Partially observable Markov decision processes (POMDPs) have been widely...
Probabilistic model checking can provide formal guarantees on the behavi...
Early identification of high risk heart failure (HF) patients is key to
...
As multi-agent reinforcement learning (MARL) systems are increasingly
de...
In this paper we focus on the problem of generating high-quality, privat...
Machine Learning (ML) technologies have been increasingly adopted in Med...
In conditionally automated driving, drivers decoupled from driving while...
Automated vehicles are gradually entering people's daily life to provide...
Reinforcement learning (RL) relies heavily on exploration to learn from ...
Advances in multi-agent reinforcement learning (MARL) enable sequential
...
Multi-objective controller synthesis concerns the problem of computing a...
Multi-agent reinforcement learning (MARL) has been increasingly used in ...
Several recent works consider the personalized route planning based on u...
Automated vehicles promise a future where drivers can engage in non-driv...
Invariants are a set of properties over program attributes that are expe...
Prior studies have found that providing explanations about robots' decis...
We present CityPM, a novel predictive monitoring system for smart cities...
Providing explanations of chosen robotic actions can help to increase th...
A safe transition between autonomous and manual control requires sustain...
With the increasing number of smart services implemented in smart cities...
There is an increasing need for the runtime monitoring of real time safe...
Type I Diabetes (T1D) is a chronic disease in which the body's ability t...
As autonomous vehicles have benefited the society, understanding the dyn...
It remains uncertain regarding the safety of driving in autonomous vehic...
Automated techniques such as model checking have been used to verify mod...