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.
Imaginative play is an area of creativity that could allow robots to engage with the world around them in a much more personified way.
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.
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.
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.
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.
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.
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.
To do this, we extend a dataset that has been previously used to train a binary normative classifier with annotations of moral principles.
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.
Recent neural generation systems have demonstrated the potential for procedurally generating game content, images, stories, and more.
We show that decomposition is an effective form of probing QA systems as well as a promising approach to explanation generation.
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.
We create a pipeline that combines GPT-3 with a supervised filter that incorporates binary acceptability judgments from humans in the loop.
Neural language model-based approaches to automated story generation suffer from two important limitations.
To better understand the dynamics of contextually offensive language, we investigate the stance of dialogue model responses in offensive Reddit conversations.
Transformer-based language model approaches to automated story generation currently provide state-of-the-art results.
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.
Automated story generation remains a difficult area of research because it lacks strong objective measures.
Our normative fine-tuning technique is able to reduce non-normative text by 27-61%, depending on the data set.
Value alignment is a property of an intelligent agent indicating that it can only pursue goals and activities that are beneficial to humans.
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.
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).
The second study further explores user preferences between the generated rationales with regard to confidence in the autonomous agent, communicating failure and unexpected behavior.
Procedural content generation via Machine Learning (PCGML) is the umbrella term for approaches that generate content for games 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.
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 #14 on Question Answering on Story Cloze
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.