Search Results for author: Mark Riedl

Found 38 papers, 7 papers with code

An Ontology of Co-Creative AI Systems

no code implementations11 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.

A Controllable Co-Creative Agent for Game System Design

no code implementations4 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.

Ambient Adventures: Teaching ChatGPT on Developing Complex Stories

no code implementations3 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.

Story Generation

Thespian: Multi-Character Text Role-Playing Game Agents

no code implementations3 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.

Few-Shot Learning

Dialogue Shaping: Empowering Agents through NPC Interaction

no code implementations28 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.

Knowledge Graphs reinforcement-learning +1

Improving Language Models with Advantage-based Offline Policy Gradients

1 code implementation24 May 2023 Ashutosh Baheti, Ximing Lu, Faeze Brahman, Ronan Le Bras, Maarten Sap, Mark Riedl

We introduce Advantage-Leftover Lunch RL (A-LoL), a new class of offline policy gradient algorithms that enable RL training on any pre-existing data.

Language Modelling Offline RL +1

Few-Shot Dialogue Summarization via Skeleton-Assisted Prompt Transfer

no code implementations20 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.

Dialogue State Tracking Transfer Learning

Why Don't You Do Something About It? Outlining Connections between AI Explanations and User Actions

no code implementations10 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.

Explainable Artificial Intelligence (XAI)

Beyond Prompts: Exploring the Design Space of Mixed-Initiative Co-Creativity Systems

1 code implementation3 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.

Story Shaping: Teaching Agents Human-like Behavior with Stories

no code implementations24 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.

reinforcement-learning Reinforcement Learning (RL) +1

Machine Learning Approaches for Principle Prediction in Naturally Occurring Stories

no code implementations19 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.

Robust Preference Learning for Storytelling via Contrastive Reinforcement Learning

no code implementations14 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.

Contrastive Learning Language Modelling +4

Creative Wand: A System to Study Effects of Communications in Co-Creative Settings

1 code implementation4 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.

Calibrating Trust of Multi-Hop Question Answering Systems with Decompositional Probes

no code implementations16 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.

Explanation Generation Multi-hop Question Answering +1

NovGrid: A Flexible Grid World for Evaluating Agent Response to Novelty

no code implementations23 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.

Decision Making reinforcement-learning +1

Reframing Human-AI Collaboration for Generating Free-Text Explanations

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.

Just Say No: Analyzing the Stance of Neural Dialogue Generation in Offensive Contexts

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.

Dialogue Generation

Inferring the Reader: Guiding Automated Story Generation with Commonsense Reasoning

1 code implementation4 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.

Language Modelling Story Generation

Training Value-Aligned Reinforcement Learning Agents Using a Normative Prior

no code implementations19 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.

reinforcement-learning Reinforcement Learning (RL)

Weird AI Yankovic: Generating Parody Lyrics

no code implementations25 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.

Text Generation

Conceptual Game Expansion

no code implementations22 Feb 2020 Matthew Guzdial, Mark Riedl

Automated game design is the problem of automatically producing games through computational processes.

Learning Norms from Stories: A Prior for Value Aligned Agents

no code implementations7 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.

Imitation Learning

Improving Deep Reinforcement Learning in Minecraft with Action Advice

no code implementations2 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.

BIG-bench Machine Learning reinforcement-learning +1

Guided Neural Language Generation for Automated Storytelling

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).

Story Generation

Making CNNs for Video Parsing Accessible

no code implementations10 Jun 2019 Zijin Luo, Matthew Guzdial, Mark Riedl

This serves as a barrier to groups who don't possess this access.

An Interaction Framework for Studying Co-Creative AI

no code implementations22 Mar 2019 Matthew Guzdial, Mark Riedl

Machine learning has been applied to a number of creative, design-oriented tasks.

BIG-bench Machine Learning

Automated Rationale Generation: A Technique for Explainable AI and its Effects on Human Perceptions

no code implementations11 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.

Explanation Generation

Towards Automated Let's Play Commentary

no code implementations25 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.

BIG-bench Machine Learning Test

Explainable PCGML via Game Design Patterns

no code implementations25 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.

BIG-bench Machine Learning

Co-Creative Level Design via Machine Learning

no code implementations25 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.

BIG-bench Machine Learning

Automated Game Design via Conceptual Expansion

no code implementations6 Sep 2018 Matthew Guzdial, Mark Riedl

To the best of our knowledge, this represents the first machine learning-based automated game design system.

BIG-bench Machine Learning

A Simple and Effective Approach to the Story Cloze Test

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.

Cloze Test Feature Engineering +2

Learning to Blend Computer Game Levels

no code implementations8 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.

Toward Game Level Generation from Gameplay Videos

no code implementations23 Feb 2016 Matthew Guzdial, Mark Riedl

We further demonstrate how the acquired design knowledge can be used to generate sections of game levels.

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