11 citations · 19 across the 6 of their papers we have counts for
6 papers
Explainable Reinforcement Learning Agents Using World Models
Madhuri Singh, Amal Alabdulkarim, Gennie Mansi +1
Explainable AI (XAI) systems have been proposed to help people understand how AI systems produce outputs and behaviors. Explainable Reinforcement Learning (XRL) has an added comple…
Experiential Explanations for Reinforcement Learning
Amal Alabdulkarim, Madhuri Singh, Gennie Mansi +3
Reinforcement learning (RL) systems can be complex and non-interpretable, making it challenging for non-AI experts to understand or intervene in their decisions. This is due in par…
Goal-Directed Story Generation: Augmenting Generative Language Models with Reinforcement Learning
Amal Alabdulkarim, Winston Li, Lara J. Martin +1
The advent of large pre-trained generative language models has provided a common framework for AI story generation via sampling the model to create sequences that continue the stor…
Guiding Neural Story Generation with Reader Models
Xiangyu Peng, Kaige Xie, Amal Alabdulkarim +3
Automated storytelling has long captured the attention of researchers for the ubiquity of narratives in everyday life. However, it is challenging to maintain coherence and stay on-…
AraStance: A Multi-Country and Multi-Domain Dataset of Arabic Stance Detection for Fact Checking
Tariq Alhindi, Amal Alabdulkarim, Ali Alshehri +2
With the continuing spread of misinformation and disinformation online, it is of increasing importance to develop combating mechanisms at scale in the form of automated systems tha…
Automatic Story Generation: Challenges and Attempts
Amal Alabdulkarim, Siyan Li, Xiangyu Peng
The scope of this survey paper is to explore the challenges in automatic story generation. We hope to contribute in the following ways: 1. Explore how previous research in story ge…