6,003 research outputs found
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
Expert Elicitation for Reliable System Design
This paper reviews the role of expert judgement to support reliability
assessments within the systems engineering design process. Generic design
processes are described to give the context and a discussion is given about the
nature of the reliability assessments required in the different systems
engineering phases. It is argued that, as far as meeting reliability
requirements is concerned, the whole design process is more akin to a
statistical control process than to a straightforward statistical problem of
assessing an unknown distribution. This leads to features of the expert
judgement problem in the design context which are substantially different from
those seen, for example, in risk assessment. In particular, the role of experts
in problem structuring and in developing failure mitigation options is much
more prominent, and there is a need to take into account the reliability
potential for future mitigation measures downstream in the system life cycle.
An overview is given of the stakeholders typically involved in large scale
systems engineering design projects, and this is used to argue the need for
methods that expose potential judgemental biases in order to generate analyses
that can be said to provide rational consensus about uncertainties. Finally, a
number of key points are developed with the aim of moving toward a framework
that provides a holistic method for tracking reliability assessment through the
design process.Comment: This paper commented in: [arXiv:0708.0285], [arXiv:0708.0287],
[arXiv:0708.0288]. Rejoinder in [arXiv:0708.0293]. Published at
http://dx.doi.org/10.1214/088342306000000510 in the Statistical Science
(http://www.imstat.org/sts/) by the Institute of Mathematical Statistics
(http://www.imstat.org
Strategy Synthesis for Autonomous Agents Using PRISM
We present probabilistic models for autonomous agent search and retrieve missions derived from Simulink models for an Unmanned Aerial Vehicle (UAV) and show how probabilistic model checking and the probabilistic model checker PRISM can be used for optimal controller generation. We introduce a sequence of scenarios relevant to UAVs and other autonomous agents such as underwater and ground vehicles. For each scenario we demonstrate how it can be modelled using the PRISM language, give model checking statistics and present the synthesised optimal controllers. We conclude with a discussion of the limitations when using probabilistic model checking and PRISM in this context and what steps can be taken to overcome them. In addition, we consider how the controllers can be returned to the UAV and adapted for use on larger search areas
Modeling Stochastic Crop Yield Expectations with a Limiting Beta Distribution
The use of plausible stochastic price processes in price risk analysis has allowed advances not seen in crop yield risk analysis. This study develops a stochastic process for yield modeling and risk management. The Pólya urn process is an internally consistent dynamic representation of yield expectations over a growing season that accommodates agronomic events such as growing degree days. The limiting distribution is the commonly used beta distribution. Binomial tree analysis of the process allows us to explore hedging decisions and crop valuation. The method is empirically flexible to accommodate alternative assumptions on the growing environment, such as intra-season input decisions.crop abandonment, crop insurance, derivative analysis, growing degree days, Pólya’s urn, stochastic process, Crop Production/Industries,
Frontiers of real-time data analysis
This paper describes the existing research (as of February 2008) on real-time data analysis, divided into five areas: (1) data revisions; (2) forecasting; (3) monetary policy analysis; (4) macroeconomic research; and (5) current analysis of business and financial conditions. In each area, substantial progress has been made in recent years, with researchers gaining insight into the impact of data revisions. In addition, substantial progress has been made in developing better real-time data sets around the world. Still, additional research is needed in key areas, and research to date has uncovered even more fruitful areas worth exploring.Macroeconomics
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