We present two novel algorithms for learning formulas in Linear Temporal Logic (LTL) from
examples. The first learning algorithm reduces the learning task to a series of satisfiability …

We study a class of reinforcement learning tasks in which the agent receives its reward for
complex, temporally-extended behaviors sparsely. For such tasks, the problem is how to …

In this paper, we propose and address the problem of supervisor obfuscation against
actuator enablement attack, in a common setting where the actuator attacker can eavesdrop …

In this paper, we investigate the security approach of synthesizing resilient supervisors
against combined actuator and sensor attacks, for the subclass of cyber-physical systems …

We address the problem of learning human-interpretable descriptions of a complex system
from a finite set of positive and negative examples of its behavior. In contrast to most of the …

We present an ef cient approach to learn past-time linear temporal logic formulas (PLTL)
from a set of propositional variables and a sample of nite traces over those variables. The ef …

The mining of models from data finds widespread use in industry. There exists a variety of
model inference methods for perfectly deterministic behaviour, however, in practice, the …

We explore an approach to model learning that is based on using satisfiability modulo
theories (SMT) solvers. To that end, we explain how DFAs, Mealy machines and register …

Abstract Regular Model Checking is a popular verification technique where large and even
infinite sets of program configurations can be encoded symbolically by finite automata …

The objective of this thesis is to explore automata learning, which is an umbrella term for
techniques that derive finite automata from external information sources, in the areas of …