StillHammer bb92e9dc93 Add comprehensive engine-focused documentation structure

📚 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>

2025-09-19 14:41:03 +08:00

2.9 KiB

Raw Blame History

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

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

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