Artificial Intelligence
Practical planning: extending the classical AI planning paradigm
Practical planning: extending the classical AI planning paradigm
Performance of temporal reasoning systems
ACM SIGART Bulletin
Efficient algorithms for qualitative reasoning about time
Artificial Intelligence
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Partial orders are an essential supporting component of non-linear, constraint-posting planning algorithms. However, managing partial orders represents and expensive overhead in plan construction and development. Simple solutions are only suitable for very small planning problems involving tens rather than hundreds of partially ordered elements. For more sophisticated planning languages allowing the representation of point-based intervals and abstraction it is necessary to manage far larger sets of temporal elements. In this paper is presented an approach and algorithm, TEMPMAN, for the management of partial orders which exploits the typical properties of planned partial orders to achieve a significantly better performance than existing solutions to this problem.