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Logical Structures for Representation of Knowledge and Uncertainty (Paperback, Softcover reprint of the original 1st ed. 1992)
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Logical Structures for Representation of Knowledge and Uncertainty (Paperback, Softcover reprint of the original 1st ed. 1992)
Series: Studies in Fuzziness and Soft Computing, 14
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It is the business of science not to create laws, but to discover
them. We do not originate the constitution of our own minds,
greatly as it may be in our power to modify their character. And as
the laws of the human intellect do not depend upon our will, so the
forms of science, of (1. 1) which they constitute the basis, are in
all essential regards independent of individual choice. George
Boole 10, p. llJ 1. 1 Comparison with Traditional Logic The logic
of this book is a probability logic built on top of a yes-no or
2-valued logic. It is divided into two parts, part I: BP Logic, and
part II: M Logic. 'BP' stands for 'Bayes Postulate'. This postulate
says that in the absence of knowl edge concerning a probability
distribution over a universe or space one should assume 1 a uniform
distribution. 2 The M logic of part II does not make use of Bayes
postulate or of any other postulates or axioms. It relies
exclusively on purely deductive reasoning following from the
definition of probabilities. The M logic goes an important step
further than the BP logic in that it can distinguish between
certain types of information supply sentences which have the same
representation in the BP logic as well as in traditional first
order logic, although they clearly have different meanings (see
example 6. 1. 2; also comments to the Paris-Rome problem of eqs.
(1. 8), (1. 9) below)."
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