Constructing Witnesses for Lower Bounds on Behavioural Distances

Behavioural distances provide a robust alternative to notions of equivalence such as bisimilarity in the context of probabilistic transition systems. They can be defined as least fixed points, whose universal property allows us to exhibit upper bounds on the distance between states, showing them to be at most some distance apart. In this paper, we instead consider the problem of bounding distances from below, showing states to be at least some distance apart. Contrary to upper bounds, it is possible to reason about lower bounds inductively. We exploit this by giving an inductive derivation system for lower bounds on an existing definition of behavioural distance for labelled Markov chains. This is inspired by recent work on apartness as an inductive counterpart to bisimilarity. Proofs in our system will be shown to closely match the behavioural distance by soundness and (approximate) completeness results. We further provide a constructive correspondence between our derivation system and formulas in a modal logic with quantitative semantics. This logic was used in recent work of Rady and van Breugel to construct evidence for lower bounds on behavioural distances. Our constructions provide smaller witnessing formulas in many examples.

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Publication Details

Journal
Leibniz-Zentrum für Informatik (Schloss Dagstuhl)
Published
2025-04-11
DOI
https://doi.org/10.4230/lipics.csl.2026.25
Primary Topic
Adversarial Robustness in Machine Learning
Type
article
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article

Constructing Witnesses for Lower Bounds on Behavioural Distances

Ruben Turkenburg, Franck van Breugel, Jurriaan Rot, Clemens Kupke et al.
Leibniz-Zentrum für Informatik (Schloss Dagstuhl)
Adversarial Robustness in Machine Learning
article

Constructing Witnesses for Lower Bounds on Behavioural Distances

Ruben Turkenburg, Franck van Breugel, Jurriaan Rot, Clemens Kupke, Harsh Beohar
article en

Abstract

Behavioural distances provide a robust alternative to notions of equivalence such as bisimilarity in the context of probabilistic transition systems. They can be defined as least fixed points, whose universal property allows us to exhibit upper bounds on the distance between states, showing them to be at most some distance apart. In this paper, we instead consider the problem of bounding distances from below, showing states to be at least some distance apart. Contrary to upper bounds, it is possible to reason about lower bounds inductively. We exploit this by giving an inductive derivation system for lower bounds on an existing definition of behavioural distance for labelled Markov chains. This is inspired by recent work on apartness as an inductive counterpart to bisimilarity. Proofs in our system will be shown to closely match the behavioural distance by soundness and (approximate) completeness results. We further provide a constructive correspondence between our derivation system and formulas in a modal logic with quantitative semantics. This logic was used in recent work of Rady and van Breugel to construct evidence for lower bounds on behavioural distances. Our constructions provide smaller witnessing formulas in many examples.

Leibniz-Zentrum für Informatik (Schloss Dagstuhl)
Openalex Percentile: Top 86%
Adversarial Robustness in Machine Learning
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