Precisely when the success of artificial intelligence (AI) sub-symbolic techniques makes
them be identified with the whole AI by many non-computer-scientists and non-technical …

Autonomous service robots will have to understand vaguely described tasks, such as “set
the table” or “clean up”. Performing such tasks as intended requires robots to fully, precisely …

Inductive logic programming (ILP) is a form of machine learning. The goal of ILP is to induce
a hypothesis (a set of logical rules) that generalises training examples. As ILP turns 30, we …

Both logic programming in general and Prolog in particular have a long and fascinating
history, intermingled with that of many disciplines they inherited from or catalyzed. A large …

Since its birth, the field of Probabilistic Logic Programming has seen a steady increase of
activity, with many proposals for languages and algorithms for inference and learning. This …

Many complex systems, ranging from social, industrial, economics, financial, educational, to
military, require that we obtain high-quality solutions to combinatorial problems. Linear …

With the Semantic Web data standards defined, more applications demand inference
engines in providing support for intelligent processing of the Semantic Web data. Rule …

Learning probabilistic logic programming languages is receiving an increasing attention,
and systems are available for learning the parameters (PRISM, LeProbLog, LFI-ProbLog …

This chapter is a survey of the history and the main concepts of Datalog. We begin with an
introduction to the language and its use for database definition and querying. We then look …

Modern search techniques either cannot efficiently incorporate human feedback to refine
search results or cannot express structural or semantic properties of desired code. The key …