A LOGICAL AI APPROACH TO CONTEXT
Abstract:
Logical AI develops computer programs that represent what they know
about the world primarily by logical formulas and decide what to do
primarily by logical reasoning---including nonmonotonic logical
reasoning. It is convenient to use logical sentences and terms
whose meaning depends on context. The reasons for this are similar
to what causes human language to use context dependent meanings.
This note gives elements of some of the formalisms to which we have
been led. Fuller treatments are in \cite{McC93}, \cite{guha-thesis}
and \cite{McCBuvac94} and the references cited in the Web page
\cite{Buvac95}. The first main idea is to make contexts first class
objects in the logic and use the formula $ist(c,p)$ to assert that
the \emph{proposition} $p$ is true in the \emph{context} $c$. A
second idea is to formalize how propositions true in one context
transform when they are moved to different but related contexts.
An ability to transcend the outermost context is needed to
give computer programs the ability to reason about the totality of
all they have thought about so far \cite{McC96}.
The references given here in LaTeX form are spelled out in the body of
the paper.
The paper is easiest to read on line in the html form of the paper. There are
also LaTeX, .dvi, .pdf
and .ps versions of the article.
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