2021
DOI: 10.1007/978-3-030-83723-5_3
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TraceVis: Towards Visualization for Deep Statistical Model Checking

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Cited by 9 publications
(6 citation statements)
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“…Our Racetrack case study makes it easy to produce "heat maps", as a meaningful way to represent a partitioned perspective on the state space and sampling one member state from each set as a representative. With the TraceVis tool, we also showed how visualization techniques in 3D can help to get even more insights from the DSMC results and to display more information than in the simple heat maps [26,28]. We believe that such a representative analysis makes sense (e.g., to provide an overview for human users) in many application scenarios.…”
Section: Discussionmentioning
confidence: 99%
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“…Our Racetrack case study makes it easy to produce "heat maps", as a meaningful way to represent a partitioned perspective on the state space and sampling one member state from each set as a representative. With the TraceVis tool, we also showed how visualization techniques in 3D can help to get even more insights from the DSMC results and to display more information than in the simple heat maps [26,28]. We believe that such a representative analysis makes sense (e.g., to provide an overview for human users) in many application scenarios.…”
Section: Discussionmentioning
confidence: 99%
“…There are already works building up on DSMC giving evidence for the potential impact of the approach. The information delivered by DSMC has already been used to improve reinforcement learning strategies [32] and for the design of policy-analysis tools in synergy with interactive visualization techniques [26,28]. The most important work based on DSMC is MoGym [29], the integrated toolbox enabling the training and verification of machine-learned decisionmaking agents based on formal models, which bridges the reinforcement learning community to formal methods.…”
Section: Introductionmentioning
confidence: 99%
“…Deep RL with DSMC Specifics. Usually, learning NN is done on GPUs [43][44][45][46], but for a reasonable runtime comparison, we used a CPU infrastructure here. In addition, the random start setup [44]-during learning, the agent starts randomly from one of the free road cells instead of always from the same start cell-leads to significantly better learning performance.…”
Section: Methodsmentioning
confidence: 99%
“…But since the other methods we compare to can only start from a fixed cell, we used the normal start setup during learning for this paper, where the agent also starts its exploration runs from a single start position always. The NN we trained have an input layer of 15 neurons, two hidden layers of 64 neurons each and an output layer of 9 neurons encoding the nine possible acceleration values, as done in other case studies on Racetrack [43,44,46]. We start with the barto-small track shown in Fig.…”
Section: Methodsmentioning
confidence: 99%
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