This book provides a theory, a formal language, and a practical methodology for the specification, use, and reuse of problem-solving methods. The framework developed by the author characterizes knowledge-based systems as a particular type of software architecture where the applications are developed
Problem-Solving Methods: Understanding, Description, Development, and Reuse
β Scribed by Dieter Fensel (auth.)
- Publisher
- Springer-Verlag Berlin Heidelberg
- Year
- 2000
- Tongue
- English
- Leaves
- 172
- Series
- Lecture Notes in Computer Science 1791 : Lecture Notes in Artificial Intelligence
- Edition
- 1
- Category
- Library
No coin nor oath required. For personal study only.
β¦ Synopsis
Researchers in Artificial Intelligence have traditionally been classified into two categories: the βneatiesβ and the βscruffiesβ. According to the scruffies, the neaties concentrate on building elegant formal frameworks, whose properties are beautifully expressed by means of definitions, lemmas, and theorems, but which are of little or no use when tackling real-world problems. The scruffies are described (by the neaties) as those researchers who build superficially impressive systems that may perform extremely well on one particular case study, but whose properties and underlying theories are hidden in their implementation, if they exist at all. As a life-long, non-card-carrying scruffy, I was naturally a bit suspicious when I first started collaborating with Dieter Fensel, whose work bears all the formal hallmarks of a true neaty. Even more alarming, his primary research goal was to provide sound, formal foundations to the area of knowledge-based systems, a traditional stronghold of the scruffies - one of whom had famously declared it βan artβ, thus attempting to place it outside the range of the neaties (and to a large extent succeeding in doing so).
β¦ Table of Contents
Making Assumptions for Efficiency Reasons....Pages 7-25
Front Matter....Pages 5-5
Making Assumptions for Efficiency Reasons....Pages 26-44
An Empirical Survey of Assumptions....Pages 45-59
Front Matter....Pages 41-41
A Four Component Architecture for Knowledge-Based Systems....Pages 60-77
Logics for Knowledge-Based Systems: MLPM and MCL....Pages 78-94
A Verification Framework for Knowledge-Based Systems....Pages 95-98
Front Matter....Pages 93-93
Methods for Context Explication and Adaptation....Pages 99-119
Organizing a Library of Problem-Solving Methods....Pages 120-132
Conclusions and Future Work....Pages 133-136
β¦ Subjects
Artificial Intelligence (incl. Robotics); Software Engineering
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