"Esteemed for providing the best available translations, Philosophic Classics: Ancient Philosophy, features complete works or complete sections of the most important works by the major thinkers, as well as shorter samples from transitional thinkers." First published in 1961, Forrest E. Baird's revision of "Philosophic Classics, "Pearson Education's long-standing anthology (available in split volumes), continues the tradition of providing generations of students with high quality course material. Using the complete works, or where appropriate, complete sections of works, this anthology allows philosophers to speak directly to students. "For more information on the main combined anthology, or the additional period volumes, please see below: " Philosophic Classics: From Plato to Derrida, 6/E "ISBN-10: 0205783864"Philosophic Classics, Volume I: Ancient Philosophy, 6/E "ISBN-10: 0205783856"Philosophic Classics, Volume III: Modern Philosophy, 6/E "ISBN-10: 0205783899"

Problems in decision making and in other areas such as pattern recogni tion, control, structural engineering etc. involve numerous aspects of uncertainty. Additional vagueness is introduced as models become more complex but not necessarily more meaningful by the added details. During the last two decades one has become more and more aware of the fact that not all this uncertainty is of stochastic (random) cha racter and that, therefore, it can not be modelled appropriately by probability theory. This becomes the more obvious the more we want to represent formally human knowledge. As far as uncertain data are concerned, we have neither instru ments nor reasoning at our disposal as well defined and unquestionable as those used in the probability theory. This almost infallible do main is the result of a tremendous work by the whole scientific world. But when measures are dubious, bad or no longer possible and when we really have to make use of the richness of human reasoning in its variety, then the theories dealing with the treatment of uncertainty, some quite new and other ones older, provide the required complement, and fill in the gap left in the field of knowledge representation. Nowadays, various theories are widely used: fuzzy sets, belief function, the convenient associations between probability and fuzzines~ etc *** We are more and more in need of a wide range of instruments and theories to build models that are more and more adapted to the most complex systems.

Problems in decision making and in other areas such as pattern recogni tion, control, structural engineering etc. involve numerous aspects of uncertainty. Additional vagueness is introduced as models become more complex but not necessarily more meaningful by the added details. During the last two decades one has become more and more aware of the fact that not all this uncertainty is of stochastic (random) cha racter and that, therefore, it can not be modelled appropriately by probability theory. This becomes the more obvious the more we want to represent formally human knowledge. As far as uncertain data are concerned, we have neither instru ments nor reasoning at our disposal as well defined and unquestionable as those used in the probability theory. This almost infallible do main is the result of a tremendous work by the whole scientific world. But when measures are dubious, bad or no longer possible and when we really have to make use of the richness of human reasoning in its variety, then the theories dealing with the treatment of uncertainty, some quite new and other ones older, provide the required complement, and fill in the gap left in the field of knowledge representation. Nowadays, various theories are widely used: fuzzy sets, belief function, the convenient associations between probability and fuzzines~ etc *** We are more and more in need of a wide range of instruments and theories to build models that are more and more adapted to the most complex systems.