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Emergence of Social Norms in Generative Agent Societies: Principles and Architecture
We are happy to announce that CRSEC has been accepted to HAI Special Track at IJCAI 2024!
This repository is associated with our research paper titled "Emergence of Social Norms in Generative Agent Societies: Principles and Architecture" In this paper, we propose a novel agent architecture named CRSEC, consisting of four modules: Creation & Representation, Spreading, Evaluation, and Compliance, designed to facilitate the emergence of social norms within a generative agent society. This repository includes all four modules designed in our architecture and provides a replay for better understanding. We offer instructions for setting up the simulation environment on your local machine and for replaying the simulation as a demonstration animation. To access the full version of the demo, please refer to this link. For supplementary materials, please refer to the following arXiv link.
Setting Up the Environment
To set up your environment, you will need to generate a utils.py file that contains your OpenAI API key and download the necessary packages.
Step 1. Generate Utils File
In the reverie/backend_server folder (where reverie.py is located), create a new file titled utils.py and copy and paste the content below into the file:
# Copy and paste your OpenAI API Key
openai_api_key = "<Your OpenAI API>"
# Put your name
key_owner = "<Name>"
maze_assets_loc = "../../environment/frontend_server/static_dirs/assets"
env_matrix = f"{maze_assets_loc}/the_ville/matrix"
env_visuals = f"{maze_assets_loc}/the_ville/visuals"
fs_storage = "../../environment/frontend_server/storage"
fs_temp_storage = "../../environment/frontend_server/temp_storage"
collision_block_id = "32125"
# Verbose
debug = True
Replace <Your OpenAI API> with your OpenAI API key, and <name> with your name.
Step 2. Install requirements.txt
Install everything listed in the requirements.txt file (I strongly recommend first setting up a virtualenv as usual). A note on Python version: we tested our environment on Python 3.9.7.
Running a Simulation
To run a new simulation, you will need to concurrently start two servers: the environment server and the agent simulation server.
Step 1. Starting the Environment Server
Again, the environment is implemented as a Django project, and as such, you will need to start the Django server. To do this, first navigate to environment/frontend_server (this is where manage.py is located) in your command line. Then run the following command:
python manage.py runserver
Then, on your favorite browser, go to http://localhost:8000/. If you see a message that says, "Your environment server is up and running," your server is running properly. Ensure that the environment server continues to run while you are running the simulation, so keep this command-line tab open!
Step 2. Starting the Simulation Server
Open up another command line (the one you used in Step 1 should still be running the environment server, so leave that as it is). Navigate to reverie/backend_server and run reverie.py.
python reverie.py
This will start the simulation server. A command-line prompt will appear, asking the following: "Enter the name of the forked simulation: ". To start a 10-agent simulation without any initial norm, type the following:
base_the_ville_n10
The prompt will then ask, "Enter the name of the new simulation: ". Type any name to denote your current simulation (e.g., just "test-simulation" will do for now).
test-simulation
Initialization will then started.
Step 3. Creating norms for entrepreneurs
After the initialization, the prompt will ask the following: "Regenerate norms? (y or n): ". To generate norms for an norm entrepreneur, type "y", otherwise, type "n".
# generate norms
y
# otherwise
n
If you chose "y" in the last step, the prompt will then ask, "Enter the name of the entrepreneur: ". Type a name of an agent who's identify is "entrepreneur". In our setup, there are 3 norm entrepreneurs: Abigail Chen, Bob Johnson and Francisco Lopez. Type the name of any one of them to complete a generation round.
# e.g.,
Abigail Chen
After completing norm generation, it will prompt "Regenerate norms? (y or n):" again. Type "y" to either generate norms for another entrepreneur or redo the generation for the current agent. When all norm generation is complete, type"n" to end this stage.
Then it will display the following prompt: "Enter option: "
Step 4. Running and Saving the Simulation
On your browser, navigate to http://localhost:8000/simulator_home. You should see the map of Smallville, along with a list of active agents on the map. You can move around the map using your keyboard arrows. Please keep this tab open. To run the simulation, type the following command in your simulation server in response to the prompt, "Enter option":
run <step-count>
Note that you will want to replace <step-count> above with an integer indicating the number of game steps you want to simulate. For instance, if you want to simulate 100 game steps, you should input run 100. One game step represents 10 seconds in the game.
Your simulation should be running, and you will see the agents moving on the map in your browser. Once the simulation finishes running, the "Enter option" prompt will re-appear. At this point, you can simulate more steps by re-entering the run command with your desired game steps, exit the simulation without saving by typing exit, or save and exit by typing fin.
The saved simulation can be accessed the next time you run the simulation server by providing the name of your simulation as the forked simulation. This will allow you to restart your simulation from the point where you left off.
Step 5. Replaying a Simulation
You can replay a simulation that you have already run simply by having your environment server running and navigating to the following address in your browser: http://localhost:8000/replay/<simulation-name>/<starting-time-step>. Please make sure to replace <simulation-name> with the name of the simulation you want to replay, and <starting-time-step> with the integer time-step from which you wish to start the replay.
For instance, by visiting the following link, you will initiate a pre-simulated example, starting at time-step 1: http://localhost:8000/replay/the_ville_n10_with_norm_day_1_19/1/
Step 5. Demoing a Simulation
You may have noticed that all character sprites in the replay look identical. We would like to clarify that the replay function is primarily intended for debugging purposes and does not prioritize optimizing the size of the simulation folder or the visuals. To properly demonstrate a simulation with appropriate character sprites, you will need to compress the simulation first. To do this, open the compress_sim_storage_norm.py file located in the reverie/backend_server directory using a text editor. Then, execute the compress function with the name of the target simulation as its input. By doing so, the simulation file will be compressed, making it ready for demonstration.
To start the demo, go to the following address on your browser: http://localhost:8000/demo/<simulation-name>/<starting-time-step>/<simulation-speed>. Note that <simulation-name> and <starting-time-step> denote the same things as mentioned above. <simulation-speed> can be set to control the demo speed, where 1 is the slowest, and 5 is the fastest. For instance, visiting the following link will start a pre-simulated example, beginning at time-step 1, with a medium demo speed: http://localhost:8000/demo/the_ville_n10_with_norm_day_1_19/1/3/
Simulation Storage Location
All simulations that you save will be located in environment/frontend_server/storage, and all compressed demos will be located in environment/frontend_server/compressed_storage.
Authors and Citation
Authors: Siyue Ren, Zhiyao Cui, Ruiqi Song, Zhen Wang, Shuyue Hu
Please cite our paper if you use the code or data in this repository.
@inproceedings{ren2024emergence,
title={Emergence of Social Norms in Generative Agent Societies: Principles and Architecture,
author={Ren, Siyue and Cui, Zhiyao and Song, Ruiqi and Wang, Zhen and Hu, Shuyue},
booktitle={Proceedings of the 33rd International Joint Conference on Artificial Intelligence (IJCAI)},
year={2024}
}