Probabilistic Verification of Discrete Event Systems Using Acceptance Sampling

Younes, Håkan L. S.; Simmons, Reid

doi:10.1007/3-540-45657-0_17

Cited by 288 publications

(287 citation statements)

References 15 publications

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“…One approach is to use numerical methods to compute the exact probability; however, this naive implementation suffers from the state explosion problem, and does not scale well to large-scale systems [6]. Statistical model checking provides an excellent solution to this problem, by estimating the probability using simulation and thus, avoiding a full state space search.…”

Section: Statistical Model Checkingmentioning

confidence: 99%

Methods to Expand Cell Signaling Models Using Automated Reading and Model Checking

Liang

Wang

Telmer

et al. 2017

Computational Methods in Systems Biology

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Abstract. Biomedical research results are being published at a high rate, and with existing search engines, the vast amount of published work is usually easily accessible. However, reproducing published results, either experimental data or observations is often not viable. In this work, we propose a framework to overcome some of the issues of reproducing previous research, and to ensure re-usability of published information. We present here a framework that utilizes the results from state-of-theart biomedical literature mining, biological system modeling and analysis techniques, and provides means to scientists to assemble and reason about information from voluminous, fragmented and sometimes inconsistent literature. The overall process of automated reading, assembly and reasoning can speed up discoveries from the order of decades to the order of hours or days. Our framework described here allows for rapidly conducting thousands of in silico experiments that are designed as part of this process.

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Section: Statistical Model Checkingmentioning

confidence: 99%

Methods to Expand Cell Signaling Models Using Automated Reading and Model Checking

Liang

Wang

Telmer

et al. 2017

Computational Methods in Systems Biology

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“…Sampling approaches, such as Ymer [25], use Monte Carlo sampling of execution paths to verify probabilistic systems. Simulation is typically used to generate sample paths and sufficient samples are taken to verify a system within a bound for false positives and a bound for false negatives.…”

Section: Discussionmentioning

confidence: 99%

Getting it right the first time: Robot mission guarantees in the presence of uncertainty

Lyons

Arkin

Nirmal

et al. 2013

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-Certain robot missions need to perform predictably in a physical environment that may only be poorly characterized in advance. We have previously developed an approach to establishing performance guarantees for behaviorbased controllers in a process-algebra framework. We extend that work here to include random variables, and we show how our prior results can be used to generate a Dynamic Bayesian Network for the coupled system of program and environment model. Verification is reduced to a filtering problem for this network. Finally, we present validation results that demonstrate the effectiveness of the verification of a multiple waypoint robot mission using this approach.

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“…Statistical model checking (SMC [41,25]) is a variety of probabilistic model checking (see e.g. [5]) that avoids an explicit representation or traversal of the state space; estimating the probability of a property from an empirical distribution of executions (simulations) of a system.…”

Section: Statistical Model Checkingmentioning

confidence: 99%

Group abstraction for assisted navigation of social activities in intelligent environments

Given-Wilson

Legay

Sedwards

et al. 2018

J Reliable Intell Environ

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The ACANTO project is developing robotic assistants to aid the confidence and recovery of older adults. A key requirement of these assistants is aiding with navigation in complex and potentially chaotic environments. Prior work has addressed this for a single user, using a single robotic assistant in an intelligent environment. However, for therapeutic purposes, ACANTO supports social groups and group activities. ACANTO's robotic assistants must therefore be able to plan the motion of groups of older adults walking together. This requires an efficient navigation solution that can handle large numbers of users and that can operate rapidly on embedded computing devices. To increase user confidence, the solution must encourage group cohesion without trying to impose its own rigid structure; it must try to maintain the natural (de facto) group structure despite unpredictable behaviours and environmental conditions. Our on-the-fly group motion planner addresses these challenges by: using intelligent environment information to develop behavioural traces, clustering traces to determine groups, constructing a predictive model of the groups as a whole, and finding an optimal suggested trajectory using Statistical Model Checking. In this work we describe our proposed approach in detail and validate some of its novel aspects on the ETH Zürich pedestrian motion dataset.

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Probabilistic Verification of Discrete Event Systems Using Acceptance Sampling

Cited by 288 publications

References 15 publications

Methods to Expand Cell Signaling Models Using Automated Reading and Model Checking

Methods to Expand Cell Signaling Models Using Automated Reading and Model Checking

Getting it right the first time: Robot mission guarantees in the presence of uncertainty

Group abstraction for assisted navigation of social activities in intelligent environments

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