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    1. Data och IT
    2. Systemvetenskap och AI
    3. Artificiell intelligens

    Engineering Artificial Intelligence Software

    AvDerek Partridge

    Inbunden, Engelska, 1992

    352 kr

    Tillfälligt slut

    Beskrivning

    Aimed at the computer-literate person wishing to find out about the reality of exploiting the promise of artificial intelligence (AI) in practical, maintainable software systems, this text tries to avoid the hype usually associated with the subject. Instead, it presents the realities, the problems, the current state of the art, and future directions. Throughout, the reader will find a comprehensive and coherent examination of the many problems that engineering AI software involves, as well as a consideration of the alternative routes to solution of these problems.

    Produktinformation

    • Utgivningsdatum:1992-05-01
    • Format:Inbunden
    • Språk:Engelska
    • Antal sidor:200
    • Förlag:Intellect
    • ISBN:9781871516067

    Utforska kategorier

    • Artificiell intelligens inom Data och IT

    Innehållsförteckning

    • Preface1 Introduction to Computer Software   11.1 Computers and software systems1.2 An introduction to software engineering1.3 Bridges and buildings versus software systems1.4 The software crisis1.5 A demand for more software power1.6 Responsiveness to human users1.7 Software systems in new types of domains1.8 Responsiveness to dynamic usage environments1.9 Software systems with self-maintenance capabilities1.10 A need for AI systems2 AI Problems and Conventional SE Problems   272.1 What is an AI problem?2.2 Ill-defined specifications2.3 Correct versus 'good enough' solutions2.4 It's the HOW not the WHAT2.5 The problem of dynamics2.6 The quality of modular approximations2.7 Context-free problems3 Software Engineering Methodology   363.1 Specify and verify - the SAV methodology3.2 The myth of complete specification3.3 What is verifiable?3.4 Specify and test - the SAT methodology3.5 The strengths3.6 Testing for reliability3.7 The weaknesses3.8 What are the requirements for testing?3.9 What's in a specification?3.10 Prototyping as a link4 An Incremental and Exploratory Methodology   564.1 Classical methodology and AI problems4.2 The RUDE cycle4.3 How do we start?4.4 Malleable software4.5 AI muscles on a conventional skeleton4.6 How do we proceed?4. 7 How do we finish?4.8 The question of hacking4.9 Conventional paradigms5 New Paradigms for System Engineering   795.1 Automatic programming5.2 Transformational implementation5.3 The "new paradigm" of Balzer, Cheatham and Green5.4 Operational requirements of Kowalski5.5 The POLITE methodology6 Towards a Discipline of Exploratory Programming   1096.1 Reverse engineering6.2 Reusable software6.3 Design knowledge6.4 Stepwise abstraction6.5 The problem of 'decompiling'6.6 Controlled modification6.7 Structured growth7 Machine Learning: Much Promise, Many Problems   1417.1 Self-adaptive software7.2 The promise of increased software power7.3 The threat of increased software problems7.4 The state of the art in machine learning7.5 Practical machine learning examples8 Expert Systems: The Success Story    1588.1 Expert systems as AI software8.2 Engineering expert systems8.3 The lessons of expert systems for engineering AI software9 AI into Practical Software   1709.1 Support environments9.2 Reduction of effective complexity9.3 Moderately stupid assistance9.4 An engineering toolbox9.5 Self-reflective software9.6 Overengineering software10 Summary and What the Future Holds   193References   200Index   206