Autonomous robots operating in uncertain or hazardous environments subject to state safety
constraints must be able to identify and isolate faulty components in a time-optimal manner …

Sequential decision-making problems arise in every arena of daily life and pose unique
challenges for research in decision-theoretic planning. Although there has been a wide …

Fault detection, which requires a lot of time and complexity, is one of the most difficult tasks
for cloud computing. In this research, we investigate the utilization of the anytime algorithm …

We consider the problem of task assignment and scheduling for human-robot teams to
enable the efficient completion of complex problems, such as satellite assembly. In high-mix …

Autonomous vehicles face the problem of optimizing the expected performance of
subsequent maneuvers while bounding the risk of collision with surrounding dynamic …

In transportation networks, where traffic lights have traditionally been used for vehicle
coordination, intersections act as natural bottlenecks. A formidable challenge for existing …

Abstract The Partially Observable Markov Decision Process (POMDP) is widely used in
probabilistic planning for stochastic domains. However, current extensions, such as …

Abstract Partially Observable Markov Decision Process (POMDP) is a fundamental model for
probabilistic planning in stochastic domains. More recently, constrained POMDP and …

With the rapid development of satellite network, route problem has gained much attention in
these years to ensure the service quality. However, due to the uncertain transmission …

Risk-bounded motion planning is an important yet difficult problem for safety-critical tasks.
While existing mathematical programming methods offer theoretical guarantees in the …