115 research outputs found
Reducing Run-Time Adaptation Space via Analysis of Possible Utility Bounds
Self-adaptive systems often employ dynamic programming or similar techniques to select optimal adaptations at run-time. These techniques suffer from the âcurse of dimensionality , increasing the cost of run-time adaptation decisions. We propose a novel approach that improves upon the state-of-the-art proactive self-adaptation techniques to reduce the number of possible adaptations that need be considered for each run-time adaptation decision. The approach, realized in a tool called Thallium, employs a combination of automated formal modeling techniques to (i) analyze a structural model of the system showing which configurations are reachable from other configurations and (ii) compute the utility that can be generated by the optimal adaptation over a bounded horizon in both the best- and worst-case scenarios. It then constructs triangular possibility values using those optimized bounds to automatically compare adjacent adaptations for each configuration, keeping only the alternatives with the best range of potential results. The experimental results corroborate Thalliumâs ability to significantly reduce the number of states that need to be considered with each adaptation decision, freeing up vital resources at run-time
Multi-objective Robust Strategy Synthesis for Interval Markov Decision Processes
Interval Markov decision processes (IMDPs) generalise classical MDPs by
having interval-valued transition probabilities. They provide a powerful
modelling tool for probabilistic systems with an additional variation or
uncertainty that prevents the knowledge of the exact transition probabilities.
In this paper, we consider the problem of multi-objective robust strategy
synthesis for interval MDPs, where the aim is to find a robust strategy that
guarantees the satisfaction of multiple properties at the same time in face of
the transition probability uncertainty. We first show that this problem is
PSPACE-hard. Then, we provide a value iteration-based decision algorithm to
approximate the Pareto set of achievable points. We finally demonstrate the
practical effectiveness of our proposed approaches by applying them on several
case studies using a prototypical tool.Comment: This article is a full version of a paper accepted to the Conference
on Quantitative Evaluation of SysTems (QEST) 201
Tableaux for Policy Synthesis for MDPs with PCTL* Constraints
Markov decision processes (MDPs) are the standard formalism for modelling
sequential decision making in stochastic environments. Policy synthesis
addresses the problem of how to control or limit the decisions an agent makes
so that a given specification is met. In this paper we consider PCTL*, the
probabilistic counterpart of CTL*, as the specification language. Because in
general the policy synthesis problem for PCTL* is undecidable, we restrict to
policies whose execution history memory is finitely bounded a priori.
Surprisingly, no algorithm for policy synthesis for this natural and
expressive framework has been developed so far. We close this gap and describe
a tableau-based algorithm that, given an MDP and a PCTL* specification, derives
in a non-deterministic way a system of (possibly nonlinear) equalities and
inequalities. The solutions of this system, if any, describe the desired
(stochastic) policies.
Our main result in this paper is the correctness of our method, i.e.,
soundness, completeness and termination.Comment: This is a long version of a conference paper published at TABLEAUX
2017. It contains proofs of the main results and fixes a bug. See the
footnote on page 1 for detail
Computing Quantiles in Markov Reward Models
Probabilistic model checking mainly concentrates on techniques for reasoning
about the probabilities of certain path properties or expected values of
certain random variables. For the quantitative system analysis, however, there
is also another type of interesting performance measure, namely quantiles. A
typical quantile query takes as input a lower probability bound p and a
reachability property. The task is then to compute the minimal reward bound r
such that with probability at least p the target set will be reached before the
accumulated reward exceeds r. Quantiles are well-known from mathematical
statistics, but to the best of our knowledge they have not been addressed by
the model checking community so far.
In this paper, we study the complexity of quantile queries for until
properties in discrete-time finite-state Markov decision processes with
non-negative rewards on states. We show that qualitative quantile queries can
be evaluated in polynomial time and present an exponential algorithm for the
evaluation of quantitative quantile queries. For the special case of Markov
chains, we show that quantitative quantile queries can be evaluated in time
polynomial in the size of the chain and the maximum reward.Comment: 17 pages, 1 figure; typo in example correcte
Permissive Controller Synthesis for Probabilistic Systems
We propose novel controller synthesis techniques for probabilistic systems
modelled using stochastic two-player games: one player acts as a controller,
the second represents its environment, and probability is used to capture
uncertainty arising due to, for example, unreliable sensors or faulty system
components. Our aim is to generate robust controllers that are resilient to
unexpected system changes at runtime, and flexible enough to be adapted if
additional constraints need to be imposed. We develop a permissive controller
synthesis framework, which generates multi-strategies for the controller,
offering a choice of control actions to take at each time step. We formalise
the notion of permissivity using penalties, which are incurred each time a
possible control action is disallowed by a multi-strategy. Permissive
controller synthesis aims to generate a multi-strategy that minimises these
penalties, whilst guaranteeing the satisfaction of a specified system property.
We establish several key results about the optimality of multi-strategies and
the complexity of synthesising them. Then, we develop methods to perform
permissive controller synthesis using mixed integer linear programming and
illustrate their effectiveness on a selection of case studies
Genetically enhanced asynapsis of autosomal chromatin promotes transcriptional dysregulation and meiotic failure
During meiosis, pairing of homologous chromosomes and their synapsis are essential prerequisites for normal male gametogenesis. Even limited autosomal asynapsis often leads to spermatogenic impairment, the mechanism of which is not fully understood. The present study was aimed at deliberately increasing the size of partial autosomal asynapsis and analysis of its impact on male meiosis. For this purpose, we studied the effect of t12 haplotype encompassing four inversions on chromosome 17 on mouse autosomal translocation T(16;17)43H (abbreviated T43H). The T43H/T43H homozygotes were fully fertile in both sexes, while +/T43H heterozygous males, but not females, were sterile with meiotic arrest at late pachynema. Inclusion of the t12 haplotype in trans to the T43H translocation resulted in enhanced asynapsis of the translocated autosome, ectopic phosphorylation of histone H2AX, persistence of RAD51 foci, and increased gene silencing around the translocation break. Increase was also on colocalization of unsynapsed chromatin with sex body. Remarkably, we found that transcriptional silencing of the unsynapsed autosomal chromatin precedes silencing of sex chromosomes. Based on the present knowledge, we conclude that interference of meiotic silencing of unsynapsed autosomes with meiotic sex chromosome inactivation is the most likely cause of asynapsis-related male sterility
Variations on the Stochastic Shortest Path Problem
In this invited contribution, we revisit the stochastic shortest path
problem, and show how recent results allow one to improve over the classical
solutions: we present algorithms to synthesize strategies with multiple
guarantees on the distribution of the length of paths reaching a given target,
rather than simply minimizing its expected value. The concepts and algorithms
that we propose here are applications of more general results that have been
obtained recently for Markov decision processes and that are described in a
series of recent papers.Comment: Invited paper for VMCAI 201
Validation of Decentralised Smart Contracts Through Game Theory and Formal Methods
Decentralised smart contracts represent the next step in the development of protocols that support the interaction of independent players without the presence of a coercing authority. Based on protocols a` la BitCoin for digital currencies, smart contracts are believed to be a potentially enabling technology for a wealth of future applications. The validation of such an early developing technology is as necessary as it is complex. In this paper we combine game theory and formal models to tackle the new challenges posed by the validation of such systems
Explicit Model Checking of Very Large MDP using Partitioning and Secondary Storage
The applicability of model checking is hindered by the state space explosion
problem in combination with limited amounts of main memory. To extend its
reach, the large available capacities of secondary storage such as hard disks
can be exploited. Due to the specific performance characteristics of secondary
storage technologies, specialised algorithms are required. In this paper, we
present a technique to use secondary storage for probabilistic model checking
of Markov decision processes. It combines state space exploration based on
partitioning with a block-iterative variant of value iteration over the same
partitions for the analysis of probabilistic reachability and expected-reward
properties. A sparse matrix-like representation is used to store partitions on
secondary storage in a compact format. All file accesses are sequential, and
compression can be used without affecting runtime. The technique has been
implemented within the Modest Toolset. We evaluate its performance on several
benchmark models of up to 3.5 billion states. In the analysis of time-bounded
properties on real-time models, our method neutralises the state space
explosion induced by the time bound in its entirety.Comment: The final publication is available at Springer via
http://dx.doi.org/10.1007/978-3-319-24953-7_1
A Proof System for Compositional Verification of Probabilistic Concurrent Processes
Abstract. We present a formal proof system for compositional verification of probabilistic concurrent processes. Processes are specified using an SOS-style process algebra with probabilistic operators. Properties are expressed using a probabilistic modal ”-calculus. And the proof system is formulated as a sequent calculus in which sequents are given a quantitative interpretation. A key feature is that the probabilistic scenario is handled by introducing the notion of Markov proof, according to which proof trees contain probabilistic branches and are required to satisfy a condition formulated byinterpretingthemas Markov Decision Processes. We present simple but illustrative examples demonstrating the applicability of the approach to the compositional verification of infinite state processes. Our main result is the soundness of the proof system, which is proved by applying the coupling method from probability theory to the game semantics of the probabilistic modal ”-calculus.
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