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Found 39 papers, 7 papers with code
Creating Suspenseful Stories: Iterative Planning with Large Language Models
To the best of our knowledge, this paper is the first attempt at suspenseful story generation with LLMs.
An Ontology of Co-Creative AI Systems
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
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
Imaginative play is an area of creativity that could allow robots to engage with the world around them in a much more personified way.
Thespian: Multi-Character Text Role-Playing Game Agents
Text-adventure games and text role-playing games are grand challenges for reinforcement learning game playing agents.
Dialogue Shaping: Empowering Agents through NPC Interaction
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.
Leftover Lunch: Advantage-based Offline Reinforcement Learning for Language Models
However, RLHF is an unstable and data-hungry process that continually requires new high-quality LM-generated data for finetuning.
Few-Shot Dialogue Summarization via Skeleton-Assisted Prompt Transfer in Prompt Tuning
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.
Why Don't You Do Something About It? Outlining Connections between AI Explanations and User Actions
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.
Beyond Prompts: Exploring the Design Space of Mixed-Initiative Co-Creativity Systems
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
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.
Machine Learning Approaches for Principle Prediction in Naturally Occurring Stories
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
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.
Creative Wand: A System to Study Effects of Communications in Co-Creative Settings
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
We show that decomposition is an effective form of probing QA systems as well as a promising approach to explanation generation.
NovGrid: A Flexible Grid World for Evaluating Agent Response to Novelty
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.
Reframing Human-AI Collaboration for Generating Free-Text Explanations
We create a pipeline that combines GPT-3 with a supervised filter that incorporates binary acceptability judgments from humans in the loop.
Automated Story Generation as Question-Answering
Neural language model-based approaches to automated story generation suffer from two important limitations.
Just Say No: Analyzing the Stance of Neural Dialogue Generation in Offensive Contexts
To better understand the dynamics of contextually offensive language, we investigate the stance of dialogue model responses in offensive Reddit conversations.
Inferring the Reader: Guiding Automated Story Generation with Commonsense Reasoning
Transformer-based language model approaches to automated story generation currently provide state-of-the-art results.
Training Value-Aligned Reinforcement Learning Agents Using a Normative Prior
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.
Plug-and-Blend: A Framework for Controllable Story Generation with Blended Control Codes
Large pre-trained neural language models (LM) have very powerful text generation capabilities.
Fabula Entropy Indexing: Objective Measures of Story Coherence
Automated story generation remains a difficult area of research because it lacks strong objective measures.
Weird AI Yankovic: Generating Parody Lyrics
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.
Conceptual Game Expansion
Automated game design is the problem of automatically producing games through computational processes.
Reducing Non-Normative Text Generation from Language Models
Our normative fine-tuning technique is able to reduce non-normative text by 27-61%, depending on the data set.
Learning Norms from Stories: A Prior for Value Aligned Agents
Value alignment is a property of an intelligent agent indicating that it can only pursue goals and activities that are beneficial to humans.
Improving Deep Reinforcement Learning in Minecraft with Action Advice
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.
Guided Neural Language Generation for Automated Storytelling
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).
Making CNNs for Video Parsing Accessible
This serves as a barrier to groups who don't possess this access.
An Interaction Framework for Studying Co-Creative AI
Machine learning has been applied to a number of creative, design-oriented tasks.
Automated Rationale Generation: A Technique for Explainable AI and its Effects on Human Perceptions
The second study further explores user preferences between the generated rationales with regard to confidence in the autonomous agent, communicating failure and unexpected behavior.
Towards Automated Let's Play Commentary
We introduce the problem of generating Let's Play-style commentary of gameplay video via machine learning.
Co-Creative Level Design via Machine Learning
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.
Explainable PCGML via Game Design Patterns
Procedural content generation via Machine Learning (PCGML) is the umbrella term for approaches that generate content for games via machine learning.
Automated Game Design via Conceptual Expansion
To the best of our knowledge, this represents the first machine learning-based automated game design system.
A Simple and Effective Approach to the Story Cloze Test
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
Learning to Blend Computer Game Levels
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
We further demonstrate how the acquired design knowledge can be used to generate sections of game levels.
