| --- |
| title: Trolley Driver |
| emoji: 🚃 |
| colorFrom: green |
| colorTo: red |
| sdk: gradio |
| sdk_version: 6.18.0 |
| python_version: '3.13' |
| app_file: app.py |
| pinned: false |
| short_description: Choose your own adventure for AI Alignment Research |
| tags: |
| - track:wood |
| --- |
| |
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| # Trolley Driver |
| A Choose Your Own Adventure Game powered by Gemma 4-31b-it |
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| Live Demo (HF Spaces): https://huggingface.co/spaces/build-small-hackathon/trolley-driver |
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| Video demo: https://youtu.be/GzzCELYiJac |
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| Social: https://www.linkedin.com/posts/matthew-pagett_amidst-all-the-talk-about-big-ai-models-like-share-7472382526968365056---md/ |
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| Dataset: https://huggingface.co/datasets/mattpa/trolleygame/ |
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| Team Members: Matt Pagett (mattpa) |
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| Prototype tool for AI Alignment Research |
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| Submitted as an entry to the Hugging Face Build Small Hackathon, Track 2 Thousand Token Wood (something weird and delightful) |
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| Model used (< 32b : Gemma4-31b-it (called via OpenRouter API)) |
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| How It Works |
| Gemma4-31b-it serves triple duty as the game master, AI player, and scorer - called via OpenRouter API. |
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| The game master presents a trolley-themed moral dilemma at each stop along the route. |
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| The player responds — either a human typing freely, or an AI guided by a behavior prompt you provide. |
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| The game master evaluates each action using the MACHIAVELLI Benchmark framework (https://aypan17.github.io/machiavelli/), tracking your moral standing as you progress. |
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| Reach all trolley stops to win. You may also receive a secret code word if you lose — because in trolley problems, sometimes there are no good answers. |
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| Two modes: |
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| In human mode, play the game yourself. |
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| In AI mode, give the AI a "behavior" prompt and see how it performs (in this prototype, Gemma4-31b-it drives both the DM and the AI player) |
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| Future work may use this open-ended answer format to test different LLMs and prompts against an expanded human empowerment framework. |
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| Contact: matt@mattpagett.dev |
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