In recent years, formal methods have been extensively used in the design of autonomous
systems. By employing mathematically rigorous techniques, formal methods can provide …

Capturing uncertainty in models of complex dynamical systems is crucial to designing safe
controllers. Stochastic noise causes aleatoric uncertainty, whereas imprecise knowledge of …

A multi-agent partially observable Markov decision process (MPOMDP) is a modeling
paradigm used for high-level planning of heterogeneous autonomous agents subject to …

In this article, we present a hierarchical multirate control architecture for nonlinear
autonomous systems operating in partially observable environments. Control objectives are …

In this article, we address a multi-robot planning problem in environments with partially
unknown semantics. The environment is assumed to have a known geometric structure (eg …

This paper addresses a new semantic multi-robot planning problem in uncertain and
dynamic environments. Particularly, the environment is occupied with mobile and uncertain …

We study strategy synthesis for partially observable Markov decision processes (POMDPs).
The particular problem is to determine strategies that provably adhere to (probabilistic) …

Autonomous systems are often required to operate in partially observable environments.
They must reliably execute a specified objective even with incomplete information about the …

This report presents the results of a friendly competition for formal verification and policy
synthesis of stochastic models. It also introduces new benchmarks within this category, and …

Real-life control systems are hierarchies of interacting layers; often consisting of a planning
layer, a trajectory generation layer, and a trajectory-following layer. Independently designing …