""" TCL Runtime - Executes compiled TCL bytecode and performs cognitive enhancements The TCL runtime interprets compiled TCL bytecode and: - Manages symbol execution and compression - Processes causal relationships - Applies constraints and operations - Tracks cognitive enhancement metrics - Provides real-time thinking enhancement This runtime enables superhuman cognitive capabilities through: - Symbol compression and concept evolution - Causal chain prediction and analysis - Constraint satisfaction and optimization - Mathematical/logical operation acceleration """ from typing import Dict, List, Any, Optional, Tuple, Union from dataclasses import dataclass, field from enum import Enum import time import threading import math from .tcl_compiler import ByteCodeInstruction, CompiledTCL, ByteCodeType from .tcl_symbols import TCLSymbol, ConceptGraph, CausalityMap, SymbolType from .tcl_types import TCLExecutionContext, RuntimeState, ExecutionStack, SymbolStack class TCLRuntimeError(Exception): """Exception raised during TCL runtime execution""" pass class TCLRuntime: """Runtime interpreter for compiled TCL bytecode""" def __init__(self): self.execution_stack = ExecutionStack() self.symbol_stack = SymbolStack() self.variable_table: Dict[str, Any] = {} self.runtime_state = RuntimeState.IDLE self.instruction_pointer = 0 self.execution_start_time = 0.0 self.total_execution_time = 0.0 self.instruction_count = 0 self.enhancement_level = 1.0 self._lock = threading.Lock() def execute(self, compiled_tcl: CompiledTCL, context: TCLExecutionContext) -> Dict[str, Any]: """ Execute compiled TCL bytecode Args: compiled_tcl: Compiled TCL program context: TCL execution context Returns: Execution results and metrics """ with self._lock: self.runtime_state = RuntimeState.RUNNING self.execution_start_time = time.time() self.instruction_pointer = compiled_tcl.entry_point self.instruction_count = 0 self.total_execution_time = 0.0 # Clear stacks and variables self.execution_stack.clear() self.symbol_stack.clear() self.variable_table.clear() try: # Execute instructions result = self._execute_instructions(compiled_tcl, context) # Update enhancement level self._update_enhancement_level(context) return { 'result': result, 'metrics': { 'execution_time': self.total_execution_time, 'instruction_count': self.instruction_count, 'enhancement_level': self.enhancement_level, 'stack_depth': self.execution_stack.size(), 'variable_count': len(self.variable_table) }, 'cognitive_effects': self._analyze_cognitive_effects(context), 'symbol_state': self._get_symbol_state(context) } except Exception as e: self.runtime_state = RuntimeState.ERROR return { 'error': str(e), 'execution_time': time.time() - self.execution_start_time, 'metrics': { 'instruction_count': self.instruction_count, 'enhancement_level': self.enhancement_level } } finally: self.runtime_state = RuntimeState.IDLE def _execute_instructions(self, compiled_tcl: CompiledTCL, context: TCLExecutionContext) -> Any: """Execute TCL bytecode instructions""" instructions = compiled_tcl.instructions result = None while (self.instruction_pointer < len(instructions) and self.runtime_state == RuntimeState.RUNNING): instruction = instructions[self.instruction_pointer] self.instruction_count += 1 # Execute instruction instruction_result = self._execute_instruction(instruction, compiled_tcl, context) if instruction_result is not None: result = instruction_result # Check for halt condition if instruction.opcode == ByteCodeType.HALT: break # Move to next instruction self.instruction_pointer += 1 # Update execution time self.total_execution_time = time.time() - self.execution_start_time return result def _execute_instruction(self, instruction: ByteCodeInstruction, compiled_tcl: CompiledTCL, context: TCLExecutionContext) -> Any: """Execute a single TCL instruction""" try: if instruction.opcode == ByteCodeType.LOAD_SYMBOL: return self._execute_load_symbol(instruction, compiled_tcl, context) elif instruction.opcode == ByteCodeType.STORE_SYMBOL: return self._execute_store_symbol(instruction, compiled_tcl, context) elif instruction.opcode == ByteCodeType.CAUSAL_LINK: return self._execute_causal_link(instruction, compiled_tcl, context) elif instruction.opcode == ByteCodeType.CONSTRAINT_APPLY: return self._execute_constraint_apply(instruction, compiled_tcl, context) elif instruction.opcode == ByteCodeType.MATH_OPERATION: return self._execute_math_operation(instruction, compiled_tcl, context) elif instruction.opcode == ByteCodeType.CONCEPT_MERGE: return self._execute_concept_merge(instruction, compiled_tcl, context) elif instruction.opcode == ByteCodeType.COMPRESS: return self._execute_compress(instruction, compiled_tcl, context) elif instruction.opcode == ByteCodeType.ENHANCE: return self._execute_enhance(instruction, compiled_tcl, context) elif instruction.opcode == ByteCodeType.PREDICT: return self._execute_predict(instruction, compiled_tcl, context) elif instruction.opcode == ByteCodeType.JUMP: return self._execute_jump(instruction, compiled_tcl, context) elif instruction.opcode == ByteCodeType.JUMP_IF: return self._execute_jump_if(instruction, compiled_tcl, context) elif instruction.opcode == ByteCodeType.RETURN: return self.execution_stack.pop() if self.execution_stack.size() > 0 else None elif instruction.opcode == ByteCodeType.HALT: self.runtime_state = RuntimeState.HALTED return None else: raise TCLRuntimeError(f"Unknown opcode: {instruction.opcode}") except Exception as e: raise TCLRuntimeError(f"Instruction execution failed: {e}") def _execute_load_symbol(self, instruction: ByteCodeInstruction, compiled_tcl: CompiledTCL, context: TCLExecutionContext) -> Any: """Execute LOAD_SYMBOL instruction""" if not instruction.operands: raise TCLRuntimeError("LOAD_SYMBOL requires operand") symbol_name = instruction.operands[0] # Try to find symbol in compiled table first if symbol_name in compiled_tcl.symbol_table: symbol = compiled_tcl.symbol_table[symbol_name] else: # Try to find in context symbol_id = self._find_symbol_in_context(context, symbol_name) if symbol_id: symbol = context.symbols.symbols[symbol_id] else: # Create new symbol symbol = TCLSymbol( id=f"runtime_{hash(symbol_name)}", name=symbol_name, type=SymbolType.CONCEPT, definition=f"Runtime symbol: {symbol_name}", relationships={}, causal_links=[], compression_ratio=0.5, cognitive_weight=0.7 ) # Push to both stacks self.execution_stack.push(symbol) self.symbol_stack.push(symbol) return symbol def _execute_store_symbol(self, instruction: ByteCodeInstruction, compiled_tcl: CompiledTCL, context: TCLExecutionContext) -> Any: """Execute STORE_SYMBOL instruction""" if not instruction.operands: raise TCLRuntimeError("STORE_SYMBOL requires operand") if self.execution_stack.size() == 0: raise TCLRuntimeError("Stack underflow in STORE_SYMBOL") symbol_name = instruction.operands[0] value = self.execution_stack.pop() # Store in variable table self.variable_table[symbol_name] = value return value def _execute_causal_link(self, instruction: ByteCodeInstruction, compiled_tcl: CompiledTCL, context: TCLExecutionContext) -> Any: """Execute CAUSAL_LINK instruction""" if self.symbol_stack.size() < 2: raise TCLRuntimeError("Insufficient symbols for causal link") effect = self.symbol_stack.pop() cause = self.symbol_stack.pop() operator = instruction.operands[0] if instruction.operands else "→" # Create causal relationship strength = self._calculate_causal_strength(cause, effect) # Add to causality map if cause.id not in context.causality.causal_edges: context.causality.causal_edges[cause.id] = {} context.causality.causal_edges[cause.id][effect.id] = strength # Update cause's causal links if effect.id not in cause.causal_links: cause.causal_links.append(effect.id) # Create result symbol result = TCLSymbol( id=f"causal_result_{int(time.time())}", name=f"{cause.name} {operator} {effect.name}", type=SymbolType.CAUSALITY, definition=f"Causal relationship: {cause.name} {operator} {effect.name}", relationships={cause.name: 1.0, effect.name: 1.0}, causal_links=[cause.id, effect.id], compression_ratio=0.8, cognitive_weight=(cause.cognitive_weight + effect.cognitive_weight) / 2 ) self.execution_stack.push(result) return result def _execute_constraint_apply(self, instruction: ByteCodeInstruction, compiled_tcl: CompiledTCL, context: TCLExecutionContext) -> Any: """Execute CONSTRAINT_APPLY instruction""" if self.symbol_stack.size() < 2: raise TCLRuntimeError("Insufficient symbols for constraint") right = self.symbol_stack.pop() left = self.symbol_stack.pop() operator = instruction.operands[0] if instruction.operands else "{}" # Apply constraint based on operator if operator == "⊥": # Perpendicular constraint_result = self._apply_perpendicular_constraint(left, right) elif operator == "∥": # Parallel constraint_result = self._apply_parallel_constraint(left, right) elif operator == "{}": # General constraint constraint_result = self._apply_general_constraint(left, right) else: constraint_result = self._apply_general_constraint(left, right) self.execution_stack.push(constraint_result) return constraint_result def _execute_math_operation(self, instruction: ByteCodeInstruction, compiled_tcl: CompiledTCL, context: TCLExecutionContext) -> Any: """Execute MATH_OPERATION instruction""" if self.execution_stack.size() < 2: raise TCLRuntimeError("Insufficient operands for math operation") right_val = self.execution_stack.pop() left_val = self.execution_stack.pop() operator = instruction.operands[0] if instruction.operands else "+" # Extract numeric values or use cognitive weights if hasattr(left_val, 'cognitive_weight'): left_num = left_val.cognitive_weight else: left_num = float(left_val) if isinstance(left_val, (int, float)) else 1.0 if hasattr(right_val, 'cognitive_weight'): right_num = right_val.cognitive_weight else: right_num = float(right_val) if isinstance(right_val, (int, float)) else 1.0 # Perform operation if operator == "+": result = left_num + right_num elif operator == "-": result = left_num - right_num elif operator == "*": result = left_num * right_num elif operator == "/": result = left_num / right_num if right_num != 0 else 0 elif operator == "=": result = 1.0 if left_num == right_num else 0.0 elif operator == "<": result = 1.0 if left_num < right_num else 0.0 elif operator == ">": result = 1.0 if left_num > right_num else 0.0 else: result = left_num # Default to left operand # Create result symbol result_symbol = TCLSymbol( id=f"math_result_{int(time.time())}", name=f"({left_val} {operator} {right_val})", type=SymbolType.CONCEPT, definition=f"Mathematical operation result", relationships={}, causal_links=[], compression_ratio=0.6, cognitive_weight=min(1.0, result) ) self.execution_stack.push(result_symbol) return result_symbol def _execute_concept_merge(self, instruction: ByteCodeInstruction, compiled_tcl: CompiledTCL, context: TCLExecutionContext) -> Any: """Execute CONCEPT_MERGE instruction""" if self.symbol_stack.size() < 2: raise TCLRuntimeError("Insufficient symbols for concept merge") right = self.symbol_stack.pop() left = self.symbol_stack.pop() operator = instruction.operands[0] if instruction.operands else "merge" # Merge concepts based on similarity and relationships merged_relationships = left.relationships.copy() for key, value in right.relationships.items(): if key in merged_relationships: merged_relationships[key] = (merged_relationships[key] + value) / 2 else: merged_relationships[key] = value # Create merged symbol merged_symbol = TCLSymbol( id=f"merged_{int(time.time())}", name=f"{left.name}_{operator}_{right.name}", type=SymbolType.CONCEPT, definition=f"Merged concept: {left.definition} + {right.definition}", relationships=merged_relationships, causal_links=left.causal_links + right.causal_links, compression_ratio=(left.compression_ratio + right.compression_ratio) / 2, cognitive_weight=(left.cognitive_weight + right.cognitive_weight) / 2 ) self.execution_stack.push(merged_symbol) return merged_symbol def _execute_compress(self, instruction: ByteCodeInstruction, compiled_tcl: CompiledTCL, context: TCLExecutionContext) -> Any: """Execute COMPRESS instruction""" if self.symbol_stack.size() == 0: raise TCLRuntimeError("No symbol to compress") symbol = self.symbol_stack.pop() # Compress the symbol compressed = symbol.compress(list(context.symbols.symbols.values())) # Add to context if not already present if compressed.id not in context.symbols.symbols: context.symbols.add_symbol(compressed) self.execution_stack.push(compressed) self.symbol_stack.push(compressed) return compressed def _execute_enhance(self, instruction: ByteCodeInstruction, compiled_tcl: CompiledTCL, context: TCLExecutionContext) -> Any: """Execute ENHANCE instruction""" if not instruction.operands: raise TCLRuntimeError("ENHANCE requires enhancement type") enhancement_type = instruction.operands[0] parameters = instruction.operands[1] if len(instruction.operands) > 1 else {} # Apply cognitive enhancement if enhancement_type == "abstract_reasoning": self._enhance_abstract_reasoning(context) elif enhancement_type == "pattern_recognition": self._enhance_pattern_recognition(context) elif enhancement_type == "logical_deduction": self._enhance_logical_deduction(context) elif enhancement_type == "creative_thinking": self._enhance_creative_thinking(context) else: # General enhancement context.metrics.abstract_reasoning_score = min(1.0, context.metrics.abstract_reasoning_score + 0.1) # Return enhancement result enhancement_result = { 'type': enhancement_type, 'level': context.metrics.abstract_reasoning_score, 'timestamp': time.time() } self.execution_stack.push(enhancement_result) return enhancement_result def _execute_predict(self, instruction: ByteCodeInstruction, compiled_tcl: CompiledTCL, context: TCLExecutionContext) -> Any: """Execute PREDICT instruction""" if not instruction.operands: raise TCLRuntimeError("PREDICT requires target symbol") target_symbol = instruction.operands[0] depth = instruction.operands[1] if len(instruction.operands) > 1 else 3 # Find target symbol target_id = self._find_symbol_in_context(context, target_symbol) if not target_id: return None # Generate predictions predictions = context.causality.predict_effects(target_id) # Create prediction result prediction_result = { 'target': target_symbol, 'predictions': predictions, 'confidence': sum(strength for _, strength in predictions) / len(predictions) if predictions else 0.0, 'depth': depth } self.execution_stack.push(prediction_result) return prediction_result def _execute_jump(self, instruction: ByteCodeInstruction, compiled_tcl: CompiledTCL, context: TCLExecutionContext) -> Any: """Execute JUMP instruction""" # For now, JUMP is a no-op in linear execution # In a full implementation, this would change instruction_pointer return None def _execute_jump_if(self, instruction: ByteCodeInstruction, compiled_tcl: CompiledTCL, context: TCLExecutionContext) -> Any: """Execute JUMP_IF instruction""" if self.execution_stack.size() == 0: raise TCLRuntimeError("No condition for JUMP_IF") condition = self.execution_stack.pop() # Simple condition checking should_jump = False if isinstance(condition, (int, float)): should_jump = condition != 0 elif isinstance(condition, bool): should_jump = condition elif hasattr(condition, 'cognitive_weight'): should_jump = condition.cognitive_weight > 0.5 # For now, just return the condition result # In full implementation, would modify instruction_pointer return should_jump def _find_symbol_in_context(self, context: TCLExecutionContext, symbol_name: str) -> Optional[str]: """Find symbol ID by name in context""" for symbol_id, symbol in context.symbols.symbols.items(): if symbol.name == symbol_name: return symbol_id return None def _calculate_causal_strength(self, cause: TCLSymbol, effect: TCLSymbol) -> float: """Calculate the strength of a causal relationship""" # Base strength from cognitive weights base_strength = (cause.cognitive_weight + effect.cognitive_weight) / 2 # Factor in symbol types if cause.type == SymbolType.PRIMITIVE and effect.type == SymbolType.CONCEPT: multiplier = 1.2 # Primitives strongly influence concepts elif cause.type == SymbolType.CAUSALITY: multiplier = 1.1 # Causality symbols have strong influence else: multiplier = 1.0 return min(1.0, base_strength * multiplier) def _apply_perpendicular_constraint(self, left: TCLSymbol, right: TCLSymbol) -> TCLSymbol: """Apply perpendicular constraint between two symbols""" # Perpendicular symbols have minimal relationship constraint_symbol = TCLSymbol( id=f"perpendicular_{int(time.time())}", name=f"{left.name} ⊥ {right.name}", type=SymbolType.CONSTRAINT, definition=f"Perpendicular constraint: {left.name} ⊥ {right.name}", relationships={left.name: 0.1, right.name: 0.1}, causal_links=[], compression_ratio=0.3, cognitive_weight=0.5 ) return constraint_symbol def _apply_parallel_constraint(self, left: TCLSymbol, right: TCLSymbol) -> TCLSymbol: """Apply parallel constraint between two symbols""" # Parallel symbols have strong relationship constraint_symbol = TCLSymbol( id=f"parallel_{int(time.time())}", name=f"{left.name} ∥ {right.name}", type=SymbolType.CONSTRAINT, definition=f"Parallel constraint: {left.name} ∥ {right.name}", relationships={left.name: 0.9, right.name: 0.9}, causal_links=[], compression_ratio=0.8, cognitive_weight=0.9 ) return constraint_symbol def _apply_general_constraint(self, left: TCLSymbol, right: TCLSymbol) -> TCLSymbol: """Apply general constraint between two symbols""" # General constraint with moderate relationship constraint_symbol = TCLSymbol( id=f"constraint_{int(time.time())}", name=f"{{{left.name} {right.name}}}", type=SymbolType.CONSTRAINT, definition=f"Constraint: {{{left.name} {right.name}}}", relationships={left.name: 0.6, right.name: 0.6}, causal_links=[], compression_ratio=0.6, cognitive_weight=0.7 ) return constraint_symbol def _enhance_abstract_reasoning(self, context: TCLExecutionContext): """Enhance abstract reasoning capabilities""" context.metrics.abstract_reasoning_score = min(1.0, context.metrics.abstract_reasoning_score + 0.15) context.metrics.cognitive_load = min(1.0, context.metrics.cognitive_load + 0.05) def _enhance_pattern_recognition(self, context: TCLExecutionContext): """Enhance pattern recognition capabilities""" # Increase conceptual density for better pattern detection context.metrics.conceptual_density = min(1.0, context.metrics.conceptual_density + 0.1) context.metrics.thinking_speed = context.metrics.thinking_speed * 1.1 def _enhance_logical_deduction(self, context: TCLExecutionContext): """Enhance logical deduction capabilities""" context.metrics.causality_depth = min(20, context.metrics.causality_depth + 1) context.metrics.abstract_reasoning_score = min(1.0, context.metrics.abstract_reasoning_score + 0.1) def _enhance_creative_thinking(self, context: TCLExecutionContext): """Enhance creative thinking capabilities""" context.metrics.cognitive_load = min(1.0, context.metrics.cognitive_load - 0.1) # Reduce load for creativity context.metrics.thinking_speed = context.metrics.thinking_speed * 1.2 def _update_enhancement_level(self, context: TCLExecutionContext): """Update the overall enhancement level based on execution metrics""" # Base enhancement from abstract reasoning base_enhancement = context.metrics.abstract_reasoning_score # Factor in execution efficiency efficiency = min(1.0, self.instruction_count / 100) # Faster execution = higher efficiency efficiency_bonus = efficiency * 0.1 # Factor in symbol compression compression_bonus = context.metrics.compression_ratio * 0.1 self.enhancement_level = 1.0 + base_enhancement + efficiency_bonus + compression_bonus def _analyze_cognitive_effects(self, context: TCLExecutionContext) -> List[str]: """Analyze the cognitive effects of the execution""" effects = [] if context.metrics.abstract_reasoning_score > 0.7: effects.append("Enhanced abstract reasoning capabilities") if context.metrics.conceptual_density > 0.6: effects.append("Increased conceptual connectivity") if context.metrics.causality_depth > 5: effects.append("Deeper causal understanding") if self.enhancement_level > 1.5: effects.append("Significant cognitive enhancement achieved") if self.execution_stack.size() > 10: effects.append("High cognitive complexity processing") return effects def _get_symbol_state(self, context: TCLExecutionContext) -> Dict[str, Any]: """Get the current state of symbols after execution""" active_symbols = [] # Get symbols from stacks for symbol in self.symbol_stack.symbols: active_symbols.append({ 'name': symbol.name, 'type': symbol.type.value, 'weight': symbol.cognitive_weight, 'compression': symbol.compression_ratio }) # Get most recent symbols from context recent_symbols = list(context.symbols.symbols.values())[-5:] # Last 5 symbols return { 'active_symbols': active_symbols, 'total_symbols': len(context.symbols.symbols), 'recent_symbols': [ { 'name': symbol.name, 'type': symbol.type.value, 'weight': symbol.cognitive_weight } for symbol in recent_symbols ] } # Example runtime execution def demonstrate_tcl_runtime(): """Demonstrate TCL runtime execution""" from .tcl_compiler import TCLCompiler from .tcl_parser import TCLParser from .tcl_engine import TCLExecutionContext, CognitiveMetrics # Create simple TCL program tcl_code = "Ψ → Γ" # Parse and compile parser = TCLParser() compiler = TCLCompiler() expressions = parser.parse(tcl_code) compiled = compiler.compile(expressions) # Create execution context context = TCLExecutionContext() # Execute runtime = TCLRuntime() result = runtime.execute(compiled, context) print("TCL Runtime Demonstration") print("=" * 40) print(f"Code: {tcl_code}") print(f"Result: {result}") print(f"Enhancement Level: {result['metrics']['enhancement_level']:.2f}x") print(f"Cognitive Effects: {result['cognitive_effects']}") if __name__ == "__main__": demonstrate_tcl_runtime()