80 citations · 209 across the 33 of their papers we have counts for
34 papers
Beyond Following: Mixing Active Initiative into Computational Creativity
Zhiyu Lin, Upol Ehsan, Rohan Agarwal +3
Generative Artificial Intelligence (AI) encounters limitations in efficiency and fairness within the realm of Procedural Content Generation (PCG) when human creators solely drive a…
A Mixture-of-Experts Approach to Few-Shot Task Transfer in Open-Ended Text Worlds
Christopher Z. Cui, Xiangyu Peng, Mark O. Riedl
Open-ended worlds are those in which there are no pre-specified goals or environmental reward signal. As a consequence, an agent must know how to perform a multitude of tasks. Howe…
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…
Thespian: Multi-Character Text Role-Playing Game Agents
Christopher Cui, Xiangyu Peng, Mark Riedl
Text-adventure games and text role-playing games are grand challenges for reinforcement learning game playing agents. Text role-playing games are open-ended environments where an a…
Dialogue Shaping: Empowering Agents through NPC Interaction
Wei Zhou, Xiangyu Peng, Mark Riedl
One major challenge in reinforcement learning (RL) is the large amount of steps for the RL agent needs to converge in the training process and learn the optimal policy, especially…
Why Don't You Do Something About It? Outlining Connections between AI Explanations and User Actions
Gennie Mansi, Mark Riedl
A core assumption of explainable AI systems is that explanations change what users know, thereby enabling them to act within their complex socio-technical environments. Despite the…
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…
Leftover Lunch: Advantage-based Offline Reinforcement Learning for Language Models
Ashutosh Baheti, Ximing Lu, Faeze Brahman +3
Reinforcement Learning with Human Feedback (RLHF) is the most prominent method for Language Model (LM) alignment. However, RLHF is an unstable and data-hungry process that continua…
Few-Shot Dialogue Summarization via Skeleton-Assisted Prompt Transfer in Prompt Tuning
Kaige Xie, Tong Yu, Haoliang Wang +6
In real-world scenarios, labeled samples for dialogue summarization are usually limited (i.e., few-shot) due to high annotation costs for high-quality dialogue summaries. To effici…
Charting the Sociotechnical Gap in Explainable AI: A Framework to Address the Gap in XAI
Upol Ehsan, Koustuv Saha, Munmun De Choudhury +1
Explainable AI (XAI) systems are sociotechnical in nature; thus, they are subject to the sociotechnical gap--divide between the technical affordances and the social needs. However,…
Story Shaping: Teaching Agents Human-like Behavior with Stories
Xiangyu Peng, Christopher Cui, Wei Zhou +2
Reward design for reinforcement learning agents can be difficult in situations where one not only wants the agent to achieve some effect in the world but where one also cares about…
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…
Neural Story Planning
Anbang Ye, Christopher Cui, Taiwei Shi +1
Automated plot generation is the challenge of generating a sequence of events that will be perceived by readers as the plot of a coherent story. Traditional symbolic planners plan…
Machine Learning Approaches for Principle Prediction in Naturally Occurring Stories
Md Sultan Al Nahian, Spencer Frazier, Brent Harrison +1
Value alignment is the task of creating autonomous systems whose values align with those of humans. Past work has shown that stories are a potentially rich source of information on…
Social Construction of XAI: Do We Need One Definition to Rule Them All?
Upol Ehsan, Mark O. Riedl
There is a growing frustration amongst researchers and developers in Explainable AI (XAI) around the lack of consensus around what is meant by 'explainability'. Do we need one defi…
The Role of Exploration for Task Transfer in Reinforcement Learning
Jonathan C Balloch, Julia Kim, and Jessica L Inman +1
The exploration--exploitation trade-off in reinforcement learning (RL) is a well-known and much-studied problem that balances greedy action selection with novel experience, and the…
Robust Preference Learning for Storytelling via Contrastive Reinforcement Learning
Louis Castricato, Alexander Havrilla, Shahbuland Matiana +5
Controlled automated story generation seeks to generate natural language stories satisfying constraints from natural language critiques or preferences. Existing methods to control…
Experiential Explanations for Reinforcement Learning
Amal Alabdulkarim, Madhuri Singh, Gennie Mansi +3
Reinforcement learning (RL) systems can be complex and non-interpretable, making it challenging for non-AI experts to understand or intervene in their decisions. This is due in par…
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…
The Algorithmic Imprint
Upol Ehsan, Ranjit Singh, Jacob Metcalf +1
When algorithmic harms emerge, a reasonable response is to stop using the algorithm to resolve concerns related to fairness, accountability, transparency, and ethics (FATE). Howeve…
Calibrating Trust of Multi-Hop Question Answering Systems with Decompositional Probes
Kaige Xie, Sarah Wiegreffe, Mark Riedl
Multi-hop Question Answering (QA) is a challenging task since it requires an accurate aggregation of information from multiple context paragraphs and a thorough understanding of th…
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…
Goal-Directed Story Generation: Augmenting Generative Language Models with Reinforcement Learning
Amal Alabdulkarim, Winston Li, Lara J. Martin +1
The advent of large pre-trained generative language models has provided a common framework for AI story generation via sampling the model to create sequences that continue the stor…
Automated Story Generation as Question-Answering
Louis Castricato, Spencer Frazier, Jonathan Balloch +2
Neural language model-based approaches to automated story generation suffer from two important limitations. First, language model-based story generators generally do not work towar…