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Compositional Approximate Reasoning via Bialgebraic Semantics

Total Cost €


EC-Contrib. €






Project "CARBS" data sheet

The following table provides information about the project.


Organization address
city: LONDON
postcode: WC1E 6BT
website: n.a.

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country United Kingdom [UK]
 Total cost 91˙727 €
 EC max contribution 91˙727 € (100%)
 Programme 1. H2020-EU.1.3.2. (Nurturing excellence by means of cross-border and cross-sector mobility)
 Code Call H2020-MSCA-IF-2017
 Funding Scheme MSCA-IF-EF-ST
 Starting year 2019
 Duration (year-month-day) from 2019-02-01   to  2020-01-31


Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    UNIVERSITY COLLEGE LONDON UK (LONDON) coordinator 91˙727.00


 Project objective

Programming languages with probabilistic features are used extensively in computer science and beyond, to model uncertainty, perform quantitative analysis, inference and much more. To analyse programs in such languages, it is essential to have effective tools and techniques for approximate reasoning: for instance, determining the chance of congestion in a network, or the chance of failure of a system component. CARBS proposes a general mathematical framework of compositional proof techniques for approximate reasoning, with two essential points of focus: general applicability, to deal with the wide variety of different quantitative languages and models, and compositionality, to deal with large-scale systems. A motivating case study and application for the developed proof techniques is ProbNetKAT, a probabilistic language for describing randomized protocols and analysing quantitative properties in networks such as throughput or chance of failure. Approximate reasoning about such network programs is an important but also challenging problem, and the abundance of possible case studies will allow to immediately evaluate and apply the developed proof techniques. Approximate reasoning requires to move from behavioural equivalence to behavioural metrics, formalising how far apart two programs are. CARBS is based on integrating behavioural metrics in bialgebraic semantics, a categorical approach for a systematic study of languages and calculi based on the combination of algebra and coalgebra. Coalgebra allows to define behavioural metrics, in a general manner, whereas algebra integrates compositionality in the associated proof techniques. The overall envisaged result of CARBS is an extension of bialgebraic semantics to quantitative systems, providing on the one hand fundamental insights about quantiative coalgebras and compositionality, and on the other hand concrete, effective proof techniques for approximate reasoning.


year authors and title journal last update
List of publications.
2019 Tobias Kappé, Paul Brunet, Jurriaan Rot, Alexandra Silva, Jana Wagemaker, Fabio Zanasi
Kleene Algebra with Observations
published pages: 41:1--41:16, ISSN: , DOI: 10.4230/lipics.concur.2019.41
30th International Conference on Concurrency Theory, CONCUR 2019, August 27-30, 2019, Amsterdam, the Netherlands 140 2020-02-13
2020 Clemens Kupke, Jurriaan Rot
Expressive Logics for Coinductive Predicates
published pages: 26:1--26:18, ISSN: , DOI: 10.4230/lipics.csl.2020.26
28th EACSL Annual Conference on Computer Science Logic (CSL 2020) 152 2020-02-13
2019 van Heerdt, Gerco; Kappé, Tobias; Rot, Jurriaan; Sammartino, Matteo; Silva, Alexandra
Tree Automata as Algebras: Minimisation and Determinisation
published pages: 6:1--6:22, ISSN: , DOI: 10.4230/LIPIcs.CALCO.2019.6
8th Conference on Algebra and Coalgebra in Computer Science, CALCO 2019, June 3-6, 2019, London, United Kingdom 139 2020-02-13
2020 Gerco van Heerdt, Clemens Kupke, Jurriaan Rot, Alexandra Silva
Learning Weighted Automata over Principal Ideal Domains
published pages: , ISSN: , DOI:
Accepted for publication in proceedings FoSSaCS 2020 (to appear) 2020-02-13
2020 Moerman, Joshua; Rot, Jurriaan
Separation and Renaming in Nominal Sets
published pages: 31:1--31:17, ISSN: , DOI: 10.4230/lipics.csl.2020.31
28th EACSL Annual Conference on Computer Science Logic (CSL 2020) 152 2020-02-13

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