Empirical study of algorithms for qualitative temporal or spatial constraint networks

Representing and reasoning about spatial and temporal information is an important task in many applications of Artificial Intelligence. In the past two decades numerous formalisms using qualitative constraint networks have been proposed for representing information about time and space. Most of the methods used to reason with these constraint networks are based on the weak composition closure method. The goal of this paper is to study some implementations of these methods, including three well known and very used implementations, and two new ones.