Answer Update for Rule-Based Stream Reasoning
Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence. Special Track on Knowledge Representation and Reasoning. Pages 2741-2746.
Abstract
Stream reasoning is the task of continuously deriving conclusions on streaming data. To get results instantly one evaluates a query repeatedly on recent data chunks selected by window operators. However, simply recomputing results from scratch is impractical for rule-based reasoning with semantics similar to Answer Set Programming, due to the trade-off between complexity and data throughput. To address this problem, we present a method to efficiently update models of a rule set. In particular, we show how an answer stream (model) of a LARS program can be incrementally adjusted to new or outdated input by extending truth maintenance techniques. We obtain in this way a means towards practical rule-based stream reasoning with nonmonotonic negation, various window operators and different forms of temporal reference.
@inproceedings{ijcai2015p388,
title = {Answer Update for Rule-Based Stream Reasoning},
author = {Beck, Harald and Dao-Tran, Minh and Eiter, Thomas},
booktitle = {Proceedings of the Twenty-Fourth International Joint Conference on
Artificial Intelligence, {IJCAI-15}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Qiang Yang and Michael Wooldridge},
pages = {2741--2746},
year = {2015},
month = {7},
note = {Special Track on Knowledge Representation and Reasoning},
url = {https://ijcai.org/proceedings/2015/388},
}
TY - CPAPER TI - Answer Update for Rule-Based Stream Reasoning AU - Beck, Harald AU - Dao-Tran, Minh AU - Eiter, Thomas T2 - Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-15 PB - International Joint Conferences on Artificial Intelligence PY - 2015 SP - 2741 EP - 2746 UR - https://ijcai.org/proceedings/2015/388 ER -Copied
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