Solving Overconstrained Temporal Reasoning Problems

Representing and reasoning with temporal information is an essential part of many tasks in AI such as scheduling planning and nat ural language processing Two in uential frameworks for representing temporal information are interval algebra and point algebra Given a knowledge base consisting of temporal relations the main reasoning problem is to determine whether this knowledge base is satis able i e there is a scenario which is consistent with the information provided However when a given set of temporal relations is unsatis able no fur ther reasoning is performed We argue that many real world problems are inherently overconstrained and we can not just ignore them we must address them This paper investigates approaches for handling overcon strainedness in temporal reasoning We adapt a well studied notion of partial satisfaction to de ne partial scenarios an optimal partial solu tion We propose two reasoning procedures for computing an optimal partial solution to a problem or a complete solution if it exists