Machine Learning is slowly but surely leaving academic circles and enthusiasts' nighttime projects to relocate to business applications. While some solutions have quickly adopted modern ML-based algorithms (Deep Learning) successfully, image recognition for example, others have been struggling with achieving production-grade results, leading to stark statistics highlighting that 85% of ML projects in major corporations never see the light of day. This is a defining step for ML as a whole, since the expectations companies have when onboarding a new technology are high, there's a demand for higher quality than what one usually chases in a lab, and a positive return on investment is considered the norm. Nevertheless, experiments in this field are the new normal for actors in all industries. And when ML is applied to Document Analysis, some conclusions are fairly common.

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The author makes an important scientific contribution to the theory of knowledge and automatic decision making. The book will be a reference on fundamental research as well as a useful instrument for scientists, philosophers, and advanced students. The book's structure is constructive, facilitating a clear transmission of the author's ideas. Bacchus uses two plans of exposition: the epistemological plan justifies his theory in a wide, philosophical perspective, and the formal, mathematical plan gives the reader a valuable instrument. The book may be too short to fulfill the author's goals, but it reports a research result and requires the reader to take a good look at the bibliography.

In IJCAI-87, Proc. Tenth Intl. Joint Conference on Artificial Intelligence, Milan, Italy: Morgan Kaufmann, 1987, Vol. 1, pp. 264-270.