Simple Temporal Networks: A Practical Foundation for Temporal Representation and Reasoning (Invited Talk)

Authors Luke Hunsberger, Roberto Posenato

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Author Details

Luke Hunsberger
  • Department of Computer Science, Vassar College, Poughkeepsie, NY, USA
Roberto Posenato
  • Department of Computer Science, University of Verona, Italy


The open access publication of this article was supported by the Alpen-Adria-Universität Klagenfurt, Austria.

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Luke Hunsberger and Roberto Posenato. Simple Temporal Networks: A Practical Foundation for Temporal Representation and Reasoning (Invited Talk). In 28th International Symposium on Temporal Representation and Reasoning (TIME 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 206, pp. 1:1-1:5, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)


Since Simple Temporal Networks (STNs) were first introduced in 1991, there have been numerous theoretic and algorithmic advances that have made them practical for a wide variety of applications. However, the presentation of most of the important advances have been scattered across numerous conference papers and journal articles. As a result, it is too easy for even experienced researchers to be unaware of results that could positively impact their work. In this talk we review the most important results about STNs for researchers in Artificial Intelligence who are interested in incorporating the management of time and temporal constraints into their projects.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Temporal reasoning
  • Theory of computation → Network optimization
  • Theory of computation → Dynamic graph algorithms
  • Mathematics of computing → Graph algorithms
  • Simple Temporal Networks
  • Consistency Checking
  • Restoring Consistency
  • Dispatchability
  • Temporal Decoupling Problem


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