‘4.

Research in Artificial Intelligence on constraint-based representations for temporal reasoning has largely con-centrated on two kinds of formalisms: systems of simple linear inequalities to encode metric relations between time points, and systems of binary constraints in Allen’s temporal calculus to encode qualitative relations be-tween time intervals. Each formalism has certain ad-vantages. Linear inequalities can represent dates, du-rations, and other quantitive information; Allen’s qual-itative calculus can express relations between time in-tervals, such as disjointedness, that are useful for con-straint-based approaches to planning. In this paper we demonstrate how metric and Allen-style constraint networks can be integrated in a con-straint-based reasoning system. The highlights of the work include a simple but powerful logical language for expressing both quantitative and qualitative infor-mation; translation algorithms between the metric and Allen sublanguages that entail minimal loss of informa-tion; and a constraint-propagation procedure for prob-lems expressed in a combination of metric and Allen constraints