Michaela Klauck
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View article: Towards Verifying Robotic Systems Using Statistical Model Checking in STORM
Towards Verifying Robotic Systems Using Statistical Model Checking in STORM Open
Robust autonomy and interaction of robots with their environment, even in rare or new situations, is an ultimate goal of robotics research. We settle on Statistical Model Checking (SMC) for the benefit of robustness of robot deliberation a…
View article: Towards a Verifiable Toolchain for Robotics
Towards a Verifiable Toolchain for Robotics Open
There is a growing need for autonomous robots to complete complex tasks robustly in dynamic and unstructured environments. However, current robot performance is limited to simple tasks in controlled environments. To improve robot autonomy …
View article: Towards Safe Autonomous Driving: Model Checking a Behavior Planner during Development
Towards Safe Autonomous Driving: Model Checking a Behavior Planner during Development Open
Automated driving functions are among the most critical software components to develop. Before deployment in series vehicles, it has to be shown that the functions drive safely and in compliance with traffic rules. Despite the coverage tha…
View article: DSMC Evaluation Stages: Fostering Robust and Safe Behavior in Deep Reinforcement Learning – Extended Version
DSMC Evaluation Stages: Fostering Robust and Safe Behavior in Deep Reinforcement Learning – Extended Version Open
Neural networks (NN) are gaining importance in sequential decision-making. Deep reinforcement learning (DRL), in particular, is extremely successful in learning action policies in complex and dynamic environments. Despite this success, how…
View article: Artifact for the Scalability Study of the STTT Paper "Analyzing Neural Network Behavior through Deep Statistical Model Checking"
Artifact for the Scalability Study of the STTT Paper "Analyzing Neural Network Behavior through Deep Statistical Model Checking" Open
Scripts and infrastructure for the scalability study on DSMC published in the STTT paper "Analyzing Neural Network Behavior through Deep Statistical Model Checking".
View article: Artifact for the Scalability Study of the STTT Paper "Analyzing Neural Network Behavior through Deep Statistical Model Checking"
Artifact for the Scalability Study of the STTT Paper "Analyzing Neural Network Behavior through Deep Statistical Model Checking" Open
Scripts and infrastructure for the scalability study on DSMC published in the STTT paper "Analyzing Neural Network Behavior through Deep Statistical Model Checking".
View article: Artifact of Infrastructure and Tools in the Context of Deep Statistical Model Checking
Artifact of Infrastructure and Tools in the Context of Deep Statistical Model Checking Open
This artifact contains all infrastructure, tools, and additional material used in the context of Deep Statistical Model Checking (DSMC). This includes the DSMC implementation in modes of the Modest Toolset, the infrastructure used to perfo…
View article: Artifact of Infrastructure and Tools in the Context of Deep Statistical Model Checking
Artifact of Infrastructure and Tools in the Context of Deep Statistical Model Checking Open
This artifact contains all infrastructure, tools, and additional material used in the context of Deep Statistical Model Checking (DSMC). This includes the DSMC implementation in modes of the Modest Toolset, the infrastructure used to perfo…
View article: MoGym: Using Formal Models for Training and Verifying Decision-making Agents
MoGym: Using Formal Models for Training and Verifying Decision-making Agents Open
M o G ym , is an integrated toolbox enabling the training and verification of machine-learned decision-making agents based on formal models, for the purpose of sound use in the real world. Given a formal representation of a decision-making…
View article: The Modest State of Learning, Sampling, and Verifying Strategies
The Modest State of Learning, Sampling, and Verifying Strategies Open
Optimal decision-making under stochastic uncertainty is a core problem tackled in artificial intelligence/machine learning (AI), planning, and verification. Planning and AI methods aim to find good or optimal strategies to maximise rewards…
View article: The 2020 Comparison of Tools for the Analysis of Quantitative Formal Models: Results and Reproduction
The 2020 Comparison of Tools for the Analysis of Quantitative Formal Models: Results and Reproduction Open
This archive contains detailed results from QComp 2020 as well as the necessary scripts and data to reproduce them. Visit http://qcomp.org for more information for QComp. Overview of Contents - `qcomp.org/` contains the state of our websit…
View article: The 2020 Comparison of Tools for the Analysis of Quantitative Formal Models: Results and Reproduction
The 2020 Comparison of Tools for the Analysis of Quantitative Formal Models: Results and Reproduction Open
This archive contains detailed results from QComp 2020 as well as the necessary scripts and data to reproduce them. Visit http://qcomp.org for more information for QComp. Overview of Contents - `qcomp.org/` contains the state of our websit…
View article: Momba: JANI Meets Python
Momba: JANI Meets Python Open
A duplicate of https://zenodo.org/record/5758844. Reason: The submitter forgot to indicate the DOI before publishing, so it got another one assigned automatically, which is unchangeable.
View article: On Correctness, Precision, and Performance in Quantitative Verification
On Correctness, Precision, and Performance in Quantitative Verification Open
Quantitative verification tools compute probabilities, expected rewards, or steady-state values for formal models of stochastic and timed systems. Exact results often cannot be obtained efficiently, so most tools use floating-point arithme…
View article: Tooling, Data and Results for "Components in Probabilistic Systems: Suitable by Construction"
Tooling, Data and Results for "Components in Probabilistic Systems: Suitable by Construction" Open
The tooling, data and results for the racetrack case study in the paper Components in Probabilistic Systems: Suitable by Construction, ISoLA 2020, DOI
View article: Tooling, Data and Results for "Components in Probabilistic Systems: Suitable by Construction"
Tooling, Data and Results for "Components in Probabilistic Systems: Suitable by Construction" Open
The tooling, data and results for the racetrack case study in the paper Components in Probabilistic Systems: Suitable by Construction, ISoLA 2020, DOI
View article: TraceVis: Visualization for DSMC: tool, demonstration video, data
TraceVis: Visualization for DSMC: tool, demonstration video, data Open
Tool demonstration video and source code of TraceVis, the visualization tool for Deep Statistical Model Checking, presented in the paper TraceVis: Towards Visualization for Deep Statistical Model Checking, published at ISoLA 2020 (9th Inte…
View article: TraceVis: Visualization for DSMC: tool, demonstration video, data
TraceVis: Visualization for DSMC: tool, demonstration video, data Open
Tool demonstration video and source code of TraceVis, the visualization tool for Deep Statistical Model Checking, presented in the paper TraceVis: Towards Visualization for Deep Statistical Model Checking, published at ISoLA 2020 (9th Inte…
View article: Bridging the Gap Between Probabilistic Model Checking and Probabilistic Planning: Survey, Compilations, and Empirical Comparison
Bridging the Gap Between Probabilistic Model Checking and Probabilistic Planning: Survey, Compilations, and Empirical Comparison Open
Markov decision processes are of major interest in the planning community as well as in the model checking community. But in spite of the similarity in the considered formal models, the development of new techniques and methods happened la…
View article: Models and Infrastructure used in "Deep Statistical Model Checking"
Models and Infrastructure used in "Deep Statistical Model Checking" Open
This repository contains the models and all other infrastructure (learning procedure, NNs, Jani generator, maps, modes & mcsta binaries) used in the FORTE 2020 paper "Deep Statistical Model Checking".
View article: Models and Infrastructure used in "Deep Statistical Model Checking"
Models and Infrastructure used in "Deep Statistical Model Checking" Open
This repository contains the models and all other infrastructure (learning procedure, NNs, Jani generator, maps, modes & mcsta binaries) used in the FORTE 2020 paper "Deep Statistical Model Checking".
View article: Let's Learn Their Language? A Case for Planning with Automata-Network Languages from Model Checking
Let's Learn Their Language? A Case for Planning with Automata-Network Languages from Model Checking Open
It is widely known that AI planning and model checking are closely related. Compilations have been devised between various pairs of language fragments. What has barely been voiced yet, though, is the idea to let go of one's own modeling la…
View article: Towards Dynamic Dependable Systems Through Evidence-Based Continuous Certification
Towards Dynamic Dependable Systems Through Evidence-Based Continuous Certification Open
International audience
View article: The Quantitative Verification Benchmark Set
The Quantitative Verification Benchmark Set Open
We present an extensive collection of quantitative models to facilitate the development, comparison, and benchmarking of new verification algorithms and tools. All models have a formal semantics in terms of extensions of Markov chains, are…
View article: Compiling Probabilistic Model Checking into Probabilistic Planning
Compiling Probabilistic Model Checking into Probabilistic Planning Open
It has previously been observed that the verification of safety properties in deterministic model-checking frameworks can be compiled into classical planning. A similar connection exists between goal probability analysis on either side, ye…