Fensel D. Problem-Solving Methods. Understanding, Description, Development, and Reuse

Springer, 2000. — 173 p. — (Lecture Notes in Computer Science 1791 : Lecture Notes in Artificial Intelligence). — ISBN: 9783540678168, 3540678166.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). However, even an unreconstructed scruffy such as myself can recognize a good neaty when he comes across one. What Dieter has managed to produce with his research on problem solving methods is what all neaties hope to do, but few achieve: a rigorous and useful theory, which can be used analytically, to explain a range of phenomena in the (real) world and synthetically, to support the development of robust and well defined artifacts.
Specifically, this book provides a theory, a formal language and a practical methodology to support the specification, use, and reuse of problem solving methods. Thus, knowledge engineering is not characterized as an art any longer, but as an engineering discipline, where artifacts are constructed out of reusable components, according to well-understood, robust development methods. The value of the framework proposed by Dieter is illustrated extensively, by showing its application to complex knowledge engineering tasks - e.g., diagnosis and design - and by applying it to the specification of libraries with both scope and depth (i.e., both usable and reusable). Another important contribution of this book is that it clarifies the similarities and the differences between knowledge-based and 'conventional' systems. The framework proposed by Dieter characterizes knowledge-based systems as a particular type of software architecture, where applications are developed by integrating generic task specifications, problem solving methods, and domain models by means of formally defined adapters. The latter can be used to map the terminologies used by the different system components, and also to formally introduce the assumptions on the domain knowledge required by an intelligent problem solver. This notion of assumption is central to Dieter's characterization of knowledge-based systems: these are defined as systems that make assumptions for the sake of efficiency. Thus, Dieter is able to build a continuum of assumption-making systems, ranging from “weak” search methods to “strong”, task-specific methods. As a result we can now see clearly the relationship between all these various classes of algorithms, which have traditionally been treated as distinct.
In conclusion, I believe this is the first 'real' theory of knowledge engineering to come out of several decades of research in this area. It describes the class of systems we are talking about, how to model them and how to develop them. I also believe that it is very important that this theory has come out at a time when the explosion of internet based services is going to provide unprecedented opportunities for deploying and sharing knowledge-based services. I advise anybody who plans to be a player in this area to read this book and learn what robust knowledge engineering is about.Section I What Are Problem-Solving Methods
Making Assumptions for Efficiency Reasons
An Empirical Survey of Assumptions
Section II 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
Section III 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