Temporal Reasoning without Transitive Tables
  Representing and reasoning about qualitative temporal information is an
essential part of many artificial intelligence tasks. Lots of models have been
proposed in the litterature for representing such temporal information. All
derive from a point-based or an interval-based framework. One fundamental
reasoning task that arises in applications of these frameworks is given by the
following scheme: given possibly indefinite and incomplete knowledge of the
binary relationships between some temporal objects, find the consistent
scenarii between all these objects. All these models require transitive tables
-- or similarly inference rules-- for solving such tasks. We have defined an
alternative model, S-languages - to represent qualitative temporal information,
based on the only two relations of \emph{precedence} and \emph{simultaneity}.
In this paper, we show how this model enables to avoid transitive tables or
inference rules to handle this kind of problem.
