Point-Based Approaches to Qualitative Temporal Reasoning

Proceedings of the AAAI Conference on Artificial Intelligence, 16

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 16

Tractable Reasoning

We address the general problem of efficient, qualitative, point-based temporal reasoning over a set of operations and their corresponding algorithms. We consider general reasoners tailored for temporal domains that exhibit a particular structure and introduce a general reasoner based on the series parallel graph reasoner of Delgrande and Gupta. This reasoner is also an extension of the TimeGraph reasoner of Gerevini and Schubert. Test results indicate that when there is some underlying structure in the data, our reasoner performs better than other approaches. When there is no underlying structure in the data, our reasoner performs about the same as the simple approaches for query answering, although compile times are higher.