It is essential for users to understand what their AI systems can and can't do in order to use
them safely. However, the problem of enabling users to assess AI systems with sequential …

Much of the research on learning symbolic models of AI agents focuses on agents with
stationary models. This assumption fails to hold in settings where the agent's capabilities …

Abstract Knowledge graph embedding has received widespread attention in recent years.
Most existing models represent time-independent facts as low dimensional embeddings …

This paper presents JEDAI, an AI system designed for outreach and educational efforts
aimed at non-AI experts. JEDAI features a novel synthesis of research ideas from integrated …

As a challenging sub-task of event argument extraction, implicit event argument extraction
seeks to identify document-level arguments that play direct or implicit roles in a given event …

Hand-crafted, logic-based state and action representations have been widely used to
overcome the intractable computational complexity of long-horizon robot planning problems …

Event extraction (EE) occupies an important position in information extraction. Recently,
deep neural network methods have been demonstrated to learn potential features well …

Several approaches have been developed for answering users' specific questions about AI
behavior and for assessing their core functionality in terms of primitive executable actions …

Recent advances in Artificial Intelligence (AI) especially in deep learning have manifested
an increasing concern in trustworthiness, and its subparts such as interpretability, safety …

The prevalence and success of AI applications have been tempered by concerns about the
controllability of AI systems about AI's impact on the future of work. These concerns reflect …