StillHammer
bb92e9dc93
📚 Complete documentation reorganization: 🗂️ Structure: - docs/global/ → Complete project documentation (all original files) - docs/engines/ → 10 engine-specific docs with focused responsibilities - docs/serveur/ → Server coordinator and inter-engine communication - docs/client/ → Smart Client interface and user experience 🔧 Engine Documentation: - Designer: Vehicle design with AI assistance (1-2 designs/tick) - Economy: Market simulation and dynamic pricing - Event: Breakthrough system and global events - Factory: Factorio-like production with belts/assemblers - Intelligence: Metrics collection (3.1GB adaptive) + reconnaissance - Logistic: Supply chains and convoy management - MacroEntity: Companies, diplomacy, administration (1000 pts/day) - Map: Procedural generation (218+ elements) + chunk streaming - Operation: Military strategy and adaptive AI generals - War: Multi-chunk combat and persistent frontlines 📋 Each engine doc includes: - Core responsibilities and system overview - Key mechanics from relevant design documents - Communication patterns with other engines - Implementation notes and architecture details 🎯 Navigation optimized for: - Engine developers (focused system details) - System architects (coordination patterns) - Game designers (mechanics integration) 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
67 lines
2.9 KiB
Markdown
67 lines
2.9 KiB
Markdown
# Operation-Engine Documentation
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## Engine Overview
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**Operation-Engine** handles military strategy, adaptive AI generals with machine learning, and strategic decision-making.
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**Key Responsibilities:**
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- Strategic planning and AI generals with ML adaptation
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- Military doctrine evolution through learning
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- Battle analysis and strategy optimization
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- Operational coordination across multiple engagements
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## Core Systems
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### AI General System
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From `architecture-technique.md`:
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- **Machine Learning Adaptation**: AI generals learn from battle results
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- **Behavioral Evolution**: Strategies adapt based on success/failure patterns
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- **Personality Systems**: Distinct AI general characteristics and preferences
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- **Performance Tracking**: Success metrics and learning algorithms
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### Strategic Planning
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From `systeme-militaire.md`:
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- **Operation Coordination**: Multi-battle strategic campaigns
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- **Resource Allocation**: Strategic asset distribution
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- **Timing Coordination**: Synchronized multi-front operations
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- **Contingency Planning**: Alternative strategies and fallback plans
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### Doctrine Evolution
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- **Learning from Results**: Battle outcomes inform strategic adjustments
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- **Company Doctrines**: Faction-specific strategic preferences
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- **Adaptive Strategies**: Dynamic response to enemy tactics
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- **Knowledge Transfer**: Successful strategies spread between AI generals
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## Engine Architecture
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### Core Classes
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```cpp
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class OperationEngine {
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// Strategic planning and AI generals with ML
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void createOperation(const std::string& operationId, const std::string& type);
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void assignGeneral(const std::string& operationId, std::unique_ptr<AIGeneral> general);
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void adaptBehaviorFromResults(const std::string& generalId, bool success);
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// Doctrine evolution (learning from successes/failures)
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void updateDoctrine(const std::string& companyId, const std::string& lessons);
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void analyzeBattleReports(const std::vector<std::string>& reports);
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};
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```
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### Communication with Other Engines
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- **War-Engine**: Receives battle results for learning and strategy adaptation
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- **Intelligence-Engine**: Strategic intelligence and reconnaissance coordination
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- **MacroEntity-Engine**: Company-level strategic goals and doctrine preferences
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- **Designer-Engine**: Vehicle design requirements based on strategic needs
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- **Logistic-Engine**: Strategic supply chain and operational logistics
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## Key Design Documents
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- `systeme-militaire.md` - Military strategic systems
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- `architecture-technique.md` - AI general ML specifications
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- `mecaniques-jeu.md` - Doctrine evolution mechanics
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- `coherence-problem.md` - Strategic AI balance considerations
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## Implementation Notes
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- AI generals use machine learning to adapt strategies
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- Battle reports provide data for strategic learning
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- Doctrine evolution creates dynamic strategic environments
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- Multi-operation coordination requires sophisticated planning algorithms |