State-of-the-art deep neural networks are trained with large amounts
(mi...
Text-adventure games and text role-playing games are grand challenges fo...
Imaginative play is an area of creativity that could allow robots to eng...
One major challenge in reinforcement learning (RL) is the large amount o...
Data pruning aims to obtain lossless performances as training on the ori...
Reward design for reinforcement learning agents can be difficult in
situ...
Open-world novelty–a sudden change in the mechanics or properties of an
...
Prompt tuning approaches, which learn task-specific soft prompts for a
d...
Domain Adaptation of Black-box Predictors (DABP) aims to learn a model o...
Face recognition, as one of the most successful applications in artifici...
Learning with noisy labels has aroused much research interest since data...
A robust body of reinforcement learning techniques have been developed t...
Dataset condensation aims at reducing the network training effort throug...
Multimodal conditionality in transformer-based natural language models h...
Recent self-supervised contrastive learning methods greatly benefit from...
We focus on the task of creating a reinforcement learning agent that is
...
Automated storytelling has long captured the attention of researchers fo...
Open-world novelty occurs when the rules of an environment can change
ab...
Transformer-based language model approaches to automated story generatio...
The scope of this survey paper is to explore the challenges in automatic...