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Frenetic at the SBST 2021 Tool Competition
TLDR
Frenetic is a genetic approach that leverages a curvature-based road representation to generate roads where the agent fails to stay within its lane, and tries to minimize the out of bound distance.
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The experiment model and validity frame in M&S
TLDR
This work describes the process of capturing the context of models and systems to provide truly reproducible experiment descriptions and how this information can be used for different purposes and in different scenarios.
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Modeling Frames
TLDR
A framework for the definition of model frames, which capture the context an activity/model is executed in and facilitate re-use, replacement, validation and verification of models, is described.
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KNN-Averaging for Noisy Multi-objective Optimisation
TLDR
The results of the ongoing research to counteract the effect of noisy fitness functions without requiring repeated executions of each solution are presented, including the viability of kNN-Avg on common benchmark problems and it is shown that it produces comparably good solutions whose fitness values are closer to the expected value.
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A Model Checker Collection for the Model Checking Contest Using Docker and Machine Learning
This paper introduces mcc4mcc, the Model Checker Collection for the Model Checking Contest, a tool that wraps multiple model checking solutions, and applies the most appropriate one based on the…
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CREST - A DSL for Reactive Cyber-Physical Systems
TLDR
CREST is a novel domain-specific language for the modelling of cyber-physical systems designed for the simple and clear modelling, simulation and verification of small-scale systems such as home and office automation, smart gardening systems and similar.
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Considering Execution Environment Resilience: A White-Box Approach
TLDR
ITEC is introduced, a tool for automated test case generation to support execution environment resilience in large-scaled, complex systems and one of its corner stones is a technique called semi-purification, a source code transformation technique to overcome limitations of existing tools and to set up the required system state for software testing.
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ML4CREST: Machine Learning for CPS Models
TLDR
This paper presents ML4CREST, a machine learning approach to automatically calibrate models using sensor measurements, which is well-suited for the calibration of the flow rates within an automated watering system, which allows precise simulation and prevents spillage.
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A domain-specific language approach to hybrid cps modelling
TLDR
This thesis describes the creation of the Continuous REactive SysTems language (CREST), a domain-specific language (DSL) dedicated to the combined modelling of physical resource flows and engineered behaviour that coherently merges architectural concerns, reactive dataflow and non-determinism.
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CREST - A Continuous, REactive SysTems DSL
TLDR
This paper introduces CREST, a domain-specific language for modeling entity behavior and resource transfers in CPS that aims to support CPS architects through clarity, comprehensiveness and analyzability.
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