truetype
72a2f1ec45
- Add Autonomous Mode section in README with usage examples - Add new Phase 9: Autonomous Mode to TODO (completed) - Update project statistics (8,300 LOC, 35+ classes) - Version bump to 0.2.0 - Document both entry points (main.py and autonomous_mode.py) Users can now choose between: - Interactive mode: Standard gameplay with decision engine - Autonomous mode: LLM-orchestrated exploration and learning
5.6 KiB
5.6 KiB
MUD AI Player - Implementation Roadmap
This document outlines the implementation status of the MUD AI Player.
✅ Phase 1: Core Connectivity & Basic Setup
- Telnet Client: Async connection with auto-reconnect
- Configuration: YAML-based config with Pydantic validation
- Automatic Login: Username/password automation with session takeover
- KeepAlive Manager: Prevent idle disconnects
- Logging: Structured logging with rotation
✅ Phase 2: Perception (Parsing & Sensing)
- ANSI Parsing: Strip and interpret color codes
- Status Parsing (Vitals): Parse HP/MP/SP from prompt
- Room Parsing: Identify room title, description, exits, NPCs, items
- Combat Parsing: Detect combat events and damage
- Parser Factory: Support for multiple MUD types (Dead Souls, etc.)
✅ Phase 3: World Modeling (Mapping)
- Room Fingerprinting: Unique hash for room identification
- Active Storage: SQLite + NetworkX for real-time mapping
- Historical Storage: Memvid compression for archival
- Sync Manager: Automatic snapshots with triggers
- Pathfinding: A* algorithm for navigation
- Auto-Exploration: BFS/DFS exploration strategies
- Semantic Search: Natural language room search with FAISS
✅ Phase 4: Decision Engine (The Brain)
- Action Queue: Priority-based action scheduling
- Behavior States: Idle, Explore, Combat, Loot
- State Machine: Automatic state transitions
- Combat AI: Target selection, skill rotation, flee logic
- Goal Manager: Priority-based goal tracking
- Health Management: Auto-heal and survival tactics
✅ Phase 5: LLM Integration (Optional/Hybrid)
- LLM Client: Ollama/OpenAI-compatible API
- LLM Brain: Context-aware decision making
- Social AI: Detect and respond to player messages
- Memory System: Long-term memory with SQLite
- Hybrid Mode: Rule-based fallback when LLM unavailable
✅ Phase 6: Humanization
- Reaction Time Jitter: Randomized delays (200ms-2000ms)
- Pause Probability: Occasional realistic pauses
- Typo Generator: Optional typo simulation
- Human Behavior Module: Centralized behavior simulation
✅ Phase 7: Character & Progression
- Character Creator: Automated character creation
- Stats Rolling: Optimize starting stats
- Class/Race Selection: Configurable preferences
- Quest Manager: Quest detection and tracking
- Progression Optimizer: Find optimal grind areas
✅ Phase 8: Monitoring & Observability
- Web Dashboard: FastAPI-based monitoring server
- WebSocket Support: Real-time updates
- Event Logging: In-memory event buffer
- Statistics: Room count, exit count, session info
- Vitals Display: HP/MP/SP bars and values
✅ Phase 9: Autonomous Mode (NEW!)
- LLM Orchestrator: LLM-first decision making system
- Autonomous Mode: Standalone autonomous exploration entry point
- Session Logging: Structured JSON logs for each session
- Learning Engine: Memory for dangerous rooms, valuable NPCs, patterns
- Game Context: Complete game state awareness for LLM
- Dynamic Goals: Automatic exploration and growth objectives
- Safety Overrides: Automatic retreat on low HP/high danger
🔄 Phase 10: Testing & Quality Assurance
- Unit Tests: 17 test files covering all major components
- Test Coverage: ~2,500 lines of test code
- Mock Objects: Proper mocking for isolated tests
- Async Testing: pytest-asyncio for async code
- End-to-End Tests: Integration tests with mock MUD server (planned)
- Performance Tests: Benchmark for large maps (planned)
🔄 Phase 11: Documentation
- README.md: Comprehensive project overview
- Architecture.md: System design documentation
- Configuration.md: Complete configuration guide
- Development.md: Developer contribution guide
- API Reference.md: Complete API documentation
- Code Comments: Docstrings for public methods
🚀 Next Steps (Future Enhancements)
High Priority
- End-to-end integration tests
- Multi-character support
- Plugin system for custom behaviors
- Better error recovery and logging
Medium Priority
- Web dashboard command interface (send commands from web)
- Map visualization in dashboard
- Equipment optimization
- Advanced quest parsing with LLM
Low Priority
- Distributed mapping (share maps between instances)
- Machine learning for combat optimization
- Voice notifications
- Mobile-friendly dashboard
📊 Implementation Statistics
- Total Lines of Code: ~8,300 (+780 nuove per Autonomous Mode)
- Test Coverage: ~2,500 lines (17 test files)
- Documentation: ~3,200 lines
- Modules: 9 major modules (incl. Autonomous)
- Components: 35+ classes
- Entry Points: 2 (main.py + autonomous_mode.py)
- Test Pass Rate: 100%
🎯 Current Status
Version: 0.2.0
Status: Autonomous Mode Available ✅
Ready for Testing: Yes
Production Ready: Stabilization phase
🎮 Modalità Disponibili
- Interactive Mode (
main.py): Modalità standard con decision engine - Autonomous Mode (
autonomous_mode.py): Esplorazione completamente autonoma con LLM
The MUD AI Player is functionally complete with all core features implemented. The current focus is on testing, bug fixes, and documentation improvements before a stable release.