Providing the reader with an in-depth understanding of empirical inductive logic programming approaches - which can cope with imperfect data and can be used to construct knowledge bases for solving practical problems - this book also describes several practical applications in detail and gives an overview of other current applications of inductive logic programming. The book is at the leading edge of current research: inductive logic programming is an emerging field at the intersection of machine learning and logic programming. It proposes new methods for learning relational descriptions (dealing also with imperfect data) that can be viewed as alternative methods for logic program synthesis. The book also presents a veiw on inductive logic programming as a search of the structured space of logic program clauses, which addresses the issue of search complexity and search heuristics in detail. Two empirical inductive logic programming systems (LINUS and mFOIL) are described, as well as several applications of these systems.

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