Artificial Intelligence : Strategies, Applications, and Models Through SEARCH

396 Pages·2002·1.73 MB·English

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Artificial Intelligence
Strategies, Applications, and Models Through Search Second Edition
CHRISTOPHER THORNTON
BENEDICT du BOULAY
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© 1998 Intellect
ISBN: 0-8144-0470-7
All rights reserved. No part of this book may be reproduced in any form or by any means, electronic,
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Foreword
This book started out as a set of notes for a POP-11 based course at the University of Sussex on an
introduction to Artificial Intelligence. Chris Thornton designed this course around the fundamental idea
of search and devised the basic structure of the book. The course expanded to take in Prolog and was
then taught by Benedict du Boulay. He added Prolog variants to the POP-11 code. The course is now
taught by Steve Easterbrook who made several helpful comments on a draft of the book, as did Mike
Sharples. We thank Aaron Sloman and David Hogg for use of their method of writing a simple natural
language interface to a blocksworld, and Allan Ramsay and Rosalind Barrett for permission to adapt
their "weather rules" in the chapter on expert systems. We also thank various Sussex students who
pointed out errors and omissions, particularly Sarah Cole, who carefully proof-read parts of an earlier
draft. The programs were developed and the book written using the POPLOG programming
environment.
Benedict du Boulay would like to dedicate his contribution to this book to his friend Hugh Noble with
whom he started to write a text book not unlike this one but never finished it.
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Contents
1 Search-Related Techniques in AI 1
Why search 1
Objectives 2
Problem solving systems 3
State space search and problem reduction 4
Blind search and heuristic search 5
Graphs and trees 7
Organisation of the book 8
Searching for a solution path in a state space 8
Problem reduction 10
A very brief comparison of POP-11 and Prolog 13
Further reading 14
2 Simple State Space Search 15
Path-finding 15
Setting up the database 16
Setting up the path-finding function 19
The generality of search 24
Search spaces and search trees 24
Constructing an explicit representation of the search-tree 27
Search graphs 29
Node terminology 32
Backwards v. forwards searching 32
OR-tree search in Prolog 33
Reading 40
Exercises 40

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3 State Space Search 43
Introduction 43
The water jugs problem 43
Constructing successor nodes 44
The problem space 48
Searching for a solution path 50
Problem space exploration strategies 52
Breadth-first search 53
Agendas 54
Implementing depth-first search using an agenda 58
Iterative deepening 59
Water jugs in Prolog 60
Agendas in Prolog 62
Iterative deepening in Prolog 67
Reading 68
Exercises 68
Notes 69
4 Heuristic State Space Search 71
Introduction 71
The 8-puzzle 71
Constructing 8-puzzle successors 72
Heuristic search 77
Hill-climbing search 81
Heuristic breadth-first search 83
Ordered search and the A* Algorithm 86
Heuristic Search in Prolog 92
Reading 103
Exercises 103
Notes 105
5 Heuristic Search of Game Trees 107
Computing successors in the game of nim 109
Minimax evaluation 110
Worked example 116
Alpha-beta cutoffs 117
Implementing a nim-playing program 121

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Minimaxing in Prolog 125
Reading 130
Exercises 131
Notes 131
6 Problem Reduction (AND/OR-tree search) 133
Introduction 133
Problem-reduction and the Tower of Hanoi 133
Implementing arbitrary AND/OR-trees 136
Implementing the AND/OR-tree search function 138
Implementing planning as AND/OR-tree search 139
Implementing reasoning as AND/OR-tree search 141
Loops in the search space 145
Solution tree 150
The AND/OR search tree 153
State space search and problem reduction 155
Forward AND/OR-tree search 159
Terminology 161
Production systems 162
AND/OR Search in Prolog 162
Reading 168
Exercises 168
Notes 169
7 Planning (microworld search) 171
Introduction 171
Operators 173
Strategies for microworld search 174
Implementing the new search function 177
Difference tables 183
Goal interactions 183
Operators containing variables 187
Simple plan finding in Prolog 194
Reading 199
Exercises 199

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8 Parsing (search as analysis) 201
Introduction 201
Solution trees as analyses 201
Parsing 204
Data-driven (bottom-up) parsing 210
Building trees bottom-up 212
Advantages of bottom-up search 216
Backwards and forwards parsing in Prolog 216
The Prolog grammar rule formalism 219
Bottom-up parsing in Prolog 221
Why is parsing useful 225
Building a top-level interpreter 231
Meaning in Prolog 234
Reading 240
Exercises 240
9 Expert Systems (probabilistic search) 243
Introduction 243
Probabilistic rules 244
Basic probability theory 245
Fuzzy set theory 246
MYCIN-like search 247
Implementing certainty-factors 251
Forwards or backwards 256
Probabilistic search in Prolog 258
Reading 265