Neuro-symbolic AI is an emerging subfield of Artificial Intelligence that brings together two
hitherto distinct approaches.” Neuro” refers to the artificial neural networks prominent in …

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 tension between deduction and induction is perhaps the most fundamental issue in
areas such as philosophy, cognition and artificial intelligence (AI). The deduction camp …

In this paper, we study the problem of automatically verifying higher-order masking
countermeasures. This problem is important in practice, since weaknesses have been …

Abstract Propositional model counting (# SAT), ie, counting the number of satisfying
assignments of a propositional formula, is a problem of significant theoretical and practical …

Many computational problems in modern society account to probabilistic reasoning,
statistics, and combinatorics. A variety of these real-world questions can be solved by …

The continued development of efficient automated decision procedures has spurred the
resurgence of research on symbolic execution over the past decade. Researchers have …

Automated program repair reduces the manual effort in fixing program errors. However,
existing repair techniques modify a buggy program such that it passes given tests. Such …

We study the complexity of computing a query on a probabilistic database. We consider
unions of conjunctive queries, UCQ, which are equivalent to positive, existential First Order …

Weighted model counting (WMC) on a propositional knowledge base is an effective and
general approach to probabilistic inference in a variety of formalisms, including Bayesian …