Efficient Temporal Reasoning through Timegraphs
Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence. Knowledge Representation. Pages 648-653.
Abstract
In this paper we address the problem of scalability in temporal reasoning. In particular, new algorithms for efficiently managing large sets of relations in the Point Algebra are provided. Our representation of time is based on timegraphs, graphs partitioned into a set of chains on which the search is supported by a metagraph data structure. The approach is an extension of the time representation proposed by Schubert, Taugher and Miller in the context of story comprehension. The algorithms presented in this work concern the construction of a timegraph from a given set of relations and are implemented in a temporal reasoning system called TG-II. Experimental results show that our approach is very efficient, especially when the given relations admit representation as a collection of chains connected by relatively few cross-chain links.
@inproceedings{ijcai1993p91,
title = {Efficient Temporal Reasoning through Timegraphs},
author = {Gerevini, Alfonso and Schubert, Lenhart K.},
booktitle = {Proceedings of the Thirteenth International Joint Conference on
Artificial Intelligence, {IJCAI-93}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Ruzena Bajcsy},
pages = {648--653},
year = {1993},
note = {Knowledge Representation},
url = {https://ijcai.org/proceedings/1993/91},
}
TY - CPAPER TI - Efficient Temporal Reasoning through Timegraphs AU - Gerevini, Alfonso AU - Schubert, Lenhart K. T2 - Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, IJCAI-93 PB - International Joint Conferences on Artificial Intelligence PY - 1993 SP - 648 EP - 653 UR - https://ijcai.org/proceedings/1993/91 ER -Copied
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