27 citations · 61 across the 13 of their papers we have counts for
8 papers · 1 filter
External Model Motivated Agents: Reinforcement Learning for Enhanced Environment Sampling
Rishav Bhagat, Jonathan Balloch, Zhiyu Lin +2
Unlike reinforcement learning (RL) agents, humans remain capable multitaskers in changing environments. In spite of only experiencing the world through their own observations and i…
An Ontology of Co-Creative AI Systems
Zhiyu Lin, Mark Riedl
The term co-creativity has been used to describe a wide variety of human-AI assemblages in which human and AI are both involved in a creative endeavor. In order to assist with disa…
A Controllable Co-Creative Agent for Game System Design
Rohan Agarwal, Zhiyu Lin, Mark Riedl
Many advancements have been made in procedural content generation for games, and with mixed-initiative co-creativity, have the potential for great benefits to human designers. Howe…
Beyond Prompts: Exploring the Design Space of Mixed-Initiative Co-Creativity Systems
Zhiyu Lin, Upol Ehsan, Rohan Agarwal +3
Generative Artificial Intelligence systems have been developed for image, code, story, and game generation with the goal of facilitating human creativity. Recent work on neural gen…
Neuro-Symbolic World Models for Adapting to Open World Novelty
Jonathan Balloch, Zhiyu Lin, Robert Wright +5
Open-world novelty--a sudden change in the mechanics or properties of an environment--is a common occurrence in the real world. Novelty adaptation is an agent's ability to improve…
Creative Wand: A System to Study Effects of Communications in Co-Creative Settings
Zhiyu Lin, Rohan Agarwal, Mark Riedl
Recent neural generation systems have demonstrated the potential for procedurally generating game content, images, stories, and more. However, most neural generation algorithms are…
NovGrid: A Flexible Grid World for Evaluating Agent Response to Novelty
Jonathan Balloch, Zhiyu Lin, Mustafa Hussain +5
A robust body of reinforcement learning techniques have been developed to solve complex sequential decision making problems. However, these methods assume that train and evaluation…
Explore, Exploit or Listen: Combining Human Feedback and Policy Model to Speed up Deep Reinforcement Learning in 3D Worlds
Zhiyu Lin, Brent Harrison, Aaron Keech +1
We describe a method to use discrete human feedback to enhance the performance of deep learning agents in virtual three-dimensional environments by extending deep-reinforcement lea…