KR2021Proceedings of the 18th International Conference on Principles of Knowledge Representation and ReasoningProceedings of the 18th International Conference on Principles of Knowledge Representation and Reasoning

Online event. November 3-12, 2021.

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ISSN: 2334-1033
ISBN: 978-1-956792-99-7

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Copyright © 2021 International Joint Conferences on Artificial Intelligence Organization

Timed Trace Alignment with Metric Temporal Logic over Finite Traces

  1. Giuseppe De Giacomo(Sapienza University of Rome, Italy)
  2. Aniello Murano(University of Naples Federico II, Italy)
  3. Fabio Patrizi(Sapienza University of Rome, Italy)
  4. Giuseppe Perelli(Sapienza University of Rome, Italy)


  1. Geometric, spatial, and temporal reasoning
  2. Reasoning about actions and change, action languages


Trace Alignment is a prominent problem in Declarative Process Mining, which consists in identifying a minimal set of modifications that a log trace (produced by a system under execution) requires in order to be made compliant with a temporal specification. In its simplest form, log traces are sequences of events from a finite alphabet and specifications are written in DECLARE, a strict sublanguage of linear-time temporal logic over finite traces (LTLf ). The best approach for trace alignment has been developed in AI, using cost-optimal planning, and handles the whole LTLf . In this paper, we study the timed version of trace alignment, where events are paired with timestamps and specifications are provided in metric temporal logic over finite traces (MTLf ), essentially a superlanguage of LTLf . Due to the infiniteness of timestamps, this variant is substantially more challenging than the basic version, as the structures involved in the search are (uncountably) infinite-state, and calls for a more sophisticated machinery based on alternating (timed) automata, as opposed to the standard finite-state automata sufficient for the untimed version. The main contribution of the paper is a provably correct, effective technique for Timed Trace Alignment that takes advantage of results on MTLf decidability as well as on reachability for well-structured transition systems.