Reasons for the success of constraint programming in this problem are iden tied and discussed. It can be used either to explain or to predict. Explaining or temporally projecting backward means giving possible models or interpretations of what took place in the past based on a partial temporal description of the facts, whereas predicting or temporally projecting forward consists of determining the facts that may occur in the future, starting from the imperfect knowledge available. Thus, broadly speaking, TIM can be seen as a development tool and library for temporal projection applications. The runtime of such pro-cedures is characterized by a random variable.
Different algorithms give rise to different probability distributions.
One can take advantage of such differences by combining several algorithms into a portfolio, and running them in parallel or interleaving them on a single processor. We provide a detailed evaluation of the portfolio approach on distributions of hard combinatorial search problems. We show under what conditions the portfolio approach can have a dramatic computational advantage over the best traditional methods. Inc. All rights reserved. Legal terms .