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Title: Truth from Trash: How Learning Makes Sense (Complex Adaptive Systems) by Chris Thornton ISBN: 0-262-70087-5 Publisher: MIT Press Pub. Date: 07 February, 2002 Format: Paperback Volumes: 1 List Price(USD): $24.00

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Average Customer Rating: 2.67 (3 reviews)

Rating: 4
Summary: Interesting Introduction to a Framework for AI Application
Comment: This book provides interesting and inspiring coverage of the field of AI and machine learning at a conceptual level.

Those coming to AI and machine learning from allied disciplines like mathematics, economics, strategy or decision modeling might find this book fits well into their reading plans.

This book will probably not satisfy those skilled in AI, but I would not hesitate to recommend the book to those with whom I work in industrial planning that find themselves interested in the application of AI concepts to our field.

I found it interesting and inspiring. Complete and complex enough for me to develop an understanding of AI, and a framework for the application of its tools, but not overwhelming.

I would also recommend it as one of a two textbook set for a graduate course on the application of AI and machine learning techniques to those outside of the field. The other book would need to be an applications text (or maybe a "Kinko's" notes pack).

Writing an interesting book on AI without making it overwhelming seems to me a tough task. This author handled the task admirably; the result is an interesting and inspiring text on machine learning.

Rating: 3
Summary: patchy but interesting
Comment: Sure, the book jumps around a bit, is patchy when it comes to technical details and is fairly poorly referenced, but there's some interesting and inspiring ideas here. However, if you're from a country with a lousy exchange rate with the US (such as poor old Australia) then wait for the paperback edition!

Rating: 1
Summary: pretty trashy
Comment: I was very disappointed by this book. He makes a valid point that most machine learning research is concerned with attribute-based (propositional) representations, and that many problems require relational (first-order) representations, but this is not a novel claim.

He calls propositional learning "fence'n'fill" algorithms, because they basically carve up the input space (e.g., a perceptron uses linear boundaries). The advantage is that they are fast and well-understood. In the final chapter, he proposes an algorithm for relational learning which is based on top of a standard fence-n-fill algorithm, but doesn't explain it well, and doesn't give any compelling evidence that it works. The papers on his web site are no better.

He intersperses what little technical material he has with some historical anecdotes about code-breaking during WWII, etc. It's not really clear what the connection is. Overall, the book just does not hang together.

If you felt inclined to buy this book, I would recommend you check out Andy Clark's excellent "Being There" instead.