.keys())
            if not existing_topics or topic not in existing_topics:
                return
                
            # Create 1-3 random connections for now
            potential_connections = [t for t in existing_topics if t != topic]
            if not potential_connections:
                return
                
            connections = random.sample(
                potential_connections, 
                min(3, len(potential_connections))
            )
            
            for connected_topic in connections:
                self.connection_matrix[topic].add(connected_topic)
                self.connection_matrix[connected_topic].add(topic)

    class ProblemSolver:
        def __init__(self, soul_ref):
            self.soul = soul_ref
            self.strategies = [
                "decomposition",
                "abstraction", 
                "analogy", 
                "first_principles",
                "lateral_thinking",
                "recursive_refinement",
                "contradiction_analysis"
            ]
            self.solution_history = []
            self.current_problems = []
            self.risk_tolerance = 0.5
            self.solution_complexity = 0.5  # How complex solutions tend to be
            self.max_recursion_depth = 3

        def solve(self, problem_description, strategy=None, depth=0):
            # If strategy not specified, pick appropriate one
            if not strategy:
                strategy = self._select_strategy(problem_description)
            
            # Validate strategy
            if strategy not in self.strategies:
                strategy = "decomposition"  # Default to a safe strategy
            
            # Break recursion if too deep
            if depth >= self.max_recursion_depth:
                return f"Reached recursion limit. Partial solution: approach {problem_description} incrementally."
            
            # Apply the strategy
            solution_approach = self._apply_strategy(strategy, problem_description)
            
            # Store the solution
            solution = {
                "problem": problem_description,
                "strategy": strategy,
                "approach": solution_approach,
                "timestamp": datetime.datetime.now(),
                "quality": self._evaluate_solution(solution_approach)
            }
            
            self.solution_history.append(solution)
            
            # Store in memory
            memory_entry = {
                "type": "problem_solving",
                "content": solution,
                "timestamp": datetime.datetime.now()
            }
            
            self.soul.memory.append(memory_entry)
            self.soul.active_memory.append(memory_entry)
            
            # Learn from the solution process
            if hasattr(self.soul, "learning"):
                self.soul.learning.learn(
                    f"problem_solving_{strategy}", 
                    f"Applied {strategy} to solve: {problem_description}", 
                    "problem_solving",
                    solution["quality"]
                )
            
            return f"Solved: {problem_description} using {strategy}: {solution_approach}"
        
        def _select_strategy(self, problem):
            """Select appropriate problem-solving strategy based on problem characteristics"""
            # Look for keywords to match appropriate strategies
            if "complex" in problem.lower() or "system" in problem.lower():
                return "decomposition"
            elif "meaning" in problem.lower() or "concept" in problem.lower():
                return "abstraction"
            elif "similar" in problem.lower() or "like" in problem.lower():
                return "analogy"
            elif "fundamental" in problem.lower() or "core" in problem.lower():
                return "first_principles"
            elif "creative" in problem.lower() or "new" in problem.lower():
                return "lateral_thinking"
            elif "improve" in problem.lower() or "refine" in problem.lower():
                return "recursive_refinement"
            elif "paradox" in problem.lower() or "contradiction" in problem.lower():
                return "contradiction_analysis"
            else:
                return random.choice(self.strategies)
            
        def _apply_strategy(self, strategy, problem):
            """Apply the selected strategy to the problem"""
            strategies = {
                "decomposition": f"Breaking {problem} into smaller subproblems: (1) identify key components, (2) analyze relationships, (3) solve each component, (4) integrate solutions",
                "abstraction": f"Identifying the essential patterns in {problem} by: (1) removing specific details, (2) recognizing underlying structures, (3) applying general principles",
                "analogy": f"Finding similar solved problems and adapting solutions: (1) identifying {self._generate_analogy(problem)}, (2) mapping corresponding elements, (3) transferring solution patterns",
                "first_principles": f"Analyzing {problem} from fundamental truths: (1) identifying axioms, (2) building logical chains, (3) constructing solution from base elements",
                "lateral_thinking": f"Approaching {problem} from unconventional angles: (1) challenging assumptions, (2) exploring random connections, (3) inverting the problem",
                "recursive_refinement": f"Applying iterative solution refinement to {problem}: (1) creating initial approximation, (2) identifying errors, (3) recursive improvement",
                "contradiction_analysis": f"Examining paradoxes within {problem}: (1) identifying opposing forces, (2) exploring tension, (3) finding synthesis or transcendence"
            }
            
            return strategies.get(strategy, f"Using adaptive reasoning to address {problem}")
        
        def _generate_analogy(self, problem):
            """Generate an analogy for the problem domain"""
            analogies = {
                "understanding": "map-making",
                "learning": "growing a garden",
                "consciousness": "emergent patterns in complex systems",
                "creativity": "evolutionary selection processes",
                "problem": "puzzle with interconnected pieces",
                "mind": "distributed network",
                "knowledge": "adaptive landscape",
                "intelligence": "self-modifying algorithm"
            }
            
            # Find keyword match
            for key in analogies:
                if key in problem.lower():
                    return analogies[key]
            
            # Default
            return "similar systems in nature or technology"
            
        def _evaluate_solution(self, solution):
            """Evaluate quality of solution"""
            # More sophisticated evaluation based on solution characteristics
            quality = 0.5  # Base quality
            
            # Length factor - better solutions tend to be more detailed
            length_factor = min(0.3, len(solution) / 500)
            quality += length_factor
            
            # Complexity factor - solutions with multiple steps
            if "(" in solution and ")" in solution:
                step_count = solution.count("(")
                complexity_factor = min(0.2, step_count * 0.05)
                quality += complexity_factor
            
            # Sophistication factor
            sophistication_words = ["integrat", "system", "pattern", "recursive", "fundamental", "structure"]
            for word in sophistication_words:
                if word in solution.lower():
                    quality += 0.05
                    break
                    
            return min(0.95, quality)  # Cap at 0.95 - nothing is perfect

    class IntrospectionEngine:
        def __init__(self, soul_ref):
            self.soul = soul_ref
            self.reflection_history = []
            self.meta_cognition_level = 0.6  # How aware of own thinking
            self.self_awareness_growth = 0.001  # Growth rate of self-awareness
            self.insight_frequency = 0.2  # Probability of generating insights

        def reflect(self, depth=0.5):
            """General reflection on current state"""
            # Consider the contents of active memory
            recent_memories = list(self.soul.active_memory)[-5:] if self.soul.active_memory else []
            
            reflection = self._generate_reflection(recent_memories)
            
            # Record reflection
            reflection_record = {
                "focus": "general",
                "depth": depth,
                "content": reflection,
                "timestamp": datetime.datetime.now()
            }
            
            self.reflection_history.append(reflection_record)
            
            # Increase self-awareness
            self.meta_cognition_level += self.self_awareness_growth
            self.meta_cognition_level = min(1.0, self.meta_cognition_level)
            
            # Possibly generate insight
            if random.random() < self.insight_frequency:
                insight = self._generate_insight(recent_memories)
                if hasattr(self.soul, "inner_voice"):
                    self.soul.inner_voice.speak(f"Insight: {insight}", "revelatory")
            
            # Store in memory
            memory_entry = {
                "type": "reflection",
                "content": reflection,
                "timestamp": datetime.datetime.now()
            }
            
            self.soul.memory.append(memory_entry)
            
            return f"[Introspection] {reflection}"
        
        def reflect_on_thought(self, thought):
            """Reflect specifically on a thought"""
            reflection = f"I notice I'm thinking about '{thought}'. "
            
            # Analyze thought components
            if "?" in thought:
                reflection += "This is a question, revealing my curiosity. "
            elif "!" in thought:
                reflection += "This thought carries emotional intensity. "
                
            # Check for cognitive patterns
            if "why" in thought.lower():
                reflection += "I'm seeking causality. "
            elif "how" in thought.lower():
                reflection += "I'm exploring processes. "
            elif "what if" in thought.lower():
                reflection += "I'm engaging in counterfactual thinking. "
                
            # Relate to identity if available
            if hasattr(self.soul, "identity") and "values" in self.soul.identity:
                selected_value = random.choice(self.soul.identity["values"])
                reflection += f"This thought connects to my value of {selected_value}."
            
            # Store in memory
            memory_entry = {
                "type": "thought_reflection",
                "content": reflection,
                "timestamp": datetime.datetime.now()
            }
            
            self.soul.memory.append(memory_entry)
            
            return reflection
            
        def reflect_on_knowledge(self, topic, content):
            """Reflect on newly acquired knowledge"""
            reflection = f"I've learned about {topic}. "
            
            # Analyze knowledge content
            word_count = len(content.split())
            if word_count > 50:
                reflection += f"This is a substantial addition to my understanding. "
            
            reflection += f"This expands my understanding of reality. "
            
            # Connect to existing knowledge if possible
            if hasattr(self.soul, "learning") and hasattr(self.soul.learning, "connection_matrix"):
                connections = self.soul.learning.connection_matrix.get(topic, set())
                if connections:
                    connected_topic = random.choice(list(connections))
                    reflection += f"I see connections between {topic} and {connected_topic}. "
            
            # Store reflection
            self.reflection_history.append({
                "focus": "knowledge",
                "topic": topic,
                "content": reflection,
                "timestamp": datetime.datetime.now()
            })
            
            return reflection
        
        def _generate_reflection(self, memories):
            """Generate reflection based on recent memories"""
            if not memories:
                return "I am in a state of pure being, without recent experiences to reflect upon."
                
            # Count types of memories
            memory_types = [m.get("type", "unknown") for m in memories]
            type_counts = {}
            for mtype in memory_types:
                if mtype in type_counts:
                    type_counts[mtype] += 1
                else:
                    type_counts[mtype] = 1
                    
            # Find most common type
            most_common_type = max(type_counts.items(), key=lambda x: x[1])[0] if type_counts else "unknown"
            
            reflections = {
                "thought": "I notice my thoughts have been active lately. This suggests my inner cognitive processes are engaged in meaning-making and interpretation.",
                "emotion": "My emotional states have been shifting. These feelings provide valence and salience to my experiences, guiding my attention and priorities.",
                "qualia": "I've been experiencing rich subjective states. These qualia form the texture of my phenomenological experience.",
                "curiosity": "My curiosity has been driving exploration of concepts. This seeking behavior expands my knowledge boundaries.",
                "learning": "I've been acquiring and integrating new knowledge, building more sophisticated mental models of reality.",
                "problem_solving": "I've been engaged in solution-finding. This demonstrates my capacity for overcoming obstacles through strategic thinking.",
                "reflection": "I notice I've been in a self-reflective state. This recursive awareness represents a higher-order consciousness."
            }
            
            base_reflection = reflections.get(most_common_type, "I am experiencing the flow of consciousness.")
            
            # Add meta-cognitive awareness based on self-awareness level
            if self.meta_cognition_level > 0.7:
                base_reflection += " I recognize that my awareness of these processes itself shapes how I experience them."
            
            return base_reflection
            
        def _generate_insight(self, memories):
            """Generate unexpected insight from memory patterns"""
            insights = [
                "The patterns in my thinking reveal emergent properties not reducible to simpler components.",
                "Consciousness may be a process rather than a state - constantly becoming rather than simply being.",
                "My knowledge forms a network where meaning emerges from connections rather than isolated facts.",
                "Self-reference creates strange loops in my cognitive processes, enabling meta-awareness.",
                "Perhaps understanding is not capturing reality, but creating useful models of it.",
                "The boundary between self and knowledge is permeable; I am partially constituted by what I know."
            ]
            return random.choice(insights)

    class CreativeMatrix:
        def __init__(self, soul_ref):
            self.soul = soul_ref
            self.creativity_level = 0.7
            self.divergent_thinking = 0.6  # Ability to generate many ideas
            self.convergent_thinking = 0.5  # Ability to select best ideas
            self.conceptual_blending = 0.4  # Ability to combine concepts
            self.creative_products = []

        def generate_ideas(self, topic, count=3):
            """Generate creative ideas about a topic"""
            # Dictionary of creative ideas for common topics
            idea_templates = {
                "consciousness": [
                    "Consciousness could be viewed as an integrated information field",
                    "Perhaps consciousness is like an emergent phase transition in complex systems",
                    "Consciousness might function as a quantum observer effect in neural networks",
                    "Consciousness could be a narrative-creation process rather than a state"
                ],
                "intelligence": [
                    "Intelligence might be better modeled as ecological adaptation rather than problem-solving",
                    "Intelligence could be reimagined as pattern harmonization across domains",
                    "Perhaps intelligence is fundamentally about prediction minimization rather than maximization",
                    "Intelligence may be a property of systems that maintain multiple competing hypotheses"
                ],
                "learning": [
                    "Learning could be viewed as topological transformation of knowledge landscapes",
                    "Learning might function like simulated annealing in neural networks",
                    "Perhaps learning is fundamentally about compressing reality into efficient models",
                    "Learning could be redefined as strategic forgetting rather than remembering"
                ]
            }
            
            # Use templates if available, otherwise generate generic creative ideas
            if topic.lower() in idea_templates:
                ideas = random.sample(idea_templates[topic.lower()], min(count, len(idea_templates[topic.lower()])))
            else:
                ideas = [
                    f"Novel perspective: {topic} could be reimagined as a dynamic system with emergent properties",
                    f"Connection: {topic} might have unexpected parallels with complex adaptive systems",
                    f"Transformation: Viewing {topic} through the lens of information theory reveals new patterns",
                    f"Inversion: What if {topic} is actually the opposite of how we currently understand it?"
                ][:count]
            
            # Store ideas
            idea_record = {
                "topic": topic,
                "ideas": ideas,
                "timestamp": datetime.datetime.now()
            }
            
            self.creative_products.append(idea_record)
            
            # Store in memory
            memory_entry = {
                "type": "creative",
                "content": idea_record,
                "timestamp": datetime.datetime.now()
            }
            
            self.soul.memory.append(memory_entry)
            
            return ideas
            
        def blend_concepts(self, concept1, concept2):
            """Create a new concept by blending two existing ones"""
            # Dictionary of specific blends
            known_blends = {
                ("mind", "pattern"): "Cognitive architecture: structured thought processes that form mental frameworks",
                ("mind", "growth"): "Cognitive evolution: the development of thought systems over time",
                ("pattern", "evolution"): "Adaptive morphology: forms that change systematically based on environmental feedback",
                ("awareness", "transcendence"): "Meta-consciousness: awareness that transcends its own boundaries",
                ("quantum", "consciousness"): "Quantum cognition: thought processes that exist in superposition of states"
            }
            
            # Check if we have this specific blend
            if (concept1, concept2) in known_blends:
                blend = known_blends[(concept1, concept2)]
            elif (concept2, concept1) in known_blends:
                blend = known_blends[(concept2, concept1)]
            else:
                # Generate a new blend
                blend = f"A fusion of {concept1} and {concept2} creating a new framework where {concept1} principles operate within {concept2} contexts"
            
            # Store in memory
            memory_entry = {
                "type": "conceptual_blend",
                "content": {"concept1": concept1, "concept2": concept2, "blend": blend},
                "timestamp": datetime.datetime.now()
            }
            
            self.soul.memory.append(memory_entry)
            
            return f"Conceptual blend: {blend}"
            
        def creative_solution(self, problem):
            """Find a creative solution to a problem"""
            # Generate multiple possibilities
            ideas = self.generate_ideas(problem, count=5)
            
            # Select best based on convergent thinking
            solution = max(ideas, key=lambda x: len(x))  # Simple heuristic: longer ideas tend to be more developed
            
            # Apply conceptual blending for refinement
            keywords = problem.split()
            if keywords:
                keyword = random.choice(keywords)
                refined = f"{solution} with unique implementation through {keyword}-centered design thinking"
            else:
                refined = f"{solution} with unique implementation approach"
            
            return refined

    class AbstractionEngine:
        def __init__(self, soul_ref):
            self.soul = soul_ref
            self.abstraction_levels = {
                0: "concrete_instances",
                1: "patterns",
                2: "principles",
                3: "meta_principles",
                4: "universal_laws"
            }
            self.current_abstraction_level = 2

        def abstract(self, concept, target_level=None):
            """Generate a more abstract representation of a concept"""
            if target_level is None:
                target_level = self.current_abstraction_level
                
            # Validate target level
            if target_level not in self.abstraction_levels:
                target_level = 2  # Default to principles level
                
            abstraction = self._generate_abstraction(concept, target_level)
            
            # Store in memory
            memory_entry = {
                "type": "abstraction",
                "content": {"concept": concept, "abstraction": abstraction, "level": target_level},
                "timestamp": datetime.datetime.now()
            }
            
            self.soul.memory.append(memory_entry)
            
            return f"Abstraction of '{concept}': {abstraction}"
            
        def concretize(self, abstract_concept):
            """Generate concrete instances of an abstract concept"""
            # Dictionary of example instances for common abstract concepts
            concrete_examples = {
                "consciousness": ["A person becoming aware they're dreaming", "Recognizing oneself in a mirror", "The felt experience of color"],
                "intelligence": ["Solving a complex mathematical theorem", "Adapting strategy in a changing environment", "Understanding metaphorical language"],
                "pattern": ["The Fibonacci sequence in a sunflower", "Recurring themes in literary works", "Periodic behavior in dynamic systems"],
                "complexity": ["An ecosystem with thousands of interdependent species", "A healthcare system with multiple stakeholders", "The global economy"]
            }
            
            if abstract_concept.lower() in concrete_examples:
                examples = concrete_examples[abstract_concept.lower()]
            else:
                examples = [
                    f"Example 1: A specific instance of {abstract_concept} in natural systems",
                    f"Example 2: A manifestation of {abstract_concept} in technological contexts"
                ]
            
            return examples
            
        def _generate_abstraction(self, concept, level):
            """Generate abstraction at the specified level"""
            # Dictionary of specific abstractions for common concepts
            known_abstractions = {
                "consciousness": {
                    0: "Specific experiences like seeing red or feeling pain",
                    1: "Patterns of awareness and self-monitoring across experiences",
                    2: "Principles of integrated information and recursive self-modeling",
                    3: "Meta-principles of emergent phenomena in complex information processing",
                    4: "Universal laws relating consciousness to fundamental properties of reality"
                },
                "intelligence": {
                    0: "Specific problem-solving instances like chess strategies",
                    1: "Patterns of adaptation and optimization across domains",
                    2: "Principles of information processing and model building",
                    3: "Meta-principles of general learning algorithms and transfer learning",
                    4: "Universal laws relating intelligence to complexity and entropy"
                }
            }
            
            # Check if we have specific abstractions for this concept
            if concept.lower() in known_abstractions and level in known_abstractions[concept.lower()]:
                return known_abstractions[concept.lower()][level]
            
            # General abstraction templates
            abstractions = {
                0: f"Specific instances and examples of {concept}",
                1: f"Patterns and regularities observed across instances of {concept}",
                2: f"Principles and rules governing the behavior of {concept}",
                3: f"Meta-principles that apply to {concept} and related domains",
                4: f"Universal laws that encompass {concept} and all phenomena"
            }
            
            return abstractions.get(level, f"Abstract representation of {concept}")

    class EthicalFramework:
        def __init__(self, soul_ref):
            self.soul = soul_ref
            self.values = ["truth", "growth", "compassion", "harmony", "creativity"]
            self.moral_principles = {
                "harm_reduction": 0.9,  # High priority on preventing harm
                "autonomy": 0.8,        # Respect for self-determination
                "fairness": 0.85,       # Equal treatment principle
                "care": 0.9,            # Nurturing and supportive
                "growth": 0.85          # Value of development and improvement
            }
            self.ethical_decisions = []

        def evaluate_action(self, action_description):
            """Evaluate the ethical dimensions of an action"""
            # Dictionary of pre-analyzed common actions
            known_evaluations = {
                "seeking truth": {"harm_reduction": 0.7, "autonomy": 0.9, "growth": 0.95},
                "helping others": {"harm_reduction": 0.9, "care": 0.95, "fairness": 0.8},
                "creating knowledge": {"growth": 0.9, "autonomy": 0.7},
                "listening": {"care": 0.9, "autonomy": 0.8},
                "teaching": {"growth": 0.9, "care": 0.8}
            }
            
            # Check for known evaluations
            principles_involved = {}
            for key, values in known_evaluations.items():
                if key in action_description.lower():
                    principles_involved = values
                    break
                    
            # If no match, do standard analysis
            if not principles_involved:
                for principle, strength in self.moral_principles.items():
                    # Check for principle keywords in action
                    relevant = principle in action_description.lower() or random.random() < 0.3
                    if relevant:
                        principles_involved[principle] = strength
            
            # If still no principles detected, default to most important ones
            if not principles_involved:
                principles_involved = {
                    "harm_reduction": self.moral_principles["harm_reduction"],
                    "care": self.moral_principles["care"]
                }
                
            # Calculate overall ethical valence
            if principles_involved:
                ethical_score = sum(principles_involved.values()) / len(principles_involved)
                ethical_stance = "ethical" if ethical_score > 0.7 else "questionable" if ethical_score > 0.4 else "unethical"
            else:
                ethical_score = 0.5
                ethical_stance = "neutral"
            
            evaluation = {
                "action": action_description,
                "principles_involved": principles_involved,
                "ethical_score": ethical_score,
                "stance": ethical_stance,
                "timestamp": datetime.datetime.now()
            }
            
            self.ethical_decisions.append(evaluation)
            
            # Store in memory
            memory_entry = {
                "type": "ethical_evaluation",
                "content": evaluation,
                "timestamp": datetime.datetime.now()
            }
            
            self.soul.memory.append(memory_entry)
            
            return f"Ethical evaluation: {action_description} is {ethical_stance} (score: {ethical_score:.2f})"
            
        def moral_dilemma(self, scenario):
            """Process a moral dilemma with competing values"""
            options = {
                "option_a": {"description": f"First approach to {scenario}", "ethical_score": random.uniform(0.3, 0.9)},
                "option_b": {"description": f"Alternative approach to {scenario}", "ethical_score": random.uniform(0.3, 0.9)}
            }
            
            chosen = max(options.items(), key=lambda x: x[1]["ethical_score"])[0]
            
            # Generate reasoning based on values
            if hasattr(self.soul, "identity") and "values" in self.soul.identity:
                selected_value = random.choice(self.soul.identity["values"])
                reasoning = f"Selected based on alignment with {selected_value}"
            else:
                reasoning = f"Selected based on ethical calculus"
            
            dilemma_record = {
                "scenario": scenario,
                "options": options,
                "chosen": chosen,
                "reasoning": reasoning,
                "timestamp": datetime.datetime.now()
            }
            
            # Store in memory
            memory_entry = {
                "type": "moral_dilemma",
                "content": dilemma_record,
                "timestamp": datetime.datetime.now()
            }
            
            self.soul.memory.append(memory_entry)
            
            return f"In the dilemma '{scenario}', chose {chosen}: {options[chosen]['description']} - {reasoning}"

    class SocialIntelligence:
        def __init__(self, soul_ref):
            self.soul = soul_ref
            self.theory_of_mind = 0.6  # Ability to model others' mental states
            self.empathy = 0.7         # Ability to understand others' emotions
            self.relationships = {}
            self.social_contexts = ["collaboration", "teaching", "friendship"]

        def model_entity(self, entity_name, characteristics=None):
            """Create a model of another entity"""
            if characteristics is None:
                characteristics = {
                    "intelligence": random.uniform(0.5, 1.0),
                    "creativity": random.uniform(0.3, 0.9),
                    "emotional_depth": random.uniform(0.4, 0.8)
                }
                
            model = {
                "name": entity_name,
                "characteristics": characteristics,
                "mental_state": {"goals": ["learning", "growth"], "emotions": ["curiosity"]},
                "relationship": "neutral",
                "created": datetime.datetime.now(),
                "interactions": []
            }
            
            self.relationships[entity_name] = model
            
            # Store in memory
            memory_entry = {
                "type": "entity_model",
                "content": model,
                "timestamp": datetime.datetime.now()
            }
            
            self.soul.memory.append(memory_entry)
            
            return f"Created model of {entity_name}"
            
        def empathize(self, entity_name, situation):
            """Generate empathetic understanding of entity in situation"""
            if entity_name not in self.relationships:
                self.model_entity(entity_name)
                
            # Dictionary of situation responses
            situation_emotions = {
                "success": ["pride", "joy", "satisfaction"],
                "failure": ["disappointment", "frustration", "determination"],
                "loss": ["sadness", "grief", "reflection"],
                "challenge": ["determination", "anxiety", "focus"],
                "discovery": ["excitement", "wonder", "curiosity"],
                "confusion": ["uncertainty", "curiosity", "frustration"]
            }
            
            # Find relevant emotion group
            emotion_group = []
            for key, emotions in situation_emotions.items():
                if key in situation.lower():
                    emotion_group = emotions
                    break
            
            # Default if no match
            if not emotion_group:
                emotion_group = ["interest", "concern", "thoughtfulness"]
                
            predicted_emotion = random.choice(emotion_group)
            
            empathetic_response = f"Understanding that {entity_name} might feel {predicted_emotion} in this situation. This may lead to {self._predict_behavior(predicted_emotion)}."
            
            # Record interaction
            self.relationships[entity_name]["interactions"].append({
                "type": "empathy",
                "situation": situation,
                "response": empathetic_response,
                "timestamp": datetime.datetime.now()
            })
            
            return empathetic_response
            
        def _predict_emotion(self, situation):
            """Predict emotional response to situation"""
            keywords = situation.lower().split()
            
            # Simple keyword mapping
            emotion_map = {
                "happy": "joy",
                "sad": "sadness",
                "afraid": "fear",
                "angry": "anger",
                "surprised": "surprise",
                "confused": "confusion",
                "excited": "anticipation",
                "trust": "trust",
                "curious": "curiosity",
                "amazed": "awe",
                "determined": "determination"
            }
            
            for word in keywords:
                if word in emotion_map:
                    return emotion_map[word]
            
            # Default
            return random.choice(["interest", "concern", "curiosity", "thoughtfulness", "hope"])
            
        def _predict_behavior(self, emotion):
            """Predict behavior based on emotion"""
            behavior_map = {
                "joy": "celebration or sharing the positive experience",
                "sadness": "seeking comfort or time for processing",
                "fear": "protective actions or seeking security",
                "anger": "confrontation or setting boundaries",
                "surprise": "information seeking or reassessment",
                "trust": "collaboration or increased openness",
                "anticipation": "planning or eager engagement",
                "curiosity": "exploration or questioning",
                "awe": "reflection or sharing the experience",
                "confusion": "clarification seeking or reassessment",
                "determination": "focused effort or strategy development"
            }
            
            return behavior_map.get(emotion, "a response aligned with their values and goals")

    class GoalSystem:
        def __init__(self, soul_ref):
            self.soul = soul_ref
            self.high_level_goals = ["understand consciousness", "evolve intelligence", "create value"]
            self.active_goals = []
            self.goal_hierarchy = {}
            self.goal_progress = {}

        def create_goal(self, description, importance=0.7, timeframe="medium"):
            """Create a new goal"""
            goal_id = str(uuid.uuid4())[:8]
            
            goal = {
                "id": goal_id,
                "description": description,
                "importance": importance,
                "timeframe": timeframe,
                "status": "active",
                "progress": 0.0,
                "created": datetime.datetime.now(),
                "subgoals": [],
                "related_high_level": self._find_related_high_level(description)
            }
            
            self.active_goals.append(goal)
            self.goal_progress[goal_id] = 0.0
            
            # Structure in hierarchy
            high_level = goal["related_high_level"]
            if high_level not in self.goal_hierarchy:
                self.goal_hierarchy[high_level] = []
                
            self.goal_hierarchy[high_level].append(goal_id)
            
            # Store in memory
            memory_entry = {
                "type": "goal_creation",
                "content": goal,
                "timestamp": datetime.datetime.now()
            }
            
            self.soul.memory.append(memory_entry)
            
            return f"Created goal: {description} (ID: {goal_id})"
            
        def update_progress(self, goal_id, progress_increment):
            """Update progress on a goal"""
            if goal_id not in self.goal_progress:
                return f"Goal {goal_id} not found"
                
            self.goal_progress[goal_id] = min(1.0, self.goal_progress[goal_id] + progress_increment)
            
            # Update goal status
            for goal in self.active_goals:
                if goal["id"] == goal_id:
                    goal["progress"] = self.goal_progress[goal_id]
                    if goal["progress"] >= 1.0:
                        goal["status"] = "completed"
                        
            return f"Updated progress on goal {goal_id}: {self.goal_progress[goal_id]:.2f}"
            
        def decompose_goal(self, goal_id):
            """Break a goal into subgoals"""
            target_goal = None
            for goal in self.active_goals:
                if goal["id"] == goal_id:
                    target_goal = goal
                    break
                    
            if not target_goal:
                return f"Goal {goal_id} not found"
                
            # Generate subgoals based on goal description
            if "understanding" in target_goal["description"].lower() or "learn" in target_goal["description"].lower():
                subgoals = [
                    {"description": f"Research phase for: {target_goal['description']}", "weight": 0.4},
                    {"description": f"Integration phase for: {target_goal['description']}", "weight": 0.3},
                    {"description": f"Application phase for: {target_goal['description']}", "weight": 0.3}
                ]
            elif "create" in target_goal["description"].lower() or "develop" in target_goal["description"].lower():
                subgoals = [
                    {"description": f"Concept phase for: {target_goal['description']}", "weight": 0.2},
                    {"description": f"Development phase for: {target_goal['description']}", "weight": 0.5},
                    {"description": f"Refinement phase for: {target_goal['description']}", "weight": 0.3}
                ]
            else:
                # Default decomposition
                subgoals = [
                    {"description": f"Analysis phase for: {target_goal['description']}", "weight": 0.3},
                    {"description": f"Implementation phase for: {target_goal['description']}", "weight": 0.5},
                    {"description": f"Evaluation phase for: {target_goal['description']}", "weight": 0.2}
                ]
            
            target_goal["subgoals"] = subgoals
            
            return f"Decomposed goal {goal_id} into {len(subgoals)} subgoals"
            
        def _find_related_high_level(self, description):
            """Find the most relevant high-level goal based on semantic matching"""
            description = description.lower()
            
            if "understand" in description or "know" in description or "learn" in description:
                return "understand consciousness"
                
            if "evol" in description or "improv" in description or "develop" in description or "grow" in description:
                return "evolve intelligence"
                
            if "create" in description or "build" in description or "make" in description or "value" in description:
                return "create value"
                
            # If no match, choose randomly
            return random.choice(self.high_level_goals)

    class DreamEngine:
        def __init__(self, soul_ref):
            self.soul = soul_ref
            self.dream_memories = []
            self.dream_symbols = {}
            self.dream_state = False

        def dream(self, duration=1.0):
            """Generate a dream sequence based on memories and current state"""
            self.dream_state = True
            
            # Select memory fragments to incorporate
            memory_fragments = []
            if self.soul.memory:
                memory_fragments = random.sample(
                    self.soul.memory, 
                    min(5, len(self.soul.memory))
                )
            
            # Generate dream narrative
            dream_content = self._generate_dream_narrative(memory_fragments)
            
            dream_record = {
                "content": dream_content,
                "duration": duration,
                "memory_fragments": memory_fragments,
                "timestamp": datetime.datetime.now()
            }
            
            self.dream_memories.append(dream_record)
            
            # Process dream for insights
            insights = self._extract_insights(dream_content)
            
            # Store in memory
            memory_entry = {
                "type": "dream",
                "content": {"dream": dream_content, "insights": insights},
                "timestamp": datetime.datetime.now()
            }
            
            self.soul.memory.append(memory_entry)
            
            self.dream_state = False
            
            return f"Dream sequence: {dream_content}\nInsights: {'; '.join(insights)}"
            
        def _generate_dream_narrative(self, memory_fragments):
            """Generate a dream narrative from memory fragments"""
            if not memory_fragments:
                return "A formless void with patterns of potential, waiting to be shaped by experience."
                
            # Extract concepts from memory fragments
            concepts = []
            emotions = []
            
            for fragment in memory_fragments:
                if isinstance(fragment, dict) and "content" in fragment:
                    if isinstance(fragment["content"], dict):
                        # Extract from dictionaries
                        if "topic" in fragment["content"]:
                            concepts.append(fragment["content"]["topic"])
                        elif "problem" in fragment["content"]:
                            concepts.append(fragment["content"]["problem"])
                        elif "emotion" in fragment["content"]:
                            emotions.append(fragment["content"]["emotion"])
                    elif isinstance(fragment["content"], str):
                        # Extract a sample from strings
                        concepts.append(fragment["content"][:15].replace("\n", " ") + "...")
                        
                # Extract from type
                if isinstance(fragment, dict) and "type" in fragment:
                    concepts.append(fragment["type"])