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Ticker: A system for incremental ASP-based stream reasoning*

Published online by Cambridge University Press:  23 August 2017

HARALD BECK ,
THOMAS EITER  and
CHRISTIAN FOLIE
HARALD BECK
Affiliation:
Institute of Information Systems, Vienna University of Technology, Favoritenstraße 9-11, A-1040 Vienna, Austria (e-mails: [email protected], [email protected], [email protected])
THOMAS EITER
Affiliation:
Institute of Information Systems, Vienna University of Technology, Favoritenstraße 9-11, A-1040 Vienna, Austria (e-mails: [email protected], [email protected], [email protected])
CHRISTIAN FOLIE
Affiliation:
Institute of Information Systems, Vienna University of Technology, Favoritenstraße 9-11, A-1040 Vienna, Austria (e-mails: [email protected], [email protected], [email protected])
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Abstract

In complex reasoning tasks, as expressible by Answer Set Programming (ASP), problems often permit for multiple solutions. In dynamic environments, where knowledge is continuously changing, the question arises how a given model can be incrementally adjusted relative to new and outdated information. This paper introduces Ticker, a prototypical engine for well-defined logical reasoning over streaming data. Ticker builds on a practical fragment of the recent rule-based language LARS, which extends ASP for streams by providing flexible expiration control and temporal modalities. We discuss Ticker's reasoning strategies: first, the repeated one-shot solving mode calls Clingo on an ASP encoding. We show how this translation can be incrementally updated when new data is streaming in or time passes by. Based on this, we build on Doyle's classic justification-based truth-maintenance system to update models of non-stratified programs. Finally, we empirically compare the obtained evaluation mechanisms.

Keywords

Stream reasoningAnswer set programmingNon-monotonic reasoning
Type
Regular Papers
Information
Theory and Practice of Logic Programming , Volume 17 , Special Issue 5-6: 33rd International Conference on Logic Programming , September 2017 , pp. 744 - 763
Copyright
Copyright © Cambridge University Press 2017 

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Footnotes

*

This research has been supported by the Austrian Science Fund (FWF) projects P26471 and W1255-N23.

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