Temporal Reasoning without Transitive Tables

TL;DR

This paper introduces S-languages, a novel model for qualitative temporal reasoning that eliminates the need for transitive tables or inference rules, simplifying the reasoning process in AI applications.

Contribution

The paper presents S-languages, an alternative framework based on precedence and simultaneity relations, avoiding the reliance on transitive tables for temporal reasoning tasks.

Findings

S-languages effectively represent qualitative temporal information.

The model simplifies reasoning by removing the need for transitive inference rules.

It enables consistent scenario derivation without transitive tables.

Abstract

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.