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.
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.
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
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