Reviews for Logic Programming and Knowledge Representation, Vol. 147

The average rating for Logic Programming and Knowledge Representation, Vol. 147 based on 2 reviews is 3 stars.

Review # 1 was written on 2016-10-23 00:00:00 Norbert Washington (First, despite what it says on the sidebar, this book's author is likely not the same Bruce Mills who's part of the English faculty at Kalamazoo.) There's a nice quote from this book that jumps out at me regularly, and which dovetails with Joel Spolsky's view that a good programmer be able to "view a problem at several levels of abstraction simultaneously:" But to say that int is integer arithmetic with bounds and overflow conditions is to say that it is not integer arithmetic. Abstractions leak, in other words. The book, besides containing philosophical wisdom like the bit above, covers topics like Turing machines, state machines (and everything in between), Von Neumann architecture, the lambda calculus, pre- and post-conditions, the Y-combinator, Gödel's theorem, P-vs-NP, the various language paradigms, sorting, Conway's game of life, and more. It's not a tutorial on how to program specific things in a specific language, and that is to its credit. Instead, the book is a survey of many of the other things one learns (or hopefully learns) during the course of an undergraduate CS degree. The focus is on theory of computation and programming language theory (as opposed to say, databases, AI, or operating systems). Each topic gets an overview of usually a few pages. I greatly recommend it, especially to those who've already learned to program, but don't have formal schooling. Or to those who are currently learning, or to those who could benefit from a collection of short refreshers. The writing is clear and I've yet to see another book that covers so much of the foundations of the craft so concisely.

Review # 2 was written on 2014-01-02 00:00:00 James Hopton In comparison to "Introduction to Algorithms" (the other algorithm book I had significant exposure to) this one is faster to read, easier to digest and more tailored towards applications. I found the "Hitchhiker's Guide to Algorithms" in the back to be extremely useful if you really find yourself tackling an algorithmic problem in practice. The main part (maybe skipping/skimming down a few chapters) is a very good preparation for algorithm-heavy job interviews (e.g. Google, Facebook etc ...). Very much recommended.