Inductive logic programming : from machine learning to software engineering
Author(s)
Bibliographic Information
Inductive logic programming : from machine learning to software engineering
（Logic programming）
MIT Press, c1996
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Note
Includes bibliographical references and index
Description and Table of Contents
Description
Although Inductive Logic Programming (ILP) is generally thought of as a research area at the intersection of machine learning and computational logic, Bergadano and Gunetti propose that most of the research in ILP has in fact come from machine learning, particularly in the evolution of inductive reasoning from pattern recognition, through initial approaches to symbolic machine learning, to recent techniques for learning relational concepts. In this book they provide an extended, up-to-date survey of ILP, emphasizing methods and systems suitable for software engineering applications, including inductive program development, testing, and maintenance. Inductive Logic Programming includes a definition of the basic ILP problem and its variations (incremental, with queries, for multiple predicates and predicate invention capabilities), a description of bottom-up operators and techniques (such as least general generalization, inverse resolution, and inverse implication), an analysis of top-down methods (mainly MIS and FOIL-like systems), and a survey of methods and languages for specifying inductive bias. Logic Programming series
Table of Contents
- Part 1 Fundamentals: problem statement and definitions
- bottom-up methods
- top-down methods
- a unifying framework. Part 2 ILP with strong bias: inductive bias
- programme induction with queries
- programme induction without queries. Part 3 Software engineering applications: development, maintenance and reuse
- testing
- a case study
- a how to FTP our software.
by "Nielsen BookData"
