Over the last two decades, the field of artificial intelligence
has experienced
a separation into two schools that hold opposite opinions on how
uncertainty should be treated. This separation is the result of a
debate that began at the end of the 1960 s when AI first faced the
problem of building machines required to make decisions and act in
the real world. This debate witnessed the contraposition between
the mainstream school, which relied on probability for handling
uncertainty, and an alternative school, which criticized the
adequacy of probability in AI applications and developed
alternative formalisms.
The debate has focused on the technical aspects of the
criticisms raised against probability while neglecting an important
element of contrast. This element is of an epistemological nature,
and is therefore exquisitely philosophical. In this book, the
historical context in which the debate on probability developed is
presented and the key components of the technical criticisms
therein are illustrated. By referring to the original texts, the
epistemological element that has been neglected in the debate is
analyzed in detail. Through a philosophical analysis of the
epistemological element it is argued that this element is
metaphysical in Popper s sense. It is shown that this element
cannot be tested nor possibly disproved on the
basis of experience and is therefore extra-scientific. Ii is
established that a philosophical analysis is now compelling in
order to both solve the problematic division that characterizes the
uncertainty field and to secure the foundations of the field
itself.
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