Autonomous robots facing a diversity of open environments and performing a variety of tasks
and interactions need explicit deliberation in order to fulfill their missions. Deliberation is …

We present a general framework for applying machine-learning algorithms to the verification
of Markov decision processes (MDPs). The primary goal of these techniques is to improve …

Unavoidable dead-ends are common in many probabilistic planning problems, eg when
actions may fail or when operating under resource constraints. An important objective in …

Penetration testing (pentesting) is a well established method for identifying security
weaknesses, by conducting friendly attacks. Simulated pentesting automates this process …

We study the stochastic shortest path (SSP) problem in reinforcement learning with linear
function approximation, where the transition kernel is represented as a linear mixture of …

We study the problem of learning in the stochastic shortest path (SSP) setting, where an
agent seeks to minimize the expected cost accumulated before reaching a goal state. We …

We present a novel learning framework to obtain finite-state controllers (FSCs) for partially
observable Markov decision processes and illustrate its applicability for indefinite-horizon …

Quantitative formal models capture probabilistic behaviour, real-time aspects, or general
continuous dynamics. A number of tools support their automatic analysis with respect to …

Quantitative verification tools compute probabilities, expected rewards, or steady-state
values for formal models of stochastic and timed systems. Exact results often cannot be …

We consider partially observable Markov decision processes (POMDPs) with a set of target
states and an integer cost associated with every transition. The optimization objective we …