Temporal Logic as Filtering
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Abstract (arXiv)
We show that metric temporal logic can be viewed as linear time-invariant filtering, by interpreting addition, multiplication, and their neutral elements, over the (max,min,0,1) idempotent dioid. Moreover, by interpreting these operators over the field of reals (+,*,0,1), one can associate various quantitative semantics to a metric-temporal-logic formula, depending on the filter's kernel used: square, rounded-square, Gaussian, low-pass, band-pass, or high-pass. This remarkable connection between filtering and metric temporal logic allows us to freely navigate between the two, and to regard signal-feature detection as logical inference. To the best of our knowledge, this connection has not been established before. We prove that our qualitative, filtering semantics is identical to the classical MTL semantics. We also provide a quantitative semantics for MTL, which measures the normalized, maximum number of times a formula is satisfied within its associated kernel, by a given signal. We show that this semantics is sound, in the sense that, if its measure is 0, then the formula is not satisfied, and it is satisfied otherwise. We have implemented both of our semantics in Matlab, and illustrate their properties on various formulas and signals, by plotting their computed measures.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Oct 2015.
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Citing papers per year: 2021: 2; 2022: 2.
4 papers.
- 2021Hierarchical Potential-based Reward Shaping from Task Specificationscited by 7
- 2021STL Robustness Risk over Discrete-Time Stochastic Processescited by 4
- 2022Safe Policy Improvement in Constrained Markov Decision Processescited by 0
- 2022Parameterisation of Reasoning on Temporal Markov Logic Networkscited by 0
- 2022Parameterisation of Reasoning on Temporal Markov Logic Networks29 Nov 2022
- 2022Safe Policy Improvement in Constrained Markov Decision Processes20 Oct 2022
- 2021Hierarchical Potential-based Reward Shaping from Task Specifications6 Oct 2021
- 2021STL Robustness Risk over Discrete-Time Stochastic Processes3 Apr 2021