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9780201403756

ICSS.Bratko Prolog Programming f_p3

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  • ISBN13:

    9780201403756

  • ISBN10:

    0201403757

  • Edition: 3rd
  • Format: Paperback
  • Copyright: 2000-09-08
  • Publisher: Pearson
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List Price: $79.80

Summary

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.

Table of Contents

Foreword xiii
Preface xvii
PART I The Prolog Language 1(236)
Introduction to Prolog
3(22)
Defining relations by facts
3(5)
Defining relations by rules
8(6)
Recursive rules
14(4)
How Prolog answers questions
18(5)
Declarative and procedural meaning of programs
23(2)
Syntax and Meaning of Prolog Programs
25(36)
Data objects
26(7)
Matching
33(5)
Declarative meaning of Prolog programs
38(3)
Procedural meaning
41(5)
Example: monkey and banana
46(4)
Order of clauses and goals
50(7)
The relation between Prolog and logic
57(4)
Lists, Operators, Arithmetic
61(27)
Representation of lists
61(3)
Some operations on lists
64(10)
Operator notation
74(6)
Arithmetic
80(8)
Using Structures: Example Programs
88(26)
Retrieving structured information from a database
88(4)
Doing data abstraction
92(2)
Simulating a non-deterministic automation
94(4)
Travel agent
98(5)
The eight queens problem
103(11)
Controlling Backtracking
114(18)
Preventing backtracking
114(5)
Examples using cut
119(5)
Negation as failure
124(3)
Problems with cut and negation
127(5)
Input and Output
132(15)
Communication with files
132(3)
Processing files of terms
135(5)
Manipulating characters
140(1)
Constructing and decomposing atoms
141(3)
Reading programs
144(3)
More Built-in Predicates
147(24)
Testing the type of terms
147(8)
Constructing and decomposing terms: = .., functor, arg, name
155(5)
Various kinds of equality and comparison
160(1)
Database manipulation
161(5)
Control facilities
166(1)
bagof, setof, and findall
167(4)
Programming Style and Technique
171(26)
General principles of good programming
171(2)
How to think about Prolog programs
173(3)
Programming style
176(3)
Debugging
179(2)
Improving efficiency
181(16)
Operations on Data Structures
197(27)
Sorting lists
197(5)
Representing sets by binary trees
202(6)
Insertion and deletion in binary dictionary
208(5)
Displaying trees
213(2)
Graphs
215(9)
Advanced Tree Representations
224(13)
The 2-3 dictionary
224(7)
AVL-tree: an approximately balanced tree
231(6)
PART II Prolog in Artificial Intelligence 237(410)
Basic Problem-Solving Strategies
239(21)
Introductory concepts and examples
239(5)
Depth-first search and iterative deepening
244(6)
Breadth-first search
250(5)
Analysis of basic search techniques
255(5)
Best-First Heuristic Search
260(32)
Best-first search
260(10)
Best-first search applied to the eight puzzle
270(4)
Best-first search applied to scheduling
274(6)
Space-saving techniques for best-first search
280(12)
Problem Decomposition and AND/OR Graphs
292(27)
AND/OR graph representation of problems
292(4)
Examples of AND/OR representation
296(4)
Basic AND/OR search procedures
300(5)
Best-first AND/OR search
305(14)
Constraint Logic Programming
319(28)
Constraint satisfaction and logic programming
319(5)
CLP over real numbers: CLP(R)
324(5)
Scheduling with CLP
329(7)
A simulation program with constraints
336(5)
CLP over finite domains: CLP(FD)
341(6)
Knowledge Representation and Expert Systems
347(36)
Functions and structure on an expert system
347(2)
Representing knowledge with it-then rules
349(3)
Forward and backward chaining in rule-based systems
352(6)
Generating explanation
358(2)
Introducing uncertainty
360(3)
Belief networks
363(9)
Semantic networks and frames
372(11)
An Expert System Shell
383(30)
Knowledge representation format
383(5)
Designing the inference engine
388(4)
Implementation
392(18)
Concluding remarks
410(3)
Planning
413(29)
Representing actions
413(5)
Deriving plans by means-ends analysis
418(4)
Protecting goals
422(2)
Procedural aspects and breadth-first regime
424(3)
Goal regression
427(3)
Combining means-ends planning with best-first heuristic
430(4)
Uninstantiated actions and partial-order planning
434(8)
Machine Learning
442(42)
Introduction
442(1)
The Problem of learning concepts from examples
443(5)
Learning relational descriptions: a detailed examples
448(6)
Learning simple if-then rules
454(8)
Induction of decision trees
462(7)
Learning from noisy data and tree pruning
469(7)
Success of learning
476(8)
Inductive Logic Programming
484(36)
Introduction
484(3)
Constructing Prolog programs from examples
487(13)
Program HYPER
500(20)
Qualitative Reasoning
520(35)
Common sense, qualitative reasoning and naive physics
520(5)
Qualitative reasoning about static systems
525(4)
Qualitative reasoning about dynamic systems
529(7)
A qualitative simulation program
536(11)
Discussion of the qualitative simulation program
547(8)
Languages Processing with Grammar Rules
555(26)
Grammar rules in Prolog
555(8)
Handling meaning
563(5)
Defining the meaning of natural language
568(13)
Game Playing
581(31)
Two-person, perfect-information games
581(2)
The minimax principle
583(3)
The alpha-beta algorithm; an efficient implementation of minimax
586(4)
Minimax-based programs: refinements and limitations
590(2)
Pattern knowledge and the mechanism of `advice'
592(4)
A chess endgame program in Advice Language 0
596(16)
Meta-Programming
612(35)
Meta-programs and meta-interpreters
612(1)
Prolog meta-interpreters
613(5)
Explanation-based generalization
618(6)
Object-oriented programming
624(7)
Pattern-directed programming
631(7)
A simple theorem prove as a pattern-directed program
638(9)
Appendix A: Some Differences Between Prolog Implementations 647(2)
Appendix B: Some Frequently Used Predicates 649(3)
Solutions to Selected Exercises 652(19)
Index 671

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