待翻譯：Building an LLM Agent to Play Dwarf Fortress

AI 服務暫時不可用，以下為來源正文，待恢復後補全翻譯。

Building an LLM Agent to Play Dwarf Fortress Published:Feb 28, 2026 | at 06:00 PM Table of contents Open Table of contents Why Dwarf Fortress The Architecture Phase 1 Running What’s Next Why Dwarf Fortress People say Dwarf Fortress is the most complex game ever made. That’s not why I picked it. I picked it because it’s abstract. NetHack, DF — they’re structured like Go. A small ruleset that produces near-infinite emergent complexity. Go is a 19×19 board with simple capture rules. DF is ASCII characters on a terminal, a few dozen DFHack commands, but forgetting to wall off an underground river can end your fortress in minutes. That structure — simple rules, enormous state space — is actually ideal for AI: State is structured, not pixels. Text output and data files. An LLM can read it directly without a vision model Action space is discrete. A defined set of commands (prospect, quickfort run, dig-now, showmood…) Feedback is unambiguous. Dwarves are dead or alive. Food count is a number. Whether a room was dug is a filesystem check No memorizable optimal strategy. Every world is procedurally generated. The agent has to actually understand the situation Modern 3D games are paradoxically harder to build agents for — you deal with pixels, frame rates, occlusion. DF’s ASCII interface is an accidental advantage. The Architecture Core idea: never touch the game UI. Talk to DFHack only. Four layers: Knowledge Layer — DF strategy knowledge + dreamfort blueprint library + experience logs, distilled into phase-specific context injected into the LLM prompt. Decision Layer (LLM) — receives current state + phase knowledge + action history, outputs a structured Action JSON. Action types: dfhack (quickfort/dig-now/build-now), keystroke (UI navigation), query (Lua state probe), wait, done. Execution Layer — runs dfhack-run / xdotool / Lua, writes a JSONL log after each step, refreshes state. Feedback Layer — primary: DFHack Lua queries (units, buildings, items, map, prospect). Fallback: scrot screenshot + Vision API when commands can’t determine state. Event: gamelog.txt real-time monitoring — combat/death/mood/season changes trigger immediate response. Key implementation decisions: TEXT mode on VPS. DF has PRINT_MODE:TEXT — renders as ncurses TUI, no Xvfb, runs headless in a PTY. dfhack-run command pipe, not Lua RPC. The Lua RPC channel is unstable headless. dfhack-run command pipe works reliably. quickfort with --cursor. To dig a room, no menu navigation needed. quickfort run blueprint.csv --cursor x,y,z. The AI needs coordinates, not UI skills. Filesystem as state oracle. Is the fortress running? Count data/save/region*/region_snapshot-*.dat. How many ticks? The filenames encode it. No internal queries needed for basic state. Phase 1 Running Phase 1 goal: survival basics — food, drink, shelter. The plan runs dreamfort blueprints in sequence: setup → surface clearing → dig farming level → place workshops → farm plots + dining room → unpause → verify. The agent runs, reads state, reasons through it. One interesting debugging moment — tracking DF’s time system: cur_year_tick is the year-wide tick counter. cur_season_tick is within-season. A season is ~33600 ticks (1200 ticks/day × 28 days). The agent figures out it’s still spring mid-season and needs to advance to summer for crops to ripen. Game was running slow on the headless setup — used DFHack’s timestream to accelerate. Then Phase 1 completes: Buildings went from 1 → 45 (farming2) → 68 (farming3). 23 suspended buildings are normal — buildingplan waiting on materials not yet produced. Plan complete. What’s Next Mid-term: phase-based goals continuing from here. Phase 2: production chain (farming → brewing → woodworking → smelting) Phase 3: defense (corridors, traps, military) Each phase has its own goal function and verifiable success condition. Long-term: cross-session memory. Fortress failed → record why → inject context on next load. DF’s gamelog.txt logs every event from the beginning of the fortress. That’s a natural episode memory. The LLM becomes not just a planner for the current situation, but an experience accumulator across runs. The game is designed around losing. Each failure is signal. Repo: zerone0x/df-ai-exp ai agent game tech Share this post on: Share this post via WhatsApp Share this post on Facebook Tweet this post Share this post via Telegram Share this post on Pinterest Share this post via email