Point-based approaches to qualitative temporal reasoning
James P. Delgrande, Ajay Kumar Gupta, Tim Van Allen · National Conference on Artificial Intelligence · 1999
We address the general problem of finding algorithms for efficient, qualitative, point-based temporal reasoning over a set of operations. We consider general reasoners tailored for temporal domains that exhibit a particular structure and introduce such a reasoner based on the series-parallel graph reasoner of Delgrande and Gupta; this reasoner is also an extension of the Time Graph reasoner of Gerevini and Schubert. Test results indicate that for data with underlying structure, our reasoner performs better than other approaches. When there is no underlying structure in the data, our reasoner still performs better for query answering.