Introduction to the Theory of Computation Review

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Introduction to the Theory of Computation ReviewWhen I picked up this book I thought, "You have to be kidding me." This book is very thin, and then a fair chunk of it is mathematics review for some of the formal arguments the book is going to be making later on. One wouldn't think there was much in this book.
One would be wrong. This book goes into rather impressive depth on some rather abstract concepts of computer science without dabbling for too long in the details. It does the best job I've ever seen of explaining the Turing machine and how it relates to computability and decidablity.
The exercises are both easy and insanely difficult - so you can basically chose your level and then go through the book, some of the problems are very hard, some are trivially easy, a great mix makes for great homework assignments.
The "Proof Idea:" sections before every proof give you the underlying concepts in plain english that are about to be stated formally so you have a clue what's happening when the formal definitions start flying. These are priceless and should be included in every other book that uses formal proof techniques.
The book reads fairly well on its own, or makes for a great class text book, which I used it for. As my professor said, "This is a good book because it doesn't have any extra words." but you don't seem to mind as you read it. Probably the best work on the science of computation in the world, certainly the best I've ever seen.Introduction to the Theory of Computation OverviewMichael Sipser's emphasis on unifying computer science theory - rather than offering a collection of low-level details - sets the book apart, as do his intuitive explanations. Throughout the book, Sipser builds students' knowledge of conceptual tools used in computer science, the aesthetic sense they need to create elegant systems, and the ability to think through problems on their own.

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Fuzzy Thinking: The New Science of Fuzzy Logic Review

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Fuzzy Thinking: The New Science of Fuzzy Logic ReviewBuddhist math? C'mon.
First, let me say that fuzzy logic and fuzzy arithmetic are great tools. They're valued parts of the 'soft logic' kit that includes probability, interval arithmetic, Bayesian and Markov networks, and lots of other good stuff. Fuzziness involves many of the formal techniques used in probability and elsewhere, and gives a useful, alternative view of the systems it addresses.
The basic fuzzy idea is that most descriptions involve shades of gray, that few systems really match the black/white, on/off, either/or duality of standard formal logic. That's fine, I can get along with that quite well.
My problem, though, is that Kosko presents the fuzzy world-view vs. the traditional or "scientific" in exactly the black and white terms that he rejects. He also argues that fuzziness describes the world more effectively than "scientific" terms, that the rules of arithmetic, probability, and calculus are just games. They are played for their internal consistency, not because differentiation or factorials occur in nature.
That's true, and as a heavy math user I know enough to distinguish my models from reality. Two facts remain, though. First, the models very often do describe reality in ways that can be checked easily enough: the bridge doesn't fall down and the TV receives its signal. Both happen because the bad old exact arithmetic has some kind of correspondence (no, I don't know what) to the real world, giving real ability to predict real results. Second, fuzzy logic and fuzzy arithmetic are themselves mathematical formalisms, games like all the others. Once you get past the gee-whiz stage, there is mathematical content as rigorous as in any other field of study. It's not either/or, it's very often a different way to interpret the same self-consistent games people have played for years. It adds interesting rules to the game.
The great thing is that you really can use the new interpretations and tools along with the old ones. Fuzziness doesn't demolish the old structures, it bolsters them and adds capacity.
And you can get all these benefits without shrink-wrapped, bite-sized pieces of Eastern philosophy.
//wiredweirdFuzzy Thinking: The New Science of Fuzzy Logic Overview

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Artificial Intelligence: A Modern Approach (2nd Edition) Review

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Artificial Intelligence: A Modern Approach (2nd Edition) ReviewI didn't think that the first edition of this book was as bad as some of the reviewers said, but the second edition is definitely a vast improvement. It's not just some obligatory 2nd edition that some authors release to say that they are staying actively published. The first edition was somewhat confusing in its explanations and the exercises were really blurry on what was being asked. All of that has now been resolved.
The book is a comprehensive and insightful introduction to artificial intelligence with an academic tone. It provides a unified view of the field organized around the rational decision making paradigm, which focuses on the selection of the "best" solution to a problem. The book's overall theme is that the purpose of AI is to solve problems via intelligent agents, and then goes about specifying the features such an agent or agents should have. Pseudocode is provided for all of the major AI algorithms. Being about the broadest book in terms of coverage of AI, you should therefore not expect it to be the deepest in coverage. However, each topic is covered to the extent that the reader should understand its essence. Sections one through six are absolutely wonderful, and comprise the "meat" of AI. Section seven is rather weak since it tries to cover both robotics and text processing in their own individual chapters, and entire books have a hard time covering this material. Section eight is different from the others, since it talks about the philosophy and future of AI.
Another plus for this book is that there is a great deal of extra material that deals with standard AI curriculum. For example, the chapters on logic not only include the typical introduction to propositional and first order logic together with the usual inference procedures, they also give many useful hints how to use first order logic to actually represent aspects of the real world such as measures, time, actions, mental objects, etc. These chapters also contain much information about how to implement efficient logical reasoners.
Finally, this second edition has an excellent website that can be found by going through the publisher's webpage for the book. This website contains four sample chapters, pseudocode, and actual code in Java, Python, and LISP.
I notice that Amazon shows the table of contents from the first edition, so I am showing what the actual table of contents is for the second edition for the purpose of completeness. Note that the book has been significantly reorganized.
I. ARTIFICIAL INTELLIGENCE.
1. Introduction.
2. Intelligent Agents.
II. PROBLEM-SOLVING.
3. Solving Problems by Searching.
4. Informed Search and Exploration.
5. Constraint Satisfaction Problems.
6. Adversarial Search.
III. KNOWLEDGE AND REASONING.
7. Logical Agents.
8. First-Order Logic.
9. Inference in First-Order Logic.
10. Knowledge Representation.
IV. PLANNING.
11. Planning.
12. Planning and Acting in the Real World.
V. UNCERTAIN KNOWLEDGE AND REASONING.
13. Uncertainty.
14. Probabilistic Reasoning Systems.
15. Probabilistic Reasoning Over Time.
16. Making Simple Decisions.
17. Making Complex Decisions.
VI. LEARNING.
18. Learning from Observations.
19. Knowledge in Learning.
20. Statistical Learning Methods.
21. Reinforcement Learning.
VII. COMMUNICATING, PERCEIVING, AND ACTING.
22. Agents that Communicate.
23. Text Processing in the Large.
24. Perception.
25. Robotics.
VIII. CONCLUSIONS.
26. Philosophical Foundations.
27. AI: Present and Future.Artificial Intelligence: A Modern Approach (2nd Edition) Overview

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