Trustworthy AI: From principles to practices
The rapid development of Artificial Intelligence (AI) technology has enabled the deployment
of various systems based on it. However, many current AI systems are found vulnerable to …
of various systems based on it. However, many current AI systems are found vulnerable to …
[HTML][HTML] A comprehensive survey on the application of deep and reinforcement learning approaches in autonomous driving
Abstract Recent advances in Intelligent Transport Systems (ITS) and Artificial Intelligence
(AI) have stimulated and paved the way toward the widespread introduction of Autonomous …
(AI) have stimulated and paved the way toward the widespread introduction of Autonomous …
A survey on interpretable reinforcement learning
Although deep reinforcement learning has become a promising machine learning approach
for sequential decision-making problems, it is still not mature enough for high-stake domains …
for sequential decision-making problems, it is still not mature enough for high-stake domains …
Human-in-the-loop reinforcement learning: A survey and position on requirements, challenges, and opportunities
Artificial intelligence (AI) and especially reinforcement learning (RL) have the potential to
enable agents to learn and perform tasks autonomously with superhuman performance …
enable agents to learn and perform tasks autonomously with superhuman performance …
Safe deep reinforcement learning for building energy management
The optimization of building energy systems poses a complex challenge due to the dynamic
nature of building environments and the need for ensuring both energy efficiency and …
nature of building environments and the need for ensuring both energy efficiency and …
Constrained reinforcement learning for vehicle motion planning with topological reachability analysis
Rule-based traditional motion planning methods usually perform well with prior knowledge
of the macro-scale environments but encounter challenges in unknown and uncertain …
of the macro-scale environments but encounter challenges in unknown and uncertain …
A safety-critical decision-making and control framework combining machine-learning-based and rule-based algorithms
While machine-learning-based methods suffer from a lack of transparency, rule-based (RB)
methods dominate safety-critical systems. Yet the RB approaches cannot compete with the …
methods dominate safety-critical systems. Yet the RB approaches cannot compete with the …
Reinforcement learning-based real-time control of coastal urban stormwater systems to mitigate flooding and improve water quality
Real-time control of stormwater systems can reduce flooding and improve water quality.
Current industry real-time control strategies use simple rules based on water quantity …
Current industry real-time control strategies use simple rules based on water quantity …
Human-like decision making of artificial drivers in intelligent transportation systems: An end-to-end driving behavior prediction approach
Drivers can be either human beings or artificial drivers in future intelligent transportation
systems (ITSs). It is important to learn how people drive so that artificial drivers can be …
systems (ITSs). It is important to learn how people drive so that artificial drivers can be …
Multi-agent reinforcement learning for autonomous driving: A survey
Reinforcement Learning (RL) is a potent tool for sequential decision-making and has
achieved performance surpassing human capabilities across many challenging real-world …
achieved performance surpassing human capabilities across many challenging real-world …