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.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- natural sciences computer and information sciences software
- natural sciences mathematics pure mathematics algebra
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Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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H2020-EU.1.3. - EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions
MAIN PROGRAMME
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H2020-EU.1.3.2. - Nurturing excellence by means of cross-border and cross-sector mobility
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
MSCA-IF-EF-ST - Standard EF
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) H2020-MSCA-IF-2017
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
WC1E 6BT LONDON
United Kingdom
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.