9 citations · 51 across the 15 of their papers we have counts for
15 papers
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…
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…
Ambient Adventures: Teaching ChatGPT on Developing Complex Stories
Zexin Chen, Eric Zhou, Kenneth Eaton +2
Imaginative play is an area of creativity that could allow robots to engage with the world around them in a much more personified way. Imaginary play can be seen as taking real obj…
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…
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…
Model ensemble instead of prompt fusion: a sample-specific knowledge transfer method for few-shot prompt tuning
Xiangyu Peng, Chen Xing, Prafulla Kumar Choubey +2
Prompt tuning approaches, which learn task-specific soft prompts for a downstream task conditioning on frozen pre-trained models, have attracted growing interest due to its paramet…
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…
XFBoost: Improving Text Generation with Controllable Decoders
Xiangyu Peng, Michael Sollami
Multimodal conditionality in transformer-based natural language models has demonstrated state-of-the-art performance in the task of product description generation. Recent approache…
Inherently Explainable Reinforcement Learning in Natural Language
Xiangyu Peng, Mark O. Riedl, Prithviraj Ammanabrolu
We focus on the task of creating a reinforcement learning agent that is inherently explainable -- with the ability to produce immediate local explanations by thinking out loud whil…
Guiding Neural Story Generation with Reader Models
Xiangyu Peng, Kaige Xie, Amal Alabdulkarim +3
Automated storytelling has long captured the attention of researchers for the ubiquity of narratives in everyday life. However, it is challenging to maintain coherence and stay on-…
Detecting and Adapting to Novelty in Games
Xiangyu Peng, Jonathan C. Balloch, Mark O. Riedl
Open-world novelty occurs when the rules of an environment can change abruptly, such as when a game player encounters "house rules". To address open-world novelty, game playing age…
Inferring the Reader: Guiding Automated Story Generation with Commonsense Reasoning
Xiangyu Peng, Siyan Li, Sarah Wiegreffe +1
Transformer-based language model approaches to automated story generation currently provide state-of-the-art results. However, they still suffer from plot incoherence when generati…
Automatic Story Generation: Challenges and Attempts
Amal Alabdulkarim, Siyan Li, Xiangyu Peng
The scope of this survey paper is to explore the challenges in automatic story generation. We hope to contribute in the following ways: 1. Explore how previous research in story ge…
Reducing Non-Normative Text Generation from Language Models
Xiangyu Peng, Siyan Li, Spencer Frazier +1
Large-scale, transformer-based language models such as GPT-2 are pretrained on diverse corpora scraped from the internet. Consequently, they are prone to generating non-normative t…