Stochastic Timed Automata

Bertrand, Nathalie; Bouyer, Patricia; Brihaye, Thomas; Menet, Quentin; Baier, Christel; Groesser, Marcus; Jurdziński, Marcin

doi:10.2168/lmcs-10(4:6)2014

Cited by 40 publications

(41 citation statements)

References 40 publications

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“…Computing reachability probabilities in PTA is decidable via a region graph-like construction. Whereas in PTA clocks are deterministic, stochastic timed automata [25] (STA) provide a stochastic interpretation to clocks. In STA, unbounded clocks are interpreted as negative exponential distributions, whereas bounded clocks obey a uniform distribution.…”

Section: Examplementioning

confidence: 99%

The 10,000 Facets of MDP Model Checking

Baier

Hermanns

Katoen

2019

Lecture Notes in Computer Science

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244

283

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This paper presents a retrospective view on probabilistic model checking. We focus on Markov decision processes (MDPs, for short). We survey the basic ingredients of MDP model checking and discuss its enormous developments since the seminal works by Courcoubetis and Yannakakis in the early 1990s. We discuss in particular the manifold facets of this field of research by surveying the verification of various MDP extensions, rich classes of properties, and their applications.

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Section: Examplementioning

confidence: 99%

The 10,000 Facets of MDP Model Checking

Baier

Hermanns

Katoen

2019

Lecture Notes in Computer Science

Self Cite

244

283

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“…Stochastic Timed Automaton (STA) [13] fulfill these requirements, as it is a probabilistic automaton with probabilistic distribution associated to location delay.…”

Section: B Model Definitionmentioning

confidence: 99%

Life Habits Modeling with Stochastic Timed Automata in Ambient Assisted Living

Fouquet

Faraut

Lesage

2020

2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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Recent improvements in connected tools and learning algorithms allow new opportunities in the field of Ambient Assisted Living (AAL). However, smart home inhabitant's life habits are often required to obtain adequate results for energy management, security, Health at Home (HaH), and numerous other applications. In this paper, a model for life routines representation and algorithms for its generation is introduced. Study on the state of the art exposes that activity ordering and duration are key features of human behavior. Consequently, the presented approach focuses on a higher level of semantic by observing activities performed by the inhabitant rather than the sensor logs, which allow for better understanding of his comportment and universality of the model for multiple aims. Stochastic Time Automata (STA) is proposed as it adequately models activity ordering with probability associated to edges and activity duration through probability distribution associated to location delay. Presented approach does not require specific equipment besides sensors required for activity recognition and is versatile enough to be used in various applications. A case study highlights the relevancy of the chosen features and demonstrates that the proposed model is efficient to depict and understand inhabitants' life habits.

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“…Although not as ubiquitous as FSM, TA are used widely to replicate asynchronous applications and circuits, finding logical errors in communication protocols [40], design pacemakers [41], model Bitcoin contracts [42], mimic home care plans [43], simulate biological pathway dynamics [44] and implement Publish-Subscribe structures [45]. TA transitions can also be stochastic or be triggered by external factors making them a suitable tool to model the human reaction time and attention states [46]. Despite their inherent aptitude, TA have only been used to model particle hopping models [25] but not to replicate more complex CF dynamics.…”

Section: Related Workmentioning

confidence: 99%

Finite State Machines and Timed Automata: A Hierarchical Approach for Integrated Traffic Microsimulations

Lehmann¹,

Roop²,

Ranjitkar³

2018

jtle

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Microscopic traffic simulations capture the trajectories of individual drivers as responses to stimuli from their surroundings (i.e. other vehicles or road conditions). Mathematically, these models are usually designed with differential equations or as sets of integerbased rules. Since both approaches have disadvantages, we propose an in-between approach built with Timed Automata and Finite State Machines (FSM) to reproduce the human behaviour. The fundamental idea is to model the switches between a limited set of discrete acceleration levels with a FSM and derive all other trajectory features from there. The duration for which this constant acceleration is maintained is not fixed and is modelled by a (probabilistic) Timed Automaton (TA). With this arrangement, the complexity of CF behaviour can be represented with high computational efficiency in large-scale simulations without sacrificing model fidelity. It also captures the intrinsic randomness in human driving and enables the incorporation of directly observably statistical CF properties. This paper identifies the best-correlated stimulus-response factors, analyses state machine properties of certain trajectory features and finally shows how several state machines can be hierarchically organised with the subsumption architecture. 

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Stochastic Timed Automata

Cited by 40 publications

References 40 publications

The 10,000 Facets of MDP Model Checking

The 10,000 Facets of MDP Model Checking

Life Habits Modeling with Stochastic Timed Automata in Ambient Assisted Living

Finite State Machines and Timed Automata: A Hierarchical Approach for Integrated Traffic Microsimulations

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