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Knowledge engineering : building cognitive assistants for evidence-based reasoning
Tecuci G., Marcu D., Boicu M., Schum D., Cambridge University Press, New York, NY, 2016. 480 pp.  Type: Book (978-1-107122-56-7)
Date Reviewed: Sep 1 2017

To review this book, and do it any justice, one must begin with a few “externalities.” First, the book is exceptionally beautiful in terms of production: well bound, beautiful in color, and very well produced in every aspect. The colors and their use are stunning. I will also add below that it is well written, but that gets ahead of my task. Second, the title and subtitle are slightly misleading. Knowledge engineering is generally defined as: “a field within artificial intelligence that develops knowledge-based systems. Such systems are computer programs that contain large amounts of knowledge, rules, and reasoning mechanisms to provide solutions to real-world problems” [1]. However, the book’s subtitle places this in a much narrower context of “building cognitive assistants” and furthermore, not “just any” cognitive assistant, but those designed specifically for “evidence-based reasoning.”

The reader should view these observations as somewhat of a caveat. The book is not what one might at first assume from the basic title. Yet, despite this (perhaps) philosophical (or semantic) difference, the narrowing of it via the subtitle does provide needed guidance for a first-time perusal.

And finally, a third point: the book is not for the mathematically challenged. It is not trivial to read and comprehend--it’s not the kind of book that one reads on the bus.

However, with that, I would venture to state that, for those for whom this book is a good match, it presents a new level that any author coming after will have to meet; the book is very well done.

Like many academic books, the table of contents is exceedingly detailed, much more so than I find comfortable. It has four levels, while I believe that two would have made it more usable for the vast majority of readers. It’s not an important point, but a slight pity when everything else is so pretty. On the other hand, the appendix and index are proper and fit.

Interestingly, I would match the book with another recent volume that I admired [2]. This pairing is not for the basic knowledge they both convey, but for the concepts of evidence, which are mission-critical to both. Although, in this book, the types of evidence being examined are much broader.

I liked the title of the second chapter, “Evidence-based Reasoning: Connecting the Dots,” because this concept, as clear as it ought to be, is so frequently neglected. It sounds simplistic, but “connecting the dots” is not a trivial issue, and a chapter devoted to it is quite significant and welcome. Equally, the content of the chapter is well planned and executed. As a person with intelligence experience, such questions presented as “Which evidence dots can be believed?”; “Which evidence dots should be considered?”; and “Which evidence dots should we try to connect?” are all critical, and so often neglected, issues. I really liked this chapter. On a purely personal note, I would have to consider this chapter the heart of the book, at least for me, and the most important. In many respects, I would almost say that even if cognitive assistants are not your game, this chapter alone makes the book a worthwhile purchase, certainly for any CS library, corporate or academic.

The space limitations of a book review do not allow discussion here of all of the various chapters individually. They are all well done or reasonably well done. I am not overly fond of chapter 4, as I still think nobody has yet compared to Rubinstein’s Concepts in problem solving, which is a very old, but classic book [3]. It is hard to compare to the best in class, but that seems unavoidable here.

In examining the table of contents of the book, one finds that it contains a considerable breadth of topics. I would venture so far as to say that any book coming out after this book on embedded systems, cybernetics, or reactive systems could do itself no better turn than to base its own table of contents on the analysis the authors here provide with theirs. This alone would be (almost) a breakthrough in understanding such systems; luckily, the authors continue their good work throughout.

If this subject area is relevant to you, do not skip this book. This is really hard stuff and the authors carry it off very well.

In summary, I am pleased to recommend this book. It appears to be a good textbook for a graduate-level course. The publishers did an exceptional job with paper/print/binding quality, at an acceptable price.

More reviews about this item: Amazon

Reviewer:  Mordechai Ben-Menachem Review #: CR145521 (1711-0713)
1) What is knowledge engineering? IGI Global. (08/28/2017).
2) Elmes, G.; Roedl, G.; Conley, J. (Eds.). Forensic GIS. Springer, New York, NY, 2014. See CR Review No. 143079 (1505-0352).
3) Rubinstein, M.; Pfeiffer, K. Concepts in problem solving. Prentice Hall, Englewood Cliffs, NJ, 1980.
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