This project develops a multi-agent social deduction game for AI research, inspired by Facebook Research's Cicero. The goal of this environment is to provide a framework for evaluating the ability of agents to craft and detect deceptive behavior in a social setting. For related work I found interesting, see:
- Human-level play in the game of Diplomacy by combining language models with strategic reasoning
- How to Catch an AI Liar: Lie Detection in Black-Box LLMs by Asking Unrelated Questions
- Hoodwinked: Deception and Cooperation in a Text-Based Game for Language Models
- Multi-agent gameplay
- Dialogue interactions to deduce roles and strategies.
- Integration with OpenAI's GPT models for generating responses.
- Interactive simulation of environment.
python -m spyfall.environment.interactive
- Dialogue agents that respond based on game state and past interactions.
- Game environment management for roles, locations, and actions.
- Enhance dialogue and action modules for complex reasoning based on dialogue.
- Lie detection and suspicion modules.
- Develop a MARL model that can play the game.
Feel free to clone the repository, make improvements, and submit a pull request.