One of the most important paradigm shifts in the use of SAT solvers for solving industrial
problems has been the introduction of clause learning. Clause learning entails adding a …

The last decade witnessed an ever-increasing stream of successes in Machine Learning
(ML). These successes offer clear evidence that ML is bound to become pervasive in a wide …

The importance of explanations (XP's) of machine learning (ML) model predictions and of
adversarial examples (AE's) cannot be overstated, with both arguably being essential for the …

Characterization of bugs and attack vectors is in many practical scenarios as important as
their finding. Recently, Girol et. al. have introduced the concept of robust reachability, which …

Boolean Satisfiability (SAT) is arguably the archetypical NP-complete decision problem.
Progress in SAT solving algorithms has motivated an ever increasing number of practical …

The practical success of Boolean Satisfiability (SAT) solvers stems from the CDCL (Conflict-
Driven Clause Learning) approach to SAT solving. However, from a propositional proof …

Motivated by the need to improve the scalability of Intel's in-house Static Timing Analysis
(STA) tool, we consider the problem of enumerating all the solutions of a single-output …

Two types of explanations have been receiving increased attention in the literature when
analyzing the decisions made by classifiers. The first type explains why a decision was …

Existing automated testing frameworks require multiple observations to be jointly diagnosed
with the purpose of identifying common fault locations. This is the case for example with …

Model-Based Diagnosis (MBD) is a principled and domain-independent way of analyzing
why a system under examination is not behaving as expected. Given an abstract description …