Engineering Artificial Intelligence Software

Preface

1 Introduction to Computer Software   1

1.1 Computers and software systems
1.2 An introduction to software engineering
1.3 Bridges and buildings versus software systems
1.4 The software crisis
1.5 A demand for more software power
1.6 Responsiveness to human users
1.7 Software systems in new types of domains
1.8 Responsiveness to dynamic usage environments
1.9 Software systems with self-maintenance capabilities
1.10 A need for AI systems

2 AI Problems and Conventional SE Problems   27

2.1 What is an AI problem?
2.2 Ill-defined specifications
2.3 Correct versus 'good enough' solutions
2.4 It's the HOW not the WHAT
2.5 The problem of dynamics
2.6 The quality of modular approximations
2.7 Context-free problems

3 Software Engineering Methodology   36

3.1 Specify and verify - the SAV methodology
3.2 The myth of complete specification
3.3 What is verifiable?
3.4 Specify and test - the SAT methodology
3.5 The strengths
3.6 Testing for reliability
3.7 The weaknesses
3.8 What are the requirements for testing?
3.9 What's in a specification?
3.10 Prototyping as a link

4 An Incremental and Exploratory Methodology   56

4.1 Classical methodology and AI problems
4.2 The RUDE cycle
4.3 How do we start?
4.4 Malleable software
4.5 AI muscles on a conventional skeleton
4.6 How do we proceed?
4. 7 How do we finish?
4.8 The question of hacking
4.9 Conventional paradigms

5 New Paradigms for System Engineering   79

5.1 Automatic programming
5.2 Transformational implementation
5.3 The "new paradigm" of Balzer, Cheatham and Green
5.4 Operational requirements of Kowalski
5.5 The POLITE methodology

6 Towards a Discipline of Exploratory Programming   109

6.1 Reverse engineering
6.2 Reusable software
6.3 Design knowledge
6.4 Stepwise abstraction
6.5 The problem of 'decompiling'
6.6 Controlled modification
6.7 Structured growth

7 Machine Learning: Much Promise, Many Problems   141

7.1 Self-adaptive software
7.2 The promise of increased software power
7.3 The threat of increased software problems
7.4 The state of the art in machine learning
7.5 Practical machine learning examples

8 Expert Systems: The Success Story    158

8.1 Expert systems as AI software
8.2 Engineering expert systems
8.3 The lessons of expert systems for engineering AI software

9 AI into Practical Software   170

9.1 Support environments
9.2 Reduction of effective complexity
9.3 Moderately stupid assistance
9.4 An engineering toolbox
9.5 Self-reflective software
9.6 Overengineering software

10 Summary and What the Future Holds   193

References   200
Index   206