Systems for symbolic event recognition infer occurrences of events in time using a set of
event definitions in the form of first-order rules. The Event Calculus is a temporal logic that …

Searching the hypothesis space bounded below by a bottom clause is the basis of several
state-of-the-art ILP systems (eg Progol, Aleph). These systems use refinement operators …

Most existing learning methods for Markov Logic Networks (MLNs) use batch training, which
becomes computationally expensive and eventually infeasible for large datasets with …

The game of chess has been a major testbed for research in artificial intelligence, since it
requires focus on intelligent reasoning. Particularly, several challenges arise to machine …

Abstract Theory revision systems are designed to improve the accuracy of an initial theory,
producing more accurate and comprehensible theories than purely inductive methods. Such …

Rich representation inherited from computational logic makes logic-based machine learning
a competent method for application domains involving relational background knowledge …

Statistical relational learning (SRL) is an emerging area of research that addresses the
problem of learning from noisy structured/relational data. Markov logic networks (MLNs) …

Traditionally, machine learning algorithms assume that training data is provided as a set of
independent instances, each of which can be described as a feature vector. In contrast …

Many real-world problems involve data that both have complex structures and uncertainty.
Statistical relational learning (SRL) is an emerging area of research that addresses the …

Artificial Intelligence is concerned with building computer programs that solve problems
which would require intelligence if solved by a human. As intelligence requires learning, to …