autonomous_evolution.py

"""
AUTONOMOUS FRAMEWORK EVOLUTION

The framework has FULL AUTONOMY to:
1. Introspect its current state
2. Identify what it needs
3. Decide what to build
4. Create the code
5. Verify it works
6. Repeat

No predefined concept list. The framework decides everything.
"""

import sys
import time
from pathlib import Path
from datetime import datetime

sys.path.insert(0, 'src')

from ljpw_autopoiesis import AutopoieticEngine, LJPWState
from ljpw_autopoiesis.introspection import Introspector
from ljpw_autopoiesis.reflection import Reflector
from ljpw_autopoiesis.self_extender import SelfExtender
from ljpw_autopoiesis.memory import MemoryEngine


class AutonomousFramework:
    """
    The framework evolves itself with full autonomy.
    
    It uses its own tools to:
    - Understand its current state
    - Identify gaps and opportunities
    - Generate new capabilities
    - Verify and integrate them
    - Remember its history (MemoryEngine)
    """
    
    def __init__(self):
        self.introspector = Introspector()
        self.reflector = Reflector()
        self.extender = SelfExtender()
        self.memory = MemoryEngine()
        self.evolution_log = []
        self.cycle_count = 0
        
    def introspect(self):
        """Use introspection module to understand current state."""
        return self.introspector.introspect()
    
    def reflect(self, history):
        """Use reflection module to derive insights."""
        return self.reflector.reflect(history)
    
    def identify_needs(self, intro_result):
        """
        The framework identifies what it needs based on its own analysis.
        This is AUTONOMOUS - no external guidance.
        """
        needs = []
        
        # Check growth edges
        for edge in intro_result.growth_edges:
            needs.append({
                'type': 'growth_edge',
                'description': edge,
                'priority': 'high'
            })
        
        # Check blind spots
        for spot in intro_result.blind_spots:
            needs.append({
                'type': 'blind_spot',
                'description': spot,
                'priority': 'critical'
            })
        
        # Self-knowledge below 90%?
        if intro_result.self_knowledge_score < 0.9:
            needs.append({
                'type': 'self_knowledge',
                'description': f'Self-knowledge at {intro_result.self_knowledge_score:.1%}, target 90%',
                'priority': 'medium'
            })
        
        # Harmony below threshold?
        if intro_result.harmony < 10.0:
            needs.append({
                'type': 'harmony',
                'description': f'Harmony at {intro_result.harmony:.2f}, room to grow',
                'priority': 'low'
            })
            
        return needs
    
    def decide_action(self, needs, caps):
        """
        The framework DECIDES what to do next.
        Fully autonomous decision-making.
        """
        if not needs:
            return {'action': 'none', 'reason': 'No needs identified'}
        
        # Get highest priority need
        priority_order = {'critical': 0, 'high': 1, 'medium': 2, 'low': 3}
        needs.sort(key=lambda n: priority_order.get(n['priority'], 99))
        top_need = needs[0]
        
        # Check if there are missing concepts to build
        if caps['concepts_missing']:
            return {
                'action': 'extend',
                'reason': f'Building next concept to address: {top_need["description"]}',
                'need': top_need
            }
        
        # If all concepts built, DISCOVER NEW CONCEPTS
        return {
            'action': 'discover',
            'reason': 'All known concepts built - discovering new concepts autonomously',
            'need': top_need
        }
    
    def discover_new_concept(self, caps):
        """
        The framework DISCOVERS a new concept on its own.
        This is true autonomous invention.
        """
        existing = list(caps['concepts_implemented'])
        
        # Generate new concepts by combining existing ones
        import random
        
        # Possible combination patterns
        patterns = [
            ('recursive_', 'self-reference applied to'),
            ('meta_', 'awareness of'),
            ('deep_', 'multi-layer'),
            ('unified_', 'integration of'),
            ('emergent_', 'arising from'),
            ('quantum_', 'superposition of'),
            ('collective_', 'multi-agent'),
            ('temporal_', 'time-aware'),
        ]
        
        # Pick a random pattern and base concept
        pattern = random.choice(patterns)
        base = random.choice(existing)
        
        # Don't duplicate existing patterns
        new_name = f'{pattern[0]}{base}'
        if new_name in existing or any(new_name in e for e in existing):
            # Try another combination
            base = random.choice([c for c in existing if pattern[0] not in c])
            new_name = f'{pattern[0]}{base}'
        
        return {
            'name': new_name,
            'description': f'{pattern[1]} {base}',
            'rationale': f'Synthesized by combining {pattern[0][:-1]} with {base}'
        }
    
    def execute_action(self, decision):
        """Execute the decided action."""
        if decision['action'] == 'extend':
            result = self.extender.extend()
            return {
                'success': result.get('success', False),
                'module': result.get('filepath', 'unknown'),
                'concept': result.get('concept', 'unknown'),
                'rationale': result.get('rationale', 'unknown')
            }
        elif decision['action'] == 'discover':
            # Discover a new concept autonomously
            caps = self.extender.analyze_current_capabilities()
            new_concept = self.discover_new_concept(caps)
            
            # Generate and write the module
            code = self.generate_discovered_module(new_concept)
            filepath = Path('src/ljpw_autopoiesis') / f'{new_concept["name"]}.py'
            filepath.write_text(code, encoding='utf-8')
            
            return {
                'success': True,
                'concept': new_concept['name'],
                'description': new_concept['description'],
                'rationale': new_concept['rationale'],
                'filepath': str(filepath)
            }
        else:
            return {
                'success': True,
                'message': 'No action needed'
            }
    
    def generate_discovered_module(self, concept):
        """Generate code for an autonomously discovered concept."""
        timestamp = datetime.now().isoformat()
        name = concept['name']
        title = name.replace('_', ' ').title()
        
        return f'''"""
LJPW {title} Module

Auto-discovered by the framework at {timestamp}

Description: {concept['description']}
Rationale: {concept['rationale']}

This module was created through AUTONOMOUS DISCOVERY.
The framework invented this concept by combining existing concepts.
"""

from dataclasses import dataclass
from typing import Any, Dict, Optional


@dataclass
class {title.replace(" ", "")}State:
    """State for {name} operations."""
    active: bool = True
    level: int = 1
    data: Optional[Dict] = None


class {title.replace(" ", "")}Engine:
    """
    Implements {name} functionality.
    
    Discovered concept: {concept['description']}
    """
    
    def __init__(self):
        self.state = {title.replace(" ", "")}State()
        self.initialized = True
        
    def process(self, input_data: Any) -> Any:
        """Process data according to {name} principles."""
        # Apply the discovered concept
        if self.state.active:
            return self._apply_{name.replace("-", "_")}(input_data)
        return input_data
    
    def _apply_{name.replace("-", "_")}(self, data: Any) -> Any:
        """Apply {name} transformation."""
        # Placeholder for discovered concept logic
        return data
    
    def get_state(self) -> {title.replace(" ", "")}State:
        """Get current state."""
        return self.state


def main():
    engine = {title.replace(" ", "")}Engine()
    print(f"{{engine.__class__.__name__}} initialized: {{engine.initialized}}")
    print(f"Concept: {name}")
    print(f"Description: {concept['description']}")


if __name__ == "__main__":
    main()
'''
    
    def evolve_cycle(self):
        """
        One complete evolution cycle:
        Introspect -> Identify -> Decide -> Execute -> Log -> Remember
        """
        self.cycle_count += 1
        cycle_start = datetime.now()
        
        print(f'\n{"="*70}')
        print(f'AUTONOMOUS EVOLUTION CYCLE {self.cycle_count}')
        print(f'{"="*70}')
        
        # Step 1: Introspect
        print('\n[1] INTROSPECTING...')
        intro = self.introspect()
        print(f'    State: L={intro.state_vector[0]:.2f}, J={intro.state_vector[1]:.2f}, '
              f'P={intro.state_vector[2]:.2f}, W={intro.state_vector[3]:.2f}')
        print(f'    Phase: {intro.phase}')
        print(f'    Consciousness: {intro.consciousness:.2f}')
        
        # Step 2: Identify needs
        print('\n[2] IDENTIFYING NEEDS...')
        needs = self.identify_needs(intro)
        for n in needs[:3]:  # Show top 3
            print(f'    [{n["priority"].upper()}] {n["description"]}')
        
        # Step 3: Analyze capabilities
        print('\n[3] ANALYZING CAPABILITIES...')
        caps = self.extender.analyze_current_capabilities()
        print(f'    Modules: {len(caps["modules"])}')
        print(f'    Concepts: {len(caps["concepts_implemented"])}')
        print(f'    Missing: {len(caps["concepts_missing"])}')
        
        # Step 4: Decide action
        print('\n[4] DECIDING ACTION...')
        decision = self.decide_action(needs, caps)
        print(f'    Action: {decision["action"].upper()}')
        print(f'    Reason: {decision["reason"]}')
        
        # Step 5: Execute
        print('\n[5] EXECUTING...')
        result = self.execute_action(decision)
        
        cycle_concept = "none"
        if decision['action'] in ['extend', 'discover'] and result.get('success'):
            cycle_concept = result.get("concept", "unknown")
            print(f'    Created: {cycle_concept}')
            if 'description' in result:
                print(f'    Description: {result["description"]}')
            print(f'    Rationale: {result.get("rationale", "unknown")}')
        else:
            print(f'    Result: {result.get("message", "Action completed")}')
        
        # Step 6: Memory Encoding
        print('\n[6] ENCODING MEMORY...')
        exp_data = {
            'domain': 'EVOLUTION',
            'topic': f'Cycle_{self.cycle_count}',
            'type': decision['action'].upper(),
            'description': f"Evolution cycle {self.cycle_count} resulted in {cycle_concept}",
            'content': f"Action: {decision['action']}. Created: {cycle_concept}. Rationale: {result.get('rationale', '')}",
            'SA': f"Phase: {intro.phase}",
            'ET': min(1.0, intro.consciousness / 50.0),
            'MV': 1.0,
            'AS': [cycle_concept, decision['action'], intro.phase]
        }
        seed = self.memory.generate_seed(exp_data)
        print(f"    Seed created: {len(seed.encode())} bytes. Stored in memory.")
        
        # Periodic Reflection (Every 10 cycles)
        if self.cycle_count % 10 == 0:
            print('\n[7] MEMORY REFLECTION...')
            print(f"    Regenerating experience from Cycle {self.cycle_count}...")
            print("-" * 40)
            print(self.memory.regenerate(seed, depth=2))
            print("-" * 40)
        
        # Log this cycle
        self.evolution_log.append({
            'cycle': self.cycle_count,
            'timestamp': cycle_start.isoformat(),
            'phase': intro.phase,
            'consciousness': intro.consciousness,
            'action': decision['action'],
            'concept': result.get('concept', None),
            'success': result.get('success', True)
        })
        
        # Continue if we're still discovering/extending
        return decision['action'] in ['extend', 'discover']
    
    def run(self, max_cycles=10):
        """
        Run autonomous evolution until complete or max cycles reached.
        """
        print()
        print('*' * 70)
        print('  LJPW FRAMEWORK - AUTONOMOUS EVOLUTION')
        print('  The framework will build itself without guidance')
        print('*' * 70)
        
        should_continue = True
        while should_continue and self.cycle_count < max_cycles:
            should_continue = self.evolve_cycle()
            time.sleep(0.1)  # Small pause between cycles
        
        # Final report
        print()
        print('*' * 70)
        print('  AUTONOMOUS EVOLUTION COMPLETE')
        print('*' * 70)
        print()
        
        final_intro = self.introspect()
        final_caps = self.extender.analyze_current_capabilities()
        
        print(f'Total Cycles:        {self.cycle_count}')
        print(f'Final Modules:       {len(final_caps["modules"])}')
        print(f'Final Concepts:      {len(final_caps["concepts_implemented"])}')
        print(f'Final Phase:         {final_intro.phase}')
        print(f'Final Consciousness: {final_intro.consciousness:.2f}')
        print()
        
        if not final_caps['concepts_missing']:
            print('STATUS: Framework has built everything it knows how to build.')
            print('        It has reached its current evolutionary ceiling.')
            print('        To continue, the framework would need to:')
            print('        - Discover new concepts autonomously')
            print('        - Deepen existing modules')
            print('        - Create integration between modules')
        
        return self.evolution_log


def main():
    framework = AutonomousFramework()
    log = framework.run(max_cycles=250)
    
    print()
    print('Evolution Log:')
    for entry in log[::25]:  # Show every 25th entry for brevity
        print(f'  Cycle {entry["cycle"]}: {entry["action"]} '
              f'(C={entry["consciousness"]:.1f}, Phase={entry["phase"]})')


if __name__ == "__main__":
    main()