Artificial Intelligence Techniques In Prolog

Artificial Intelligence Techniques in Prolog introduces the reader to the use of well-established algorithmic techniques in the field of artificial intelligence (AI), with Prolog as the implementation language. The techniques considered cover general areas such as search, rule-based systems, and truth maintenance, as well as constraint satisfaction and uncertainty management. Specific application domains such as temporal reasoning, machine learning, and natural language are also discussed. Comprised of 10 chapters, this book begins with an overview of Prolog, paying particular attention to Prolog terms and rules (and Prolog facts as special cases); unification; the and-or computation tree induced by a Prolog program and a query; the depth-first, left-to-right traversal of that tree by the standard Prolog interpreter; and built-in predicates such as unification and equality. Subsequent chapters deal with search and representation of graphs in Prolog; backward-chaining methods; truth maintenance systems; and constraint satisfaction. Reasoning with uncertainty, planning and temporal reasoning, and machine learning are also tackled. The book concludes with an assessment of natural language processing and some of the linguistic notions that are easily encoded in Prolog. This monograph will be of interest to both students and practitioners in the fields of AI and computer science.

This edition discusses natural language processing with grammar rules, planning and machine learning, and includes coverage of meta-programming, meta-interpreters and object-oriented programming in Prolog.

We have added new material to Chapter 3 to give an account of up-to-date programming techniques using accumulators and difference structures. Chapter 8 contains some new information on syntax errors. Operator precedences are now compatible with the most widely-used implementations. We have made further reorganisations and improvements in presentation, and have corrected a number of minor errors. We thank the many people who brought typographical errors in the previous edition to our attention, and we thank A.R.C. for careful proofreading. Cambridge, England W.F.C. January,1987 C.S.M. PREFACE TO THE SECOND EDITION (1984) Since the first publishing of Programming in Prolog in 1981, Prolog has continued to attract an unexpectedly great deal of interest in the computer science community and is now seen as a potential basis for an important new generation of programming languages and systems. We hope that Programming in Prolog has partially satisfied the increasing need for an easy, yet comprehensive introduction to the language as a tool for practical programming. In this second edition we have taken the opportunity to improve the presentation and to correct various minor errors in the original. We thank the many people who have given us suggestions for corrections and improvement. Cambridge, England W.F.C.

Prolog Programming for Artificial Intelligence Third edition Ivan Bratko The third edition of this best-selling guide to Prolog and Artificial Intelligence has been updated to include key developments in the field while retaining its lucid approach to these topics. Divided into two parts, the first part of the book introduces the programming language Prolog, while the second part teaches Artificial Intelligence using Prolog as a tool for the implementation of AI techniques. Prolog has its roots in logic, however the main aim of this book is to teach Prolog as a practical programming tool. This text therefore concentrates on the art of using the basic mechanisms of Prolog to solve interesting problems. The third edition has been fully revised and extended to provide an even greater range of applications, which further enhance its value as a self-contained guide to Prolog, AI or AI Programming for students and professional programmers alike.Features * Combined approach to Prolog and AI allows flexibility for learning and teaching * Provides a thorough representation of AI, emphasizing practical techniques and Prolog implementations * Prolog programs for use in projects and research are available for download on the World Wide Web.New for this edition: * Constraint Logic Programming * Qualitative Reasoning * Inductive Logic Programming * The addition of belief networks for handling uncertainty * A major update on machine learning * Additional techniques for improving program efficiency * Meta-programming is updated to show how Prolog can be used to implement other languages (including object-oriented programming) * A new Companion Web Site will contain further teaching materials and updates Author: Professor Ivan Bratko leads the AI groups in the Faculty of Computer and Information Science at both Ljubljana University and the Jozef Stefan Institute in Slovenia. He has taught Prolog world-wide as well as applying Prolog in medical expert systems, robot programming, qualitative modelling and computer chess research.

The fourth edition of this best-selling guide to Prolog and Artificial Intelligence has been updated to include key developments in the field while retaining its lucid approach to these topics. New and extended topics include Constraint Logic Programming, abductive reasoning and partial order planning. Divided into two parts, the first part of the book introduces the programming language Prolog, while the second part teaches Artificial Intelligence using Prolog as a tool for the implementation of AI techniques. This textbook is meant to teach Prolog as a practical programming tool and so it concentrates on the art of using the basic mechanisms of Prolog to solve interesting problems. The fourth edition has been fully revised and extended to provide an even greater range of applications, making it a self-contained guide to Prolog, AI or AI Programming for students and professional programmers.

Approaches the subject by applying the format used in successful language courses. Offers a comprehensive exhibition of Prolog programming techniques in four stages--declarative, procedural, advanced and meta-programming. Presents simple and efficient implementation of logical negation and quantified goals which are necessary in expert systems. The dynamics of these new features are shown in the construction of a multilingual expert system shell that supports negative and quantified queries as well as subtypes. The easy-to-follow tutorial style and numerous fully-solved exercises facilitate understanding. Comes with 3.5 inch disk containing all programs in the book.