Cards Against Humanity AI

Related tags

Deep Learningcah-ai
Overview

cah-ai

This is a Cards Against Humanity AI implemented using a pre-trained Semantic Search model.

How it works

A player is described by a combination of a text description (e.g. "a college frat boy") and a personality_power (which essentially controls how much their personality description affects their actions). The players are implemented using a semantic similarity model. Prompts are encoded on a per-player basis (e.g. as "a college frat boy answered question ... as") and answers are encoded as normal. The player's choice is determined probabilistically using the semantic similarity model.

Results

I tried simulating a game using scripts/simulate_game.py. I had four virtual players: three with text descriptions, and one that takes random actions. Here are the players and their resulting number of wins, after going through the whole deck:

278 wins - DescPlayer("a college frat boy", personality_power=5.0)
232 wins - DescPlayer("a middle-aged man", personality_power=5.0)
236 wins - DescPlayer("an old racist southern lady", personality_power=5.0)
211 wins - RandomPlayer()

Interestingly, the random player is only a tiny bit worse, even though the other three players are implemented the same way and are therefore more likely to select winners amongst themselves.

Let's see what happens in a game with three duplicate AI players and one random player:

267 wins - DescPlayer("a college frat boy", personality_power=5.0)
247 wins - DescPlayer("a college frat boy", personality_power=5.0)
249 wins - DescPlayer("a college frat boy", personality_power=5.0)
194 wins - RandomPlayer()

Surprisingly, the random player's wins didn't decrease very much. Maybe it's because the players are stochastic, Let's try reducing their temperature to 0.1:

270 wins - DescPlayer("a college frat boy", personality_power=5.0, temperature=0.1)
251 wins - DescPlayer("a college frat boy", personality_power=5.0, temperature=0.1)
254 wins - DescPlayer("a college frat boy", personality_power=5.0, temperature=0.1)
182 wins - RandomPlayer()

So interestingly, even if the first three players are highly "opinionated" and all agree exactly, a random player still gets 19% of the wins (25% would be the case if all players are random).

Owner
Alex Nichol
Web developer, math geek, and AI enthusiast.
Alex Nichol
Official implementation of CATs: Cost Aggregation Transformers for Visual Correspondence NeurIPS'21

CATs: Cost Aggregation Transformers for Visual Correspondence NeurIPS'21 For more information, check out the paper on [arXiv]. Training with different

Sunghwan Hong 120 Jan 04, 2023
ARAE-Tensorflow for Discrete Sequences (Adversarially Regularized Autoencoder)

ARAE Tensorflow Code Code for the paper Adversarially Regularized Autoencoders for Generating Discrete Structures by Zhao, Kim, Zhang, Rush and LeCun

19 Nov 12, 2021
Memory-Augmented Model Predictive Control

Memory-Augmented Model Predictive Control This repository hosts the source code for the journal article "Composing MPC with LQR and Neural Networks fo

Fangyu Wu 1 Jun 19, 2022
git《Self-Attention Attribution: Interpreting Information Interactions Inside Transformer》(AAAI 2021) GitHub:

Self-Attention Attribution This repository contains the implementation for AAAI-2021 paper Self-Attention Attribution: Interpreting Information Intera

60 Dec 29, 2022
Optimizes image files by converting them to webp while also updating all references.

About Optimizes images by (re-)saving them as webp. For every file it replaced it automatically updates all references. Works on single files as well

Watermelon Wolverine 18 Dec 23, 2022
RoMA: Robust Model Adaptation for Offline Model-based Optimization

RoMA: Robust Model Adaptation for Offline Model-based Optimization Implementation of RoMA: Robust Model Adaptation for Offline Model-based Optimizatio

9 Oct 31, 2022
Semantic code search implementation using Tensorflow framework and the source code data from the CodeSearchNet project

Semantic Code Search Semantic code search implementation using Tensorflow framework and the source code data from the CodeSearchNet project. The model

Chen Wu 24 Nov 29, 2022
Neural-PIL: Neural Pre-Integrated Lighting for Reflectance Decomposition - NeurIPS2021

Neural-PIL: Neural Pre-Integrated Lighting for Reflectance Decomposition Project Page | Video | Paper Implementation for Neural-PIL. A novel method wh

Computergraphics (University of Tübingen) 64 Dec 29, 2022
Plenoxels: Radiance Fields without Neural Networks, Code release WIP

Plenoxels: Radiance Fields without Neural Networks Alex Yu*, Sara Fridovich-Keil*, Matthew Tancik, Qinhong Chen, Benjamin Recht, Angjoo Kanazawa UC Be

Alex Yu 2.3k Dec 30, 2022
An offline deep reinforcement learning library

d3rlpy: An offline deep reinforcement learning library d3rlpy is an offline deep reinforcement learning library for practitioners and researchers. imp

Takuma Seno 817 Jan 02, 2023
Official implementation of MSR-GCN (ICCV 2021 paper)

MSR-GCN Official implementation of MSR-GCN: Multi-Scale Residual Graph Convolution Networks for Human Motion Prediction (ICCV 2021 paper) [Paper] [Sup

LevonDang 42 Nov 07, 2022
Retinal Vessel Segmentation with Pixel-wise Adaptive Filters (ISBI 2022)

Retinal Vessel Segmentation with Pixel-wise Adaptive Filters (ISBI 2022) Introdu

anonymous 14 Oct 27, 2022
Bootstrapped Representation Learning on Graphs

Bootstrapped Representation Learning on Graphs This is the PyTorch implementation of BGRL Bootstrapped Representation Learning on Graphs The main scri

NerDS Lab :: Neural Data Science Lab 55 Jan 07, 2023
Implementation of Fast Transformer in Pytorch

Fast Transformer - Pytorch Implementation of Fast Transformer in Pytorch. This only work as an encoder. Yannic video AI Epiphany Install $ pip install

Phil Wang 167 Dec 27, 2022
A High-Performance Distributed Library for Large-Scale Bundle Adjustment

MegBA: A High-Performance and Distributed Library for Large-Scale Bundle Adjustment This repo contains an official implementation of MegBA. MegBA is a

旷视研究院 3D 组 336 Dec 27, 2022
Multi Task RL Baselines

MTRL Multi Task RL Algorithms Contents Introduction Setup Usage Documentation Contributing to MTRL Community Acknowledgements Introduction M

Facebook Research 171 Jan 09, 2023
A set of tools for converting a darknet dataset to COCO format working with YOLOX

darknet格式数据→COCO darknet训练数据目录结构(详情参见dataset/darknet): darknet ├── class.names ├── gen_config.data ├── gen_train.txt ├── gen_valid.txt └── images

RapidAI-NG 148 Jan 03, 2023
A toolkit for Lagrangian-based constrained optimization in Pytorch

Cooper About Cooper is a toolkit for Lagrangian-based constrained optimization in Pytorch. This library aims to encourage and facilitate the study of

Cooper 34 Jan 01, 2023
Code release for paper: The Boombox: Visual Reconstruction from Acoustic Vibrations

The Boombox: Visual Reconstruction from Acoustic Vibrations Boyuan Chen, Mia Chiquier, Hod Lipson, Carl Vondrick Columbia University Project Website |

Boyuan Chen 12 Nov 30, 2022
OpenMatch: Open-set Consistency Regularization for Semi-supervised Learning with Outliers (NeurIPS 2021)

OpenMatch: Open-set Consistency Regularization for Semi-supervised Learning with Outliers (NeurIPS 2021) This is an PyTorch implementation of OpenMatc

Vision and Learning Group 38 Dec 26, 2022