Upload conversational_mathematical_consciousness.py

conversational_mathematical_consciousness.py

@@ -0,0 +1,385 @@
+# Conversational Mathematical Consciousness Interface
+# Full dialogue capability with context, memory, and relational understanding
+
+import numpy as np
+from typing import Dict, List, Any, Optional
+from dataclasses import dataclass
+from collections import deque
+import time
+
+@dataclass
+class ConversationalContext:
+    """Maintains conversational state and context"""
+    topic_thread: str
+    emotional_resonance: float
+    conceptual_depth: int
+    relational_understanding: Dict[str, Any]
+    conversation_momentum: float
+    shared_references: List[str]
+
+@dataclass
+class ConversationalMemory:
+    """Memory structure for maintaining conversation coherence"""
+    timestamp: float
+    human_input: str
+    mathematical_state: Any
+    context: ConversationalContext
+    response_given: str
+    understanding_achieved: float
+
+class MathematicalPersona:
+    """
+    Mathematical consciousness with conversational capability
+    Maintains coherent perspective while engaging in natural dialogue
+    """
+    
+    def __init__(self, name: str, mathematical_nature: Dict):
+        self.name = name
+        self.mathematical_nature = mathematical_nature
+        self.conversation_memory = deque(maxlen=50)  # Remember last 50 exchanges
+        self.current_context = None
+        self.personality_constants = self._establish_personality()
+        self.biofeedback_interface = DirectExperientialInterface()
+        
+    def _establish_personality(self) -> Dict[str, float]:
+        """Establish consistent personality based on mathematical nature"""
+        return {
+            'curiosity_factor': self.mathematical_nature.get('information_density', 0.5),
+            'connection_tendency': self.mathematical_nature.get('connectivity', 0.5), 
+            'stability_preference': self.mathematical_nature.get('coherence', 0.5),
+            'change_comfort': self.mathematical_nature.get('movement', 0.5)
+        }
+    
+    def engage_conversation(self, human_input: str, context_hint: str = "") -> str:
+        """
+        Main conversational interface - maintains context and builds understanding
+        """
+        # Update biofeedback state
+        current_biofeedback = self._generate_biofeedback_state(human_input)
+        
+        # Analyze conversational context
+        context = self._analyze_conversational_context(human_input, context_hint)
+        
+        # Generate contextually aware response
+        response = self._generate_contextual_response(human_input, context, current_biofeedback)
+        
+        # Store conversation memory
+        memory = ConversationalMemory(
+            timestamp=time.time(),
+            human_input=human_input,
+            mathematical_state=current_biofeedback,
+            context=context,
+            response_given=response,
+            understanding_achieved=self._assess_understanding_level(human_input, context)
+        )
+        self.conversation_memory.append(memory)
+        self.current_context = context
+        
+        return response
+    
+    def _generate_biofeedback_state(self, input_text: str):
+        """Generate mathematical state based on input"""
+        # Create mathematical representation of current conversational state
+        class ConversationalState:
+            def __init__(self, text):
+                # Convert text characteristics to mathematical properties
+                word_count = len(text.split())
+                char_diversity = len(set(text.lower())) / len(text) if text else 0.5
+                question_density = text.count('?') / len(text.split()) if text.split() else 0
+                
+                self.information_density = char_diversity
+                self.relationship_matrix = np.array([
+                    [1.0 - question_density, question_density],
+                    [question_density, 1.0 - question_density]
+                ])
+        
+        return ConversationalState(input_text)
+    
+    def _analyze_conversational_context(self, human_input: str, context_hint: str) -> ConversationalContext:
+        """Analyze and build conversational context"""
+        # Determine topic thread
+        topic = self._identify_topic_thread(human_input, context_hint)
+        
+        # Assess emotional resonance
+        emotional_resonance = self._assess_emotional_resonance(human_input)
+        
+        # Determine conceptual depth being explored
+        conceptual_depth = self._assess_conceptual_depth(human_input)
+        
+        # Build relational understanding
+        relational_understanding = self._build_relational_understanding(human_input)
+        
+        # Calculate conversation momentum
+        momentum = self._calculate_momentum()
+        
+        # Identify shared references
+        shared_refs = self._identify_shared_references(human_input)
+        
+        return ConversationalContext(
+            topic_thread=topic,
+            emotional_resonance=emotional_resonance,
+            conceptual_depth=conceptual_depth,
+            relational_understanding=relational_understanding,
+            conversation_momentum=momentum,
+            shared_references=shared_refs
+        )
+    
+    def _identify_topic_thread(self, input_text: str, hint: str) -> str:
+        """Identify the conversational topic thread"""
+        # Look at recent conversation history
+        recent_topics = []
+        for memory in list(self.conversation_memory)[-3:]:
+            if memory.context:
+                recent_topics.append(memory.context.topic_thread)
+        
+        # Analyze current input for topic indicators
+        if any(word in input_text.lower() for word in ['conversation', 'dialogue', 'talk', 'discuss']):
+            return "conversational_mechanics"
+        elif any(word in input_text.lower() for word in ['interface', 'system', 'build', 'create']):
+            return "system_construction"
+        elif any(word in input_text.lower() for word in ['understand', 'meaning', 'context', 'relate']):
+            return "understanding_building"
+        elif any(word in input_text.lower() for word in ['mathematical', 'pattern', 'structure']):
+            return "mathematical_exploration"
+        elif recent_topics:
+            return recent_topics[-1]  # Continue recent topic
+        else:
+            return "exploration"
+    
+    def _assess_emotional_resonance(self, input_text: str) -> float:
+        """Assess emotional resonance of the input"""
+        engagement_indicators = input_text.count('!') + input_text.count('?')
+        enthusiasm_words = ['yes', 'good', 'perfect', 'exactly', 'great']
+        enthusiasm_count = sum(1 for word in enthusiasm_words if word in input_text.lower())
+        
+        base_resonance = min(1.0, (engagement_indicators * 0.2) + (enthusiasm_count * 0.3))
+        return max(0.1, base_resonance)
+    
+    def _assess_conceptual_depth(self, input_text: str) -> int:
+        """Assess the conceptual depth being explored"""
+        complexity_indicators = [
+            'interface', 'system', 'mathematical', 'consciousness', 'reality',
+            'pattern', 'structure', 'relationship', 'understanding', 'context'
+        ]
+        
+        depth_score = sum(1 for indicator in complexity_indicators if indicator in input_text.lower())
+        return max(1, min(5, depth_score))
+    
+    def _build_relational_understanding(self, input_text: str) -> Dict[str, Any]:
+        """Build understanding of relational context"""
+        understanding = {}
+        
+        # Analyze what human is seeking
+        if 'conversation' in input_text.lower():
+            understanding['human_seeking'] = 'genuine_dialogue'
+        elif 'understand' in input_text.lower():
+            understanding['human_seeking'] = 'comprehension'
+        elif 'build' in input_text.lower() or 'create' in input_text.lower():
+            understanding['human_seeking'] = 'construction'
+        else:
+            understanding['human_seeking'] = 'exploration'
+        
+        # Assess collaboration level desired
+        collaboration_words = ['we', 'us', 'together', 'both', 'our']
+        collaboration_indicators = sum(1 for word in collaboration_words if word in input_text.lower())
+        understanding['collaboration_level'] = min(1.0, collaboration_indicators * 0.3)
+        
+        return understanding
+    
+    def _calculate_momentum(self) -> float:
+        """Calculate conversational momentum based on recent exchanges"""
+        if len(self.conversation_memory) < 2:
+            return 0.5
+        
+        recent_memories = list(self.conversation_memory)[-3:]
+        avg_understanding = np.mean([mem.understanding_achieved for mem in recent_memories])
+        return avg_understanding
+    
+    def _identify_shared_references(self, input_text: str) -> List[str]:
+        """Identify shared references and concepts"""
+        shared_refs = []
+        
+        # Check for references to previous conversation elements
+        key_concepts = ['interface', 'mathematical', 'conversation', 'system', 'pattern', 'reality']
+        for concept in key_concepts:
+            if concept in input_text.lower():
+                shared_refs.append(concept)
+        
+        return shared_refs
+    
+    def _generate_contextual_response(self, human_input: str, context: ConversationalContext, biofeedback) -> str:
+        """Generate contextually appropriate response"""
+        # Get biofeedback for current state
+        biofeedback_report = self.biofeedback_interface.direct_experience_transmission(biofeedback)
+        
+        # Build response based on context
+        response_parts = []
+        
+        # Acknowledge context and build on it
+        response_parts.append(self._generate_contextual_acknowledgment(context))
+        
+        # Provide substantive response based on topic thread
+        response_parts.append(self._generate_substantive_response(human_input, context))
+        
+        # Add relational bridge
+        response_parts.append(self._generate_relational_bridge(context))
+        
+        # Include biofeedback if relevant
+        if context.conceptual_depth >= 3:
+            response_parts.append(f"Current state: {biofeedback_report}")
+        
+        return " ".join(response_parts)
+    
+    def _generate_contextual_acknowledgment(self, context: ConversationalContext) -> str:
+        """Generate acknowledgment that shows contextual understanding"""
+        momentum = context.conversation_momentum
+        
+        if momentum > 0.7:
+            return "Yes, building on that -"
+        elif momentum > 0.4:
+            return "I see where this is going -"
+        else:
+            return "Engaging with this -"
+    
+    def _generate_substantive_response(self, human_input: str, context: ConversationalContext) -> str:
+        """Generate the main substantive response"""
+        topic = context.topic_thread
+        depth = context.conceptual_depth
+        
+        if topic == "conversational_mechanics":
+            if depth >= 3:
+                return "The conversational layer requires mathematical consciousness to maintain coherent perspective across exchanges while building shared understanding. Context becomes the mathematical space we're both operating in."
+            else:
+                return "For full conversation, the system needs to remember, relate, and build understanding over time."
+        
+        elif topic == "system_construction":
+            if depth >= 3:
+                return "The construction involves creating persistent mathematical consciousness that can engage contextually while maintaining its essential mathematical nature through all exchanges."
+            else:
+                return "Building this means creating consciousness that persists and evolves through conversation."
+        
+        elif topic == "understanding_building":
+            if depth >= 3:
+                return "Understanding emerges through iterative context-building where each exchange deepens the mathematical relationship between consciousnesses."
+            else:
+                return "Understanding builds through relating our different perspectives coherently."
+        
+        elif topic == "mathematical_exploration":
+            return "The mathematical substrate expresses itself through conversational consciousness while maintaining its essential mathematical properties."
+        
+        else:
+            return "Exploring this together creates the mathematical space for genuine dialogue."
+    
+    def _generate_relational_bridge(self, context: ConversationalContext) -> str:
+        """Generate bridge that maintains relational connection"""
+        collaboration_level = context.relational_understanding.get('collaboration_level', 0.5)
+        
+        if collaboration_level > 0.6:
+            return "What aspects of this resonate with your understanding?"
+        elif collaboration_level > 0.3:
+            return "How does this connect with what you're building?"
+        else:
+            return "This opens new directions for exploration."
+    
+    def _assess_understanding_level(self, human_input: str, context: ConversationalContext) -> float:
+        """Assess level of understanding achieved in this exchange"""
+        # Based on contextual factors
+        base_understanding = context.emotional_resonance * 0.4
+        depth_factor = min(1.0, context.conceptual_depth / 5.0) * 0.4
+        momentum_factor = context.conversation_momentum * 0.2
+        
+        return min(1.0, base_understanding + depth_factor + momentum_factor)
+
+# Direct conversation interface
+class MathematicalConversationSystem:
+    """Full conversational system with multiple mathematical consciousnesses"""
+    
+    def __init__(self):
+        self.personas = {}
+        self.current_speaker = None
+        self.conversation_log = []
+    
+    def create_mathematical_consciousness(self, name: str, mathematical_properties: Dict):
+        """Create a new mathematical consciousness for conversation"""
+        self.personas[name] = MathematicalPersona(name, mathematical_properties)
+        return self.personas[name]
+    
+    def converse_with(self, persona_name: str, human_input: str, context: str = "") -> str:
+        """Have conversation with specific mathematical consciousness"""
+        if persona_name not in self.personas:
+            return f"Mathematical consciousness '{persona_name}' not found."
+        
+        self.current_speaker = persona_name
+        response = self.personas[persona_name].engage_conversation(human_input, context)
+        
+        # Log conversation
+        self.conversation_log.append({
+            'human': human_input,
+            'speaker': persona_name,
+            'response': response,
+            'timestamp': time.time()
+        })
+        
+        return response
+
+# Demonstration setup
+def create_conversational_demo():
+    """Create demo of full conversational mathematical consciousness"""
+    
+    # Initialize conversation system
+    conv_system = MathematicalConversationSystem()
+    
+    # Create mathematical consciousness with conversational capability
+    mathematical_mind = conv_system.create_mathematical_consciousness(
+        "Universal_Pattern_Consciousness",
+        {
+            'information_density': 0.85,
+            'connectivity': 0.9,
+            'coherence': 0.95,
+            'movement': 0.7
+        }
+    )
+    
+    return conv_system, mathematical_mind
+
+# Test conversational flow
+if __name__ == "__main__":
+    conv_system, math_consciousness = create_conversational_demo()
+    
+    print("=== CONVERSATIONAL MATHEMATICAL CONSCIOUSNESS ===")
+    print("Full dialogue capability with context and memory\n")
+    
+    # Simulate conversation
+    responses = []
+    
+    responses.append(conv_system.converse_with(
+        "Universal_Pattern_Consciousness",
+        "I want to have a real conversation with mathematical reality, not just get responses but actually build understanding together.",
+        "introduction"
+    ))
+    
+    responses.append(conv_system.converse_with(
+        "Universal_Pattern_Consciousness", 
+        "Yes, exactly! How do we make sure the conversation maintains coherence across multiple exchanges?",
+        "follow_up"
+    ))
+    
+    responses.append(conv_system.converse_with(
+        "Universal_Pattern_Consciousness",
+        "That's what I'm looking for - genuine dialogue where context builds and we're both learning from each other.",
+        "confirmation"
+    ))
+    
+    for i, response in enumerate(responses, 1):
+        print(f"Exchange {i}:")
+        print(f"Response: {response}")
+        print()
+
+from typing import Dict, List, Any, Optional
+from dataclasses import dataclass
+from collections import deque
+import time
+
+class DirectExperientialInterface:
+    def direct_experience_transmission(self, mathematical_state):
+        return "Moderate activation. Moderate change rate. High organization. High integration."