Problem-Solving Methods : Understanding, Description, Development, and Reuse / by Dieter Fensel.

Format:

Book

Author/Creator:

Fensel, Dieter, author.

Contributor:

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Lecture notes in computer science. Lecture notes in artificial intelligence ; 1791.

Lecture Notes in Artificial Intelligence ; 1791

Language:

English

Subjects (All):

Artificial intelligence.

Software engineering.

Artificial Intelligence.

Software Engineering.

Local Subjects:

Artificial Intelligence.

Software Engineering.

Physical Description:

1 online resource (XII, 160 pages).

Edition:

First edition 2000.

Contained In:

Springer eBooks

Place of Publication:

Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2000.

System Details:

text file PDF

Summary:

Researchers in Artificial Intelligence have traditionally been classified into two categories: the "neaties" and the "scruffies". 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 "an art", thus attempting to place it outside the range of the neaties (and to a large extent succeeding in doing so).

Contents:

What Are Problem-Solving Methods

Making Assumptions for Efficiency Reasons

What Are Problem-Solving Methods

An Empirical Survey of Assumptions

How to Describe Problem-Solving Methods

A Four Component Architecture for Knowledge-Based Systems

Logics for Knowledge-Based Systems: MLPM and MCL

A Verification Framework for Knowledge-Based Systems

How to Develop and Reuse Problem-Solving Methods

Methods for Context Explication and Adaptation

Organizing a Library of Problem-Solving Methods

Conclusions and Future Work

Conclusions and Future Work.

Other Format:

Printed edition:

ISBN:

978-3-540-44936-2

9783540449362

Access Restriction:

Restricted for use by site license.