# Operation-Engine Documentation

## Engine Overview
**Operation-Engine** handles military strategy, adaptive AI generals with machine learning, and strategic decision-making.

**Key Responsibilities:**
- Strategic planning and AI generals with ML adaptation
- Military doctrine evolution through learning
- Battle analysis and strategy optimization
- Operational coordination across multiple engagements

## Core Systems

### AI General System
From `architecture-technique.md`:
- **Machine Learning Adaptation**: AI generals learn from battle results
- **Behavioral Evolution**: Strategies adapt based on success/failure patterns
- **Personality Systems**: Distinct AI general characteristics and preferences
- **Performance Tracking**: Success metrics and learning algorithms

### Strategic Planning
From `systeme-militaire.md`:
- **Operation Coordination**: Multi-battle strategic campaigns
- **Resource Allocation**: Strategic asset distribution
- **Timing Coordination**: Synchronized multi-front operations
- **Contingency Planning**: Alternative strategies and fallback plans

### Doctrine Evolution
- **Learning from Results**: Battle outcomes inform strategic adjustments
- **Company Doctrines**: Faction-specific strategic preferences
- **Adaptive Strategies**: Dynamic response to enemy tactics
- **Knowledge Transfer**: Successful strategies spread between AI generals

## Engine Architecture

### Core Classes
```cpp
class OperationEngine {
    // Strategic planning and AI generals with ML
    void createOperation(const std::string& operationId, const std::string& type);
    void assignGeneral(const std::string& operationId, std::unique_ptr<AIGeneral> general);
    void adaptBehaviorFromResults(const std::string& generalId, bool success);

    // Doctrine evolution (learning from successes/failures)
    void updateDoctrine(const std::string& companyId, const std::string& lessons);
    void analyzeBattleReports(const std::vector<std::string>& reports);
};
```

### Communication with Other Engines
- **War-Engine**: Receives battle results for learning and strategy adaptation
- **Intelligence-Engine**: Strategic intelligence and reconnaissance coordination
- **MacroEntity-Engine**: Company-level strategic goals and doctrine preferences
- **Designer-Engine**: Vehicle design requirements based on strategic needs
- **Logistic-Engine**: Strategic supply chain and operational logistics

## Key Design Documents
- `systeme-militaire.md` - Military strategic systems
- `architecture-technique.md` - AI general ML specifications
- `mecaniques-jeu.md` - Doctrine evolution mechanics
- `coherence-problem.md` - Strategic AI balance considerations

## Implementation Notes
- AI generals use machine learning to adapt strategies
- Battle reports provide data for strategic learning
- Doctrine evolution creates dynamic strategic environments
- Multi-operation coordination requires sophisticated planning algorithms