Time series data is increasingly used in a wide range of fields, and it is often relied on in
crucial applications and high-stakes decision-making. For instance, sensors generate time …

Formal specifications play an essential role in the life-cycle of modern systems, both at the
time of their design and during their operation. Despite their importance, formal …

In runtime verification, manually formalizing a specification for monitoring system executions
is a tedious and error-prone process. To address this issue, we consider the problem of …

Machine learning techniques using neural networks have achieved promising success for
time-series data classification. However, the models that they produce are challenging to …

Imitation learning methods have demonstrated considerable success in teaching
autonomous systems complex tasks through expert demonstrations. However, a limitation of …

Traffic systems, where human and autonomous drivers interact, are a very relevant instance
of complex systems and produce behaviors that can be regarded as trajectories over time …

Motivated by applications in robotics, we consider the task of synthesizing linear temporal
logic (LTL) specifications based on examples and natural language descriptions. While LTL …

Despite the structure of road environments, imposed via geometry and rules, traffic flows
exhibit complex multiagent dynamics. Reasoning about such dynamics is challenging due to …

In several domains, systems generate continuous streams of data during their execution,
including meaningful telemetry information, that can be used to perform tasks like …

The problem of passive learning of linear temporal logic formulae consists in finding the best
explanation for how two sets of execution traces differ, in the form of the shortest formula that …