Automated Learning of Probabilistic Assumptions for Compositional Reasoning - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2011

Automated Learning of Probabilistic Assumptions for Compositional Reasoning

Lu Feng
  • Function : Author
Marta Kwiatkowska
  • Function : Author
  • PersonId : 861066


Probabilistic verification techniques have been applied to the formal modelling and analysis of a wide range of systems, from commu- nication protocols such as Bluetooth, to nanoscale computing devices, to biological cellular processes. In order to tackle the inherent challenge of scalability, compositional approaches to verification are sorely needed. An example is assume-guarantee reasoning, where each component of a system is analysed independently, using assumptions about the other components that it interacts with. We discuss recent developments in the area of automated compositional verification techniques for probabilistic systems. In particular, we describe techniques to automatically generate probabilistic assumptions that can be used as the basis for compositional reasoning. We do so using algorithmic learning techniques, which have already proved to be successful for the generation of assumptions for compositional verification of non-probabilistic systems. We also present recent improvements and extensions to this work and survey some of the promising potential directions for further research in this area.
Fichier principal
Vignette du fichier
fase11.pdf (424.56 Ko) Télécharger le fichier
Origin : Publisher files allowed on an open archive

Dates and versions

hal-00647067 , version 1 (01-12-2011)


  • HAL Id : hal-00647067 , version 1


Lu Feng, Marta Kwiatkowska, David Parker. Automated Learning of Probabilistic Assumptions for Compositional Reasoning. 14th International Conference on Fundamental Approaches to Software Engineering (FASE'11), 2011, Saarbrücken, Germany. pp.2--17. ⟨hal-00647067⟩


57 View
164 Download


Gmail Facebook Twitter LinkedIn More