6 citations · 6 across the 6 of their papers we have counts for
7 papers
A Model-Driven Approach for Developing Families of Reinforcement Learning Environments
Xiaoran Liu, Istvan David
Virtual training environments are software-intensive systems in which reinforcement learning (RL) agents learn, adapt, and demonstrate meaningful behavior. Virtual training environ…
Trust the AI, Doubt Yourself: The Effect of Urgency on Self-Confidence in Human-AI Interaction
Baran Shajari, Xiaoran Liu, Kyanna Dagenais +1
Studies show that interactions with an AI system fosters trust in human users towards AI. An often overlooked element of such interaction dynamics is the (sense of) urgency when th…
A Reference Architecture of Reinforcement Learning Frameworks
Xiaoran Liu, Istvan David
The surge in reinforcement learning (RL) applications gave rise to diverse supporting technology, such as RL frameworks. However, the architectural patterns of these frameworks are…
AI Can Learn Scientific Taste
Jingqi Tong, Mingzhe Li, Hangcheng Li +20
Scientific discovery depends on expert judgement and foresight, which we call scientific taste: the ability to judge and propose research ideas with the potential for long-term sci…
Developing AI Agents with Simulated Data: Why, what, and how?
Xiaoran Liu, Istvan David
As insufficient data volume and quality remain the key impediments to the adoption of modern subsymbolic AI, techniques of synthetic data generation are in high demand. Simulation…
Introduction to Digital Twins for the Smart Grid
Xiaoran Liu, Istvan David
This chapter provides an introduction to the foundations of digital twins and makes the case for employing them in smart grids. As engineered systems become more complex and autono…
AI Simulation by Digital Twins: Systematic Survey, Reference Framework, and Mapping to a Standardized Architecture
Xiaoran Liu, Istvan David
Insufficient data volume and quality are particularly pressing challenges in the adoption of modern subsymbolic AI. To alleviate these challenges, AI simulation uses virtual traini…