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 …

In recent years, neural systems have demonstrated highly effective learning ability and
superior perception intelligence. However, they have been found to lack effective reasoning …

The study and understanding of human behaviour is relevant to computer science, artificial
intelligence, neural computation, cognitive science, philosophy, psychology, and several …

Markov Logic Networks (MLNs), which elegantly combine logic rules and probabilistic
graphical models, can be used to address many knowledge graph problems. However …

In this chapter, we provide a detailed description of the Markov logic representation. We
begin by providing background on first-order logic and probabilistic graphical models and …

This paper proposes a unified approach to learning from constraints, which integrates the
ability of classical machine learning techniques to learn from continuous feature-based …

Entity resolution is the problem of determining which records in a database refer to the same
entities, and is a crucial and expensive step in the data mining process. Interest in it has …

Markov Logic Networks (MLNs) have emerged as a powerful framework that combines
statistical and logical reasoning; they have been applied to many data intensive problems …

Unifying first-order logic and probability is a long-standing goal of AI, and in recent years
many representations combining aspects of the two have been proposed. However …

We address the problem of visual event recognition in surveillance where noise and missing
observations are serious problems. Common sense domain knowledge is exploited to …