ILP and ILP related books

Book announcement

"Knowledge Acquisition and Machine Learning - Theory, Methods and Applications" K. Morik, S. Wrobel, J.U.Kietz, and W. Emde Academic Press 1993

ISBN: 0-12-506230-3, 350 pages, 34.95 pounds

The book shows how incorporating learning algorithms into a knowledge acquisition environment provides new work-share between system and user, assisting the user in both setting up a learning task using the knowledge acquisition environment and supporting knowledge acquisition and knowledge maintenance using learning algorithms. The book reports on BLIP and MOBAL, fully operational systems which integrate knowledge acquisition, maintenance and learning in a restricted predicate logic. The book is practically oriented. Theoretical results have been used and and tested in real-world applications of different complexity and size.

Book announcement

"INDUCTIVE LOGIC PROGRAMMING: Techniques and Applications" Nada Lavrac and Saso Dzeroski Ellis Horwood (Simon & Schuster), 1994 (Ellis Horwood Series in Artificial Intelligence)

ISBN: 0-13-457870-8, 310 pages, 39.95 pounds (67.95 dollars)

Keywords: artificial intelligence, applications, databases, deductive databases, induction, learning, logic, logic programming, machine learning, knowledge discovery in databases

The book is an introduction to inductive logic programming (ILP), a research area at the intersection of inductive machine learning and logic programming. This field aims at a formal framework and practical algorithms for inductively learning relational descriptions in the form of logic programs. ILP is of interest to inductive machine learning researchers as it significantly extends the usual attribute-value respresentation and consequently enlarges the scope of machine learning applications; it is also of interest to logic programming researchers as it extends the basically deductive framework of logic programming with the use of induction.

The book consists of four parts. Part I is an introduction to the field of ILP. Part II describes in detail several empirical ILP techniques and their implementations. Part III presents the techniques for handling imperfect data in ILP, whereas Part IV gives an overview of several ILP applications.

The book serves two main purposes. On the one hand, it can be used as a course book on ILP since it provides an easy-to-read introduction to ILP (Chapters 1-3), an overview of empirical ILP systems (Chapter 4), discusses ILP as search of refinement graphs (Chapter 7), analyses the sources of imperfect/noisy data and the mechanisms for handling noise (Chapter 8) and gives an overview of several interesting applications of ILP (Chapter 14). On the other hand, the book is a guide/reference for an in-depth study of specific empirical ILP techniques, i.e., using attribute-value learners in an ILP framework and specialization techniques based on FOIL (Chapters 5-6,9-10) and their applications in medicine, mesh design and learning of qualitative models (Chapters 11-13).

The book will be of interest to engineers, researchers and graduate students in the field of artificial intelligence and database methodology, in particular in machine learning, logic programming, software engineering, deductive databases, and knowledge discovery in databases. Basic knowledge of artificial intelligence and logic would be helpful, but is not a prerequisite.

Book announcement

INDUCTIVE LOGIC PROGRAMMING: From Machine Learning to Software Engineering Francesco Bergadano and Daniele Gunetti

Logic Programming series, SBN 0-262-02393-8, pp. 240, $37.50

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.

Francesco Bergadano is Professor, Department of Mathematics, University of Messina. Daniele Gunetti is Researcher, Department of Informatics, University of Torino.

INDUCTIVE LOGIC PROGRAMMING: From Machine Learning to Software Engineering

     Series Foreword  ix 
     Preface  xi 
     Introduction  1 

   part I       Fundamentals 9 

        2 Problem Statement and Definitions 11 
          2.1 Logic Programs and Their Examples 11 
          2.2 The ILP Problem  13 
          2.3 Incremental Systems and Queries  22 
          2.4 Identifying Logic Programs in the Limit  27 

        3 Bottom-up Methods  33 
          3.1 Plotkin's Least General Generalization  34 
          3.2 Inverse Resolution  45 
          3.3 Inverse Implication  60 

        4 Top-Down Methods  77 
          4.1 Shapiro's Model Inference System  79 
          4.2 FOIL  85 

        5 A Unifying Framework  91 
          5.1 Theorem Proving with Inverse Resolution  91 
          5.2 Extensional Top-Down Methods Revisited  99 
          5.3 Example  103 

   part II      ILP with Strong Bias  107 

        6 Inductive Bias  109 
          6.1 Refinement Operators  111 
          6.2 Clause Templates  115 
          6.3 Domain Theories and Grammars  118 
          6.4 Bias in Bottom-up Systems  125 
          6.5 Clause Sets  129 

        7 Program Induction with Queries  137 
          7.1 The FILP System  139 
          7.2 Justification of extensionality and problems  142 
          7.3 Completing examples before learning  144 
          7.4 Discussion  147 

        8 Program Induction without Queries  149 
          8.1  The Induction Procedure  150 
          8.2 Example  153 
          8.3 Properties of the Induction Procedure  154 
          8.4 A Simplified Implementation  157 
          8.5 Discussion  163 

   part III    Software Engineering Applications  165 

        9 Development, Maintenance, and Reuse  167 
          9.1 Introduction  169 
          9.2 Inductive Logic Programming Languages  171 
          9.3 The Inductive Software Process  174 
          9.4 From Inductive Learning to Inductive Programming  180 

        10 Testing  185 
          10.1 Introduction to Testing  186 
          10.2 Induction and Testing Compared  187 
          10.3 Inductive Test Case Generation  189 
          10.4 Examples  191 

        11 A Case Study  199 
          11.1 Synthesizing Insert   200 
          11.2 Testing Insert   209 

     A How to FTP Our Software  217 
     Bibliography  219 
     Index  236 
Beside the usual ways, you can order the book directly at the MIT Press www home page and follow the ORDER link.