Probablistic Bigraphs

05/06/2021
by   Blair Archibald, et al.
0

Bigraphs are a universal computational modelling formalism for the spatial and temporal evolution of a system in which entities can be added and removed. We extend bigraphs to probablistic bigraphs, and then again to action bigraphs, which include non-determinism and rewards. The extensions are implemented in the BigraphER toolkit and illustrated through examples of virus spread in computer networks and data harvesting in wireless sensor systems. BigraphER also supports the existing stochastic bigraphs extension of Krivine et al., and using BigraphER we give, for the first time, a direct implementation of the membrane budding model used to motivate stochastic bigraphs.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
07/02/2020

System inference for the spatio-temporal evolution of infectious diseases: Michigan in the time of COVID-19

We extend the classical SIR model of infectious disease spread to accoun...
research
04/06/2021

Point classification with Runge-Kutta networks and feature space augmentation

In this paper we combine an approach based on Runge-Kutta Nets considere...
research
02/21/2023

A Note on Noisy Reservoir Computation

In this note we extend the definition of the Information Processing Capa...
research
05/19/2018

DenseImage Network: Video Spatial-Temporal Evolution Encoding and Understanding

Many of the leading approaches for video understanding are data-hungry a...
research
09/30/2020

Erratum Concerning the Obfuscated Gradients Attack on Stochastic Activation Pruning

Stochastic Activation Pruning (SAP) (Dhillon et al., 2018) is a defense ...
research
04/30/2019

Hitting Time of Stochastic Gradient Langevin Dynamics to Stationary Points: A Direct Analysis

Stochastic gradient Langevin dynamics (SGLD) is a fundamental algorithm ...

Please sign up or login with your details

Forgot password? Click here to reset