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 …

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 …

Bayesian neural networks (BNNs) provide a formalism to quantify and calibrate uncertainty
in deep learning. Current inference approaches for BNNs often resort to few-sample …

Weighted model counting has recently been extended to weighted model integration, which
can be used to solve hybrid probabilistic reasoning problems. Such problems involve both …

Abstract Model counting, or counting solutions of a set of constraints, is a fundamental
problem in Computer Science with diverse applications. Since exact counting is …

The tension between deduction and induction is perhaps the most fundamental issue in
areas such as philosophy, cognition and artificial intelligence. In this chapter, we survey …

The development of efficient exact and approximate algorithms for probabilistic inference is
a long-standing goal of artificial intelligence research. Whereas substantial progress has …

Weighted model integration (WMI) extends Weighted model counting (WMC) to the
integration of functions over mixed discrete-continuous domains. It has shown tremendous …

Weighted model counting (WMC) is a state-of-the-art technique for probabilistic inference in
discrete domains. WMC has recently been extended towards weighted model integration …

Exact inference in probabilistic graphical models is particularly challenging in the presence
of relational and other deterministic constraints. For discrete domains, weighted model …