most citedGenerative Personas That Behave and Experience Like Humans

25 citations · 42 across the 6 of their papers we have counts for

collaborators

6 papers

cs.IR20231 cited

A Preliminary Study on a Conceptual Game Feature Generation and Recommendation System

M Charity, Yash Bhartia, Daniel Zhang +2

This paper introduces a system used to generate game feature suggestions based on a text prompt. Trained on the game descriptions of almost 60k games, it uses the word embeddings o…

cs.LG20231 cited

Lode Enhancer: Level Co-creation Through Scaling

Debosmita Bhaumik, Julian Togelius, Georgios N. Yannakakis +1

We explore AI-powered upscaling as a design assistance tool in the context of creating 2D game levels. Deep neural networks are used to upscale artificially downscaled patches of l…

cs.LG202315 cited

Lode Encoder: AI-constrained co-creativity

Debosmita Bhaumik, Ahmed Khalifa, Julian Togelius

We present Lode Encoder, a gamified mixed-initiative level creation system for the classic platform-puzzle game Lode Runner. The system is built around several autoencoders which a…

cs.AI2023

Controllable Path of Destruction

Matthew Siper, Sam Earle, Zehua Jiang +2

Path of Destruction (PoD) is a self-supervised method for learning iterative generators. The core idea is to produce a training set by destroying a set of artifacts, and for each d…

cs.AI202225 cited

Generative Personas That Behave and Experience Like Humans

Matthew Barthet, Ahmed Khalifa, Antonios Liapis +1

Using artificial intelligence (AI) to automatically test a game remains a critical challenge for the development of richer and more complex game worlds and for the advancement of A…

cs.LG2022

Play with Emotion: Affect-Driven Reinforcement Learning

Matthew Barthet, Ahmed Khalifa, Antonios Liapis +1

This paper introduces a paradigm shift by viewing the task of affect modeling as a reinforcement learning (RL) process. According to the proposed paradigm, RL agents learn a policy…