no code implementations • 16 Jan 2025 • Md Sultan Al Nahian, Tasmia Tasrin, Spencer Frazier, Mark Riedl, Brent Harrison
Values or principles are key elements of human society that influence people to behave and function according to an accepted standard set of social rules to maintain social order.
no code implementations • 26 Dec 2024 • Ashutosh Baheti, Debanjana Chakraborty, Faeze Brahman, Ronan Le Bras, Ximing Lu, Nouha Dziri, Yejin Choi, Mark Riedl, Maarten Sap
Thus, we create Multi-Attribute Constraint Satisfaction (MACS), a generalized method capable of finetuning language models on any sequential domain to satisfy user-specified constraints on multiple external real-value attributes.
no code implementations • 18 Sep 2024 • Kaige Xie, Ian Yang, John Gunerli, Mark Riedl
This is achieved by fine-tuning two separate LLMs-one for precondition prediction and another for effect prediction-while leveraging synthetic data generation techniques.
no code implementations • 28 Jul 2024 • Kenneth Eaton, Jonathan Balloch, Julia Kim, Mark Riedl
Interpretability of deep reinforcement learning systems could assist operators with understanding how they interact with their environment.
1 code implementation • 28 Jun 2024 • Rishav Bhagat, Jonathan Balloch, Zhiyu Lin, Julia Kim, Mark Riedl
Unlike reinforcement learning (RL) agents, humans remain capable multitaskers in changing environments.
no code implementations • 27 Feb 2024 • Kaige Xie, Mark Riedl
To the best of our knowledge, this paper is the first attempt at suspenseful story generation with LLMs.
no code implementations • 11 Oct 2023 • 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.
no code implementations • 4 Aug 2023 • 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.
no code implementations • 3 Aug 2023 • Zexin Chen, Eric Zhou, Kenneth Eaton, Xiangyu Peng, Mark Riedl
Imaginative play is an area of creativity that could allow robots to engage with the world around them in a much more personified way.
no code implementations • 3 Aug 2023 • Christopher Cui, Xiangyu Peng, Mark Riedl
Text-adventure games and text role-playing games are grand challenges for reinforcement learning game playing agents.
no code implementations • 28 Jul 2023 • 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 in text-based game environments where the action space is extensive.
1 code implementation • 24 May 2023 • Ashutosh Baheti, Ximing Lu, Faeze Brahman, Ronan Le Bras, Maarten Sap, Mark Riedl
However, RLHF is an unstable and data-hungry process that continually requires new high-quality LM-generated data for finetuning.
no code implementations • 20 May 2023 • Kaige Xie, Tong Yu, Haoliang Wang, Junda Wu, Handong Zhao, Ruiyi Zhang, Kanak Mahadik, Ani Nenkova, Mark Riedl
In this paper, we focus on improving the prompt transfer from dialogue state tracking to dialogue summarization and propose Skeleton-Assisted Prompt Transfer (SAPT), which leverages skeleton generation as extra supervision that functions as a medium connecting the distinct source and target task and resulting in the model's better consumption of dialogue state information.
no code implementations • 10 May 2023 • 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.
1 code implementation • 3 May 2023 • Zhiyu Lin, Upol Ehsan, Rohan Agarwal, Samihan Dani, Vidushi Vashishth, Mark Riedl
We find out that MI-CC systems with more extensive coverage of the design space are rated higher or on par on a variety of creative and goal-completion metrics, demonstrating that wider coverage of the design space can improve user experience and achievement when using the system; Preference varies greatly between expertise groups, suggesting the development of adaptive, personalized MI-CC systems; Participants identified new design space dimensions including scrutability -- the ability to poke and prod at models -- and explainability.
no code implementations • 24 Jan 2023 • Xiangyu Peng, Christopher Cui, Wei Zhou, Renee Jia, Mark Riedl
We introduce a technique, Story Shaping, in which a reinforcement learning agent infers tacit knowledge from an exemplar story of how to accomplish a task and intrinsically rewards itself for performing actions that make its current environment adhere to that of the inferred story world.
no code implementations • 19 Nov 2022 • Md Sultan Al Nahian, Spencer Frazier, Brent Harrison, Mark Riedl
To do this, we extend a dataset that has been previously used to train a binary normative classifier with annotations of moral principles.
no code implementations • 14 Oct 2022 • Louis Castricato, Alexander Havrilla, Shahbuland Matiana, Michael Pieler, Anbang Ye, Ian Yang, Spencer Frazier, Mark Riedl
However, simply fine-tuning a generative language model with a contrastive reward model does not always reliably result in a story generation system capable of generating stories that meet user preferences.
1 code implementation • 4 Aug 2022 • Zhiyu Lin, Rohan Agarwal, Mark Riedl
Recent neural generation systems have demonstrated the potential for procedurally generating game content, images, stories, and more.
no code implementations • 16 Apr 2022 • Kaige Xie, Sarah Wiegreffe, Mark Riedl
We show that decomposition is an effective form of probing QA systems as well as a promising approach to explanation generation.
no code implementations • 23 Mar 2022 • Jonathan Balloch, Zhiyu Lin, Mustafa Hussain, Aarun Srinivas, Robert Wright, Xiangyu Peng, Julia Kim, Mark Riedl
We provide an ontology of for novelties most relevant to sequential decision making, which distinguishes between novelties that affect objects versus actions, unary properties versus non-unary relations, and the distribution of solutions to a task.
1 code implementation • NAACL 2022 • Sarah Wiegreffe, Jack Hessel, Swabha Swayamdipta, Mark Riedl, Yejin Choi
We create a pipeline that combines GPT-3 with a supervised filter that incorporates binary acceptability judgments from humans in the loop.
no code implementations • 7 Dec 2021 • Louis Castricato, Spencer Frazier, Jonathan Balloch, Nitya Tarakad, Mark Riedl
Neural language model-based approaches to automated story generation suffer from two important limitations.
1 code implementation • EMNLP 2021 • Ashutosh Baheti, Maarten Sap, Alan Ritter, Mark Riedl
To better understand the dynamics of contextually offensive language, we investigate the stance of dialogue model responses in offensive Reddit conversations.
1 code implementation • 4 May 2021 • Xiangyu Peng, Siyan Li, Sarah Wiegreffe, Mark Riedl
Transformer-based language model approaches to automated story generation currently provide state-of-the-art results.
no code implementations • 19 Apr 2021 • Md Sultan Al Nahian, Spencer Frazier, Brent Harrison, Mark Riedl
As more machine learning agents interact with humans, it is increasingly a prospect that an agent trained to perform a task optimally, using only a measure of task performance as feedback, can violate societal norms for acceptable behavior or cause harm.
1 code implementation • NAACL (NUSE) 2021 • Zhiyu Lin, Mark Riedl
Large pre-trained neural language models (LM) have very powerful text generation capabilities.
no code implementations • NAACL (NUSE) 2021 • Louis Castricato, Spencer Frazier, Jonathan Balloch, Mark Riedl
Automated story generation remains a difficult area of research because it lacks strong objective measures.
no code implementations • 25 Sep 2020 • Mark Riedl
Lyrics parody swaps one set of words that accompany a melody with a new set of words, preserving the number of syllables per line and the rhyme scheme.
no code implementations • 22 Feb 2020 • Matthew Guzdial, Mark Riedl
Automated game design is the problem of automatically producing games through computational processes.
no code implementations • INLG (ACL) 2020 • Xiangyu Peng, Siyan Li, Spencer Frazier, Mark Riedl
Our normative fine-tuning technique is able to reduce non-normative text by 27-61%, depending on the data set.
no code implementations • 7 Dec 2019 • Spencer Frazier, Md Sultan Al Nahian, Mark Riedl, Brent Harrison
Value alignment is a property of an intelligent agent indicating that it can only pursue goals and activities that are beneficial to humans.
no code implementations • 2 Aug 2019 • Spencer Frazier, Mark Riedl
We hypothesize that interactive machine learning IML, wherein human teachers play a direct role in training through demonstrations, critique, or action advice, may alleviate agent susceptibility to aliasing.
no code implementations • WS 2019 • Prithviraj Ammanabrolu, Ethan Tien, Wesley Cheung, Zhaochen Luo, William Ma, Lara Martin, Mark Riedl
Prior work has shown that a semantic abstraction of sentences called events improves neural plot generation and and allows one to decompose the problem into: (1) the generation of a sequence of events (event-to-event) and (2) the transformation of these events into natural language sentences (event-to-sentence).
no code implementations • 10 Jun 2019 • Zijin Luo, Matthew Guzdial, Mark Riedl
This serves as a barrier to groups who don't possess this access.
no code implementations • 22 Mar 2019 • Matthew Guzdial, Mark Riedl
Machine learning has been applied to a number of creative, design-oriented tasks.
no code implementations • 11 Jan 2019 • Upol Ehsan, Pradyumna Tambwekar, Larry Chan, Brent Harrison, Mark Riedl
The second study further explores user preferences between the generated rationales with regard to confidence in the autonomous agent, communicating failure and unexpected behavior.
no code implementations • 25 Sep 2018 • Matthew Guzdial, Shukan Shah, Mark Riedl
We introduce the problem of generating Let's Play-style commentary of gameplay video via machine learning.
no code implementations • 25 Sep 2018 • Matthew Guzdial, Nicholas Liao, Mark Riedl
Procedural Level Generation via Machine Learning (PLGML), the study of generating game levels with machine learning, has received a large amount of recent academic attention.
no code implementations • 25 Sep 2018 • Matthew Guzdial, Joshua Reno, Jonathan Chen, Gillian Smith, Mark Riedl
Procedural content generation via Machine Learning (PCGML) is the umbrella term for approaches that generate content for games via machine learning.
no code implementations • 6 Sep 2018 • Matthew Guzdial, Mark Riedl
To the best of our knowledge, this represents the first machine learning-based automated game design system.
no code implementations • NAACL 2018 • Siddarth Srinivasan, Richa Arora, Mark Riedl
In the Story Cloze Test, a system is presented with a 4-sentence prompt to a story, and must determine which one of two potential endings is the 'right' ending to the story.
Ranked #15 on
Question Answering
on StoryCloze
no code implementations • 8 Mar 2016 • Matthew Guzdial, Mark Riedl
We evaluate the extent to which the models represent stylistic level design knowledge and demonstrate the ability of our system to explain levels that were blended by human expert designers.
no code implementations • 23 Feb 2016 • Matthew Guzdial, Mark Riedl
We further demonstrate how the acquired design knowledge can be used to generate sections of game levels.