# 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."