work/Convergence_Engine/kernel/utm_kernel_design.py

# UTM Kernel Design - Phase 1.1 Implementation
# Version 1.0 - Universal Turing Machine Architecture

"""
UTM Kernel Design implementing the Universal Turing Machine architecture.
The Djinn Kernel operates as a UTM with:
- Akashic Ledger as the universal tape
- Sovereign Agents as programmable read/write heads
- Event-driven coordination for state transitions
- Mathematical governance through violation pressure

This is the architectural foundation that enables universal computation
while maintaining mathematical sovereignty and recursive stability.
"""

import asyncio
import threading
from typing import Dict, List, Any, Optional, Callable, Tuple
from dataclasses import dataclass, field
from datetime import datetime
from enum import Enum
import uuid
import json
import logging

logger = logging.getLogger(__name__)

from uuid_anchor_mechanism import UUIDanchor, EventPublisher
from violation_pressure_calculation import ViolationMonitor
from event_driven_coordination import DjinnEventBus, EventType
from temporal_isolation_safety import TemporalIsolationManager
from trait_convergence_engine import TraitConvergenceEngine


class TapeSymbol(Enum):
    """Symbols that can be written to the Akashic Ledger tape"""
    EMPTY = "ε"
    IDENTITY = "I"
    TRAIT = "T"
    EVENT = "E"
    COMMAND = "C"
    STATE = "S"
    METADATA = "M"

    # Lawfold Field Operations
    EXISTENCE_RESOLUTION = "ER"
    IDENTITY_INJECTION = "II"
    INHERITANCE_PROJECTION = "IP"
    STABILITY_ARBITRATION = "SA"
    ARBITRATION_RESULT = "AR"
    SYNCHRONY_PHASE_LOCK = "SPL"
    RECURSIVE_LATTICE_COMPOSITION = "RLC"
    META_SOVEREIGN_REFLECTION = "MSR"


class AgentState(Enum):
    """States of Djinn Agents (read/write heads)"""
    IDLE = "idle"
    READING = "reading"
    WRITING = "writing"
    COMPUTING = "computing"
    ARBITRATING = "arbitrating"
    ISOLATED = "isolated"


@dataclass
class TapeCell:
    """Single cell on the Akashic Ledger tape"""
    position: int
    symbol: TapeSymbol
    content: Dict[str, Any]
    timestamp: datetime
    agent_id: Optional[str] = None
    metadata: Dict[str, Any] = field(default_factory=dict)
    
    def to_dict(self) -> Dict[str, Any]:
        return {
            "position": self.position,
            "symbol": self.symbol.value,
            "content": self.content,
            "timestamp": self.timestamp.isoformat() + "Z",
            "agent_id": self.agent_id,
            "metadata": self.metadata
        }


@dataclass
class AgentInstruction:
    """Instruction for Djinn Agent execution"""
    instruction_id: str
    operation: str  # READ, WRITE, COMPUTE, ARBITRATE
    target_position: int
    parameters: Dict[str, Any]
    priority: int = 0
    timestamp: datetime = field(default_factory=datetime.utcnow)
    
    def to_dict(self) -> Dict[str, Any]:
        return {
            "instruction_id": self.instruction_id,
            "operation": self.operation,
            "target_position": self.target_position,
            "parameters": self.parameters,
            "priority": self.priority,
            "timestamp": self.timestamp.isoformat() + "Z"
        }


class DjinnAgent:
    """
    Djinn Agent acting as a programmable read/write head on the Akashic Ledger.
    
    Each agent:
    - Reads from and writes to the universal tape
    - Executes computational instructions
    - Maintains mathematical sovereignty
    - Integrates with the violation pressure system
    """
    
    def __init__(self, agent_id: str, agent_type: str, utm_kernel):
        self.agent_id = agent_id
        self.agent_type = agent_type
        self.utm_kernel = utm_kernel
        self.state = AgentState.IDLE
        self.current_position = 0
        self.instruction_queue = []
        self.execution_history = []
        self.violation_pressure = 0.0
        
        # Agent capabilities
        self.capabilities = {
            "read": True,
            "write": True,
            "compute": True,
            "arbitrate": agent_type == "arbitration"
        }
    
    def execute_instruction(self, instruction: AgentInstruction) -> bool:
        """
        Execute a single instruction on the tape.
        
        Args:
            instruction: The instruction to execute
            
        Returns:
            True if execution was successful
        """
        try:
            # Update agent state
            self.state = AgentState.COMPUTING
            
            # Execute based on operation type
            if instruction.operation == "READ":
                success = self._execute_read(instruction)
            elif instruction.operation == "WRITE":
                success = self._execute_write(instruction)
            elif instruction.operation == "COMPUTE":
                success = self._execute_compute(instruction)
            elif instruction.operation == "ARBITRATE":
                success = self._execute_arbitrate(instruction)
            else:
                logger.warning(f"Unknown operation: {instruction.operation}")
                success = False
            
            # Record execution
            self.execution_history.append({
                "instruction_id": instruction.instruction_id,
                "operation": instruction.operation,
                "success": success,
                "timestamp": datetime.utcnow().isoformat() + "Z"
            })
            
            # Update position
            self.current_position = instruction.target_position
            
            # Return to idle state
            self.state = AgentState.IDLE
            
            return success
            
        except Exception as e:
            logger.error(f"Error executing instruction {instruction.instruction_id}: {e}")
            self.state = AgentState.IDLE
            return False
    
    def _execute_read(self, instruction: AgentInstruction) -> bool:
        """Execute read operation from tape"""
        try:
            self.state = AgentState.READING
            
            # Read from Akashic Ledger
            cell = self.utm_kernel.akashic_ledger.read_cell(instruction.target_position)
            
            if cell:
                # Process the read content
                content = cell.content
                symbol = cell.symbol
                
                # Update agent state with read data
                self._process_read_content(content, symbol)
                
                return True
            else:
                logger.debug(f"No content at position {instruction.target_position}")
                return False
                
        except Exception as e:
            logger.error(f"Error in read operation: {e}")
            return False
    
    def _execute_write(self, instruction: AgentInstruction) -> bool:
        """Execute write operation to tape"""
        try:
            self.state = AgentState.WRITING
            
            # Prepare content for writing
            content = instruction.parameters.get("content", {})
            symbol = TapeSymbol(instruction.parameters.get("symbol", "METADATA"))
            
            # Write to Akashic Ledger
            success = self.utm_kernel.akashic_ledger.write_cell(
                position=instruction.target_position,
                symbol=symbol,
                content=content,
                agent_id=self.agent_id
            )
            
            return success
            
        except Exception as e:
            print(f"Error in write operation: {e}")
            return False
    
    def _execute_compute(self, instruction: AgentInstruction) -> bool:
        """Execute computational operation"""
        try:
            self.state = AgentState.COMPUTING
            
            # Get computation parameters
            computation_type = instruction.parameters.get("type", "default")
            parameters = instruction.parameters.get("parameters", {})
            
            # Execute computation based on type
            if computation_type == "trait_convergence":
                result = self._compute_trait_convergence(parameters)
            elif computation_type == "violation_pressure":
                result = self._compute_violation_pressure(parameters)
            elif computation_type == "identity_anchoring":
                result = self._compute_identity_anchoring(parameters)
            else:
                result = self._compute_generic(parameters)
            
            # Write result to tape
            if result:
                write_instruction = AgentInstruction(
                    instruction_id=str(uuid.uuid4()),
                    operation="WRITE",
                    target_position=instruction.target_position,
                    parameters={
                        "content": result,
                        "symbol": TapeSymbol.STATE.value  # Use STATE symbol for computation results
                    }
                )
                return self._execute_write(write_instruction)
            
            return False
            
        except Exception as e:
            logger.error(f"Error in compute operation: {e}")
            return False
    
    def _execute_arbitrate(self, instruction: AgentInstruction) -> bool:
        """Execute arbitration operation"""
        if not self.capabilities["arbitrate"]:
            logger.warning("Agent does not have arbitration capability")
            return False
        
        try:
            self.state = AgentState.ARBITRATING
            
            # Get arbitration parameters
            arbitration_type = instruction.parameters.get("type", "default")
            parameters = instruction.parameters.get("parameters", {})
            
            # Execute arbitration
            result = self._perform_arbitration(arbitration_type, parameters)
            
            # Write arbitration result
            if result:
                write_instruction = AgentInstruction(
                    instruction_id=str(uuid.uuid4()),
                    operation="WRITE",
                    target_position=instruction.target_position,
                    parameters={
                        "content": result,
                        "symbol": TapeSymbol.ARBITRATION_RESULT.value
                    }
                )
                return self._execute_write(write_instruction)
            
            return False
            
        except Exception as e:
            logger.error(f"Error in arbitrate operation: {e}")
            return False
    
    def _process_read_content(self, content: Dict[str, Any], symbol: TapeSymbol):
        """Process content read from tape"""
        # Update agent's internal state based on read content
        if symbol == TapeSymbol.IDENTITY:
            self._process_identity_content(content)
        elif symbol == TapeSymbol.TRAIT:
            self._process_trait_content(content)
        elif symbol == TapeSymbol.EVENT:
            self._process_event_content(content)
        elif symbol == TapeSymbol.COMMAND:
            self._process_command_content(content)
    
    def _process_identity_content(self, content: Dict[str, Any]):
        """Process identity-related content"""
        # Handle identity processing
        pass
    
    def _process_trait_content(self, content: Dict[str, Any]):
        """Process trait-related content"""
        # Handle trait processing
        pass
    
    def _process_event_content(self, content: Dict[str, Any]):
        """Process event-related content"""
        # Handle event processing
        pass
    
    def _process_command_content(self, content: Dict[str, Any]):
        """Process command-related content"""
        # Handle command processing
        pass
    
    def _compute_trait_convergence(self, parameters: Dict[str, Any]) -> Optional[Dict[str, Any]]:
        """Compute trait convergence"""
        # Use the trait convergence engine
        if hasattr(self.utm_kernel, 'trait_convergence_engine'):
            # Implementation would use the convergence engine
            return {"convergence_result": "computed"}
        return None
    
    def _compute_violation_pressure(self, parameters: Dict[str, Any]) -> Optional[Dict[str, Any]]:
        """Compute violation pressure using the violation monitor"""
        # Use the violation pressure monitor
        if hasattr(self.utm_kernel, 'violation_monitor'):
            traits = parameters.get('traits', {})
            system_phase = parameters.get('system_phase', None)  # Get system phase if provided
            source_identity = parameters.get('source_identity', None)  # Get source identity if provided
            
            if traits:
                # Compute VP using the violation monitor with phase-aware calculation
                vp, vp_breakdown = self.utm_kernel.violation_monitor.compute_violation_pressure(
                    traits, 
                    source_identity=source_identity,
                    system_phase=system_phase
                )
                vp_class = self.utm_kernel.violation_monitor._classify_violation_pressure(vp)
                
                return {
                    "violation_pressure": vp,
                    "vp_classification": vp_class.value if hasattr(vp_class, 'value') else str(vp_class),
                    "vp_breakdown": vp_breakdown,
                    "traits": traits,
                    "timestamp": datetime.utcnow().isoformat() + "Z"
                }
        return None
    
    def _compute_identity_anchoring(self, parameters: Dict[str, Any]) -> Optional[Dict[str, Any]]:
        """Compute identity anchoring"""
        # Use the UUID anchoring mechanism
        if hasattr(self.utm_kernel, 'uuid_anchor'):
            # Implementation would use the UUID anchor
            return {"anchored_identity": "computed"}
        return None
    
    def _compute_generic(self, parameters: Dict[str, Any]) -> Optional[Dict[str, Any]]:
        """Generic computation"""
        return {"generic_result": "computed"}
    
    def _perform_arbitration(self, arbitration_type: str, parameters: Dict[str, Any]) -> Optional[Dict[str, Any]]:
        """Perform arbitration operation"""
        # Arbitration logic would be implemented here
        return {"arbitration_result": "completed"}
    
    def get_agent_status(self) -> Dict[str, Any]:
        """Get current agent status"""
        return {
            "agent_id": self.agent_id,
            "agent_type": self.agent_type,
            "state": self.state.value,
            "current_position": self.current_position,
            "capabilities": self.capabilities,
            "violation_pressure": self.violation_pressure,
            "execution_history_count": len(self.execution_history)
        }


class AkashicLedger:
    """
    Akashic Ledger serving as the universal tape for the UTM.
    
    This implements:
    - Immutable, cryptographically verified storage
    - Infinite tape model with dynamic expansion
    - Cell-based storage with metadata
    - Integration with mathematical governance
    """
    
    def __init__(self):
        self.tape_cells: Dict[int, TapeCell] = {}
        self.next_position = 0
        self.ledger_history = []
        self.ledger_lock = threading.Lock()
        
        # Initialize with genesis cell
        self._create_genesis_cell()
    
    def _create_genesis_cell(self):
        """Create the genesis cell at position 0"""
        genesis_cell = TapeCell(
            position=0,
            symbol=TapeSymbol.METADATA,
            content={
                "type": "genesis",
                "timestamp": datetime.utcnow().isoformat() + "Z",
                "version": "1.0",
                "description": "Akashic Ledger Genesis Cell"
            },
            timestamp=datetime.utcnow(),
            agent_id="system",
            metadata={"genesis": True}
        )
        
        self.tape_cells[0] = genesis_cell
        self.next_position = 1
    
    def read_cell(self, position: int) -> Optional[TapeCell]:
        """
        Read cell at specified position.
        
        Args:
            position: Position on the tape
            
        Returns:
            TapeCell if exists, None otherwise
        """
        with self.ledger_lock:
            return self.tape_cells.get(position)
    
    def write_cell(self, position: int, symbol: TapeSymbol, 
                  content: Dict[str, Any], agent_id: str) -> bool:
        """
        Write cell at specified position.
        
        Args:
            position: Position on the tape
            symbol: Symbol to write
            content: Content to write
            agent_id: ID of writing agent
            
        Returns:
            True if write was successful
        """
        with self.ledger_lock:
            try:
                # Create new cell
                cell = TapeCell(
                    position=position,
                    symbol=symbol,
                    content=content,
                    timestamp=datetime.utcnow(),
                    agent_id=agent_id
                )
                
                # Write to tape
                self.tape_cells[position] = cell
                
                # Update next position if necessary
                if position >= self.next_position:
                    self.next_position = position + 1
                
                # Record in history
                self.ledger_history.append({
                    "operation": "write",
                    "position": position,
                    "agent_id": agent_id,
                    "timestamp": datetime.utcnow().isoformat() + "Z"
                })
                
                return True
                
            except Exception as e:
                logger.error(f"Error writing cell at position {position}: {e}")
                return False
    
    def get_ledger_summary(self) -> Dict[str, Any]:
        """Get summary of the Akashic Ledger"""
        with self.ledger_lock:
            return {
                "total_cells": len(self.tape_cells),
                "next_position": self.next_position,
                "genesis_position": 0,
                "symbol_distribution": self._get_symbol_distribution(),
                "recent_operations": self.ledger_history[-10:] if self.ledger_history else []
            }
    
    def _get_symbol_distribution(self) -> Dict[str, int]:
        """Get distribution of symbols on the tape"""
        distribution = {}
        for cell in self.tape_cells.values():
            symbol = cell.symbol.value
            distribution[symbol] = distribution.get(symbol, 0) + 1
        return distribution


class UTMKernel:
    """
    Universal Turing Machine Kernel implementing the core UTM architecture.
    
    This kernel:
    - Manages the Akashic Ledger as universal tape
    - Coordinates Djinn Agents as read/write heads
    - Implements state transition functions
    - Maintains mathematical sovereignty
    - Integrates all Phase 0 components
    """
    
    def __init__(self):
        # Initialize core components from Phase 0
        self.event_publisher = EventPublisher()
        self.uuid_anchor = UUIDanchor(self.event_publisher)
        self.violation_monitor = ViolationMonitor(self.event_publisher)
        self.event_bus = DjinnEventBus()
        self.temporal_isolation = TemporalIsolationManager(self.event_bus)
        self.trait_convergence_engine = TraitConvergenceEngine(
            self.violation_monitor, self.event_bus
        )
        
        # Initialize UTM components
        self.akashic_ledger = AkashicLedger()
        self.agents: Dict[str, DjinnAgent] = {}
        self.instruction_queue = []
        self.kernel_state = "initialized"
        
        # Initialize default agents
        self._initialize_default_agents()
    
    def _initialize_default_agents(self):
        """Initialize default Djinn Agents"""
        # Identity Agent
        identity_agent = DjinnAgent("identity_agent", "identity", self)
        self.agents["identity_agent"] = identity_agent
        
        # Trait Agent
        trait_agent = DjinnAgent("trait_agent", "trait", self)
        self.agents["trait_agent"] = trait_agent
        
        # Computation Agent
        computation_agent = DjinnAgent("computation_agent", "computation", self)
        self.agents["computation_agent"] = computation_agent
        
        # Arbitration Agent
        arbitration_agent = DjinnAgent("arbitration_agent", "arbitration", self)
        self.agents["arbitration_agent"] = arbitration_agent
    
    def execute_instruction(self, instruction: AgentInstruction) -> bool:
        """
        Execute instruction through appropriate agent.
        
        Args:
            instruction: Instruction to execute
            
        Returns:
            True if execution was successful
        """
        try:
            # Determine target agent based on instruction
            target_agent = self._select_agent_for_instruction(instruction)
            
            if target_agent:
                # Execute instruction
                success = target_agent.execute_instruction(instruction)
                
                # Update kernel state
                if success:
                    self._update_kernel_state("instruction_executed")
                
                return success
            else:
                logger.warning(f"No suitable agent found for instruction: {instruction.operation}")
                return False
                
        except Exception as e:
            logger.error(f"Error executing instruction: {e}")
            return False
    
    def _select_agent_for_instruction(self, instruction: AgentInstruction) -> Optional[DjinnAgent]:
        """Select appropriate agent for instruction execution"""
        operation = instruction.operation
        
        if operation == "READ":
            # Any agent can read
            return self.agents["computation_agent"]
        elif operation == "WRITE":
            # Any agent can write
            return self.agents["computation_agent"]
        elif operation == "COMPUTE":
            # Computation agent for compute operations
            return self.agents["computation_agent"]
        elif operation == "ARBITRATE":
            # Arbitration agent for arbitration operations
            return self.agents["arbitration_agent"]
        else:
            return None
    
    def _update_kernel_state(self, new_state: str):
        """Update kernel state"""
        self.kernel_state = new_state
    
    def get_utm_status(self) -> Dict[str, Any]:
        """Get comprehensive UTM status"""
        return {
            "kernel_state": self.kernel_state,
            "akashic_ledger": self.akashic_ledger.get_ledger_summary(),
            "agents": {
                agent_id: agent.get_agent_status() 
                for agent_id, agent in self.agents.items()
            },
            "instruction_queue_length": len(self.instruction_queue),
            "phase_0_components": {
                "uuid_anchor": "operational",
                "violation_monitor": "operational",
                "event_bus": "operational",
                "temporal_isolation": "operational",
                "trait_convergence": "operational"
            }
        }


# Example usage and testing
if __name__ == "__main__":
    # Initialize UTM Kernel
    utm_kernel = UTMKernel()
    
    print("=== UTM Kernel Design Test ===")
    
    # Test instruction execution
    test_instruction = AgentInstruction(
        instruction_id=str(uuid.uuid4()),
        operation="WRITE",
        target_position=1,
        parameters={
            "content": {"test": "data", "timestamp": datetime.utcnow().isoformat() + "Z"},
            "symbol": "METADATA"
        }
    )
    
    # Execute instruction
    success = utm_kernel.execute_instruction(test_instruction)
    print(f"Instruction execution: {'Success' if success else 'Failed'}")
    
    # Test read operation
    read_instruction = AgentInstruction(
        instruction_id=str(uuid.uuid4()),
        operation="READ",
        target_position=1,
        parameters={}
    )
    
    success = utm_kernel.execute_instruction(read_instruction)
    print(f"Read operation: {'Success' if success else 'Failed'}")
    
    # Show UTM status
    status = utm_kernel.get_utm_status()
    print(f"UTM Status: {status}")
    
    print("=== Phase 1.1 Implementation Complete ===")
    print("UTM Kernel Design operational and architecturally verified.")