Book Reviews

Imust confess I was somewhat leery when I first opened this book. Too often I have seen papers and books whose titles combine any number of terms from the list “neural networks, genetic algorithms, fuzzy logic, chaos theory, synergetics, self-organized criticality” with any one item from the list “artificial intelligence, finance, pattern recognition, data mining” to produce a piece of work that is neither very readable nor very useful. Although this book has its moments of evangelical zeal and proselytizing, I am happy to report that it possesses enough redeeming features to render my initial skepticism unwarranted. In fact, I highly recommend this volume as the best introduction for newcomers to an active and exciting field. The subject matter of the book is nowadays often called “soft computing,” a term that hints at something less than rigorous and does not do the field complete justice. In fact, the “softness” of the computing reflects the uncertainty element in some or all of the input data, necessitating a probabilistic treatment of the problem. This is where fuzzy logic comes to the rescue, frequently in combination with heuristic treatments such as neural networks or genetic algorithms. Plenty of conferences have been held on this subject in recent years, with a consequent plethora of published proceedings but no book that can serve as a text for use in a course or for self-study. The closest to a tutorial volume is the excellent monograph by the prolific Bart Kosko [5], but that work is becoming a little dated and cannot be considered exhaustive in coverage. Kasabov’s book clearly fills a void and does so admirably, although it too does not provide a complete survey of the field. “Fuzzy logic” is the brainchild of the Berkeley professor Lotfi Zadeh, who coined the phrase in 1965 and has been an indefatigable proponent of the theory and its applications [11]. Earlier ideas on multivalued logic have been traced back to the 1920s, but it was Zadeh who came up with the catchy name and applied the theory to real engineering problems. The core idea is simple enough: Rather than using binary, Aristotelian logic with its sharp division between true and false, allow for degrees of truth and introduce shades of gray in value judgments. Students of human affairs might be amused that such notions emerged from Berkeley in the 1960s, although in this particular case the connection between science and society is tenuous at best. Fuzziness clearly has its advantages. Not only does it inject an element of probability in logic, enabling it to deal with the uncertainties of nature, it also neatly eliminates some of the paradoxes that have plagued traditional logic [1]. My own favorites are the sorites, various incarnations of which go back to the ancient Greek philosophers and are known as the “paradox of the heap” or the “paradox of the bald man. ” Consider, for example, a man with a full head of hair. Surely this person is not bald. Equally clearly, removing a single hair from the head of a person who is not bald will not make that person bald. Now iterate: extracting one hair at a time, one ends up with a person who has no hair, yet who never crossed over from “nonbaldness” to “baldness. ” The source of the paradox is the notion that a person is either bald or not bald (binabook reviews