[Zurück]

H. Beck, M. Dao-Tran, T. Eiter:
"Answer Update for Rule-based Stream Reasoning";
Vortrag: International Joint Conference on Artificial Intelligence (IJCAI), Buenos Aires, Argentinia.; 25.07.2015 - 31.07.2015; in: "Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI), July 25-31, 2015, Buenos Aires, Argentinia.", (2015), S. 2741 - 2747.

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 In particular, we show how an answer stream (model) of a LARS program can be incrementally adjusted to new or outdated input by extending truth maintenance techniques.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.

Knowledge Representation & Reasoning; Answer Set Programming; Stream Reasoning; Nonmonotonic Reasoning

http://publik.tuwien.ac.at/files/PubDat_245323.pdf