4,669 research outputs found
Analysis of Timed and Long-Run Objectives for Markov Automata
Markov automata (MAs) extend labelled transition systems with random delays
and probabilistic branching. Action-labelled transitions are instantaneous and
yield a distribution over states, whereas timed transitions impose a random
delay governed by an exponential distribution. MAs are thus a nondeterministic
variation of continuous-time Markov chains. MAs are compositional and are used
to provide a semantics for engineering frameworks such as (dynamic) fault
trees, (generalised) stochastic Petri nets, and the Architecture Analysis &
Design Language (AADL). This paper considers the quantitative analysis of MAs.
We consider three objectives: expected time, long-run average, and timed
(interval) reachability. Expected time objectives focus on determining the
minimal (or maximal) expected time to reach a set of states. Long-run
objectives determine the fraction of time to be in a set of states when
considering an infinite time horizon. Timed reachability objectives are about
computing the probability to reach a set of states within a given time
interval. This paper presents the foundations and details of the algorithms and
their correctness proofs. We report on several case studies conducted using a
prototypical tool implementation of the algorithms, driven by the MAPA
modelling language for efficiently generating MAs.Comment: arXiv admin note: substantial text overlap with arXiv:1305.705
Parallel BioScape: A Stochastic and Parallel Language for Mobile and Spatial Interactions
BioScape is a concurrent language motivated by the biological landscapes
found at the interface of biology and biomaterials. It has been motivated by
the need to model antibacterial surfaces, biofilm formation, and the effect of
DNAse in treating and preventing biofilm infections. As its predecessor, SPiM,
BioScape has a sequential semantics based on Gillespie's algorithm, and its
implementation does not scale beyond 1000 agents. However, in order to model
larger and more realistic systems, a semantics that may take advantage of the
new multi-core and GPU architectures is needed. This motivates the introduction
of parallel semantics, which is the contribution of this paper: Parallel
BioScape, an extension with fully parallel semantics.Comment: In Proceedings MeCBIC 2012, arXiv:1211.347
Hybrid performance modelling of opportunistic networks
We demonstrate the modelling of opportunistic networks using the process
algebra stochastic HYPE. Network traffic is modelled as continuous flows,
contact between nodes in the network is modelled stochastically, and
instantaneous decisions are modelled as discrete events. Our model describes a
network of stationary video sensors with a mobile ferry which collects data
from the sensors and delivers it to the base station. We consider different
mobility models and different buffer sizes for the ferries. This case study
illustrates the flexibility and expressive power of stochastic HYPE. We also
discuss the software that enables us to describe stochastic HYPE models and
simulate them.Comment: In Proceedings QAPL 2012, arXiv:1207.055
Learning probability distributions generated by finite-state machines
We review methods for inference of probability distributions generated by probabilistic automata and related models for sequence generation. We focus on methods that can be proved to learn in the inference
in the limit and PAC formal models. The methods we review are state merging and state splitting methods for probabilistic deterministic automata and the recently developed spectral method for nondeterministic probabilistic automata. In both cases, we derive them from a high-level algorithm described in terms of the Hankel matrix of the distribution to be learned, given as an oracle, and then describe how to adapt that algorithm to account for the error introduced by a finite sample.Peer ReviewedPostprint (author's final draft
Probabilistic Model-Based Safety Analysis
Model-based safety analysis approaches aim at finding critical failure
combinations by analysis of models of the whole system (i.e. software,
hardware, failure modes and environment). The advantage of these methods
compared to traditional approaches is that the analysis of the whole system
gives more precise results. Only few model-based approaches have been applied
to answer quantitative questions in safety analysis, often limited to analysis
of specific failure propagation models, limited types of failure modes or
without system dynamics and behavior, as direct quantitative analysis is uses
large amounts of computing resources. New achievements in the domain of
(probabilistic) model-checking now allow for overcoming this problem.
This paper shows how functional models based on synchronous parallel
semantics, which can be used for system design, implementation and qualitative
safety analysis, can be directly re-used for (model-based) quantitative safety
analysis. Accurate modeling of different types of probabilistic failure
occurrence is shown as well as accurate interpretation of the results of the
analysis. This allows for reliable and expressive assessment of the safety of a
system in early design stages
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