Executing Scenario-Based Specification with Dynamic Generation of Rich Events

Harel, David; Katz, Guy; Marron, Assaf; Sadon, Aviran; Weiss, Gera

doi:10.1007/978-3-030-37873-8_11

Cited by 7 publications

(5 citation statements)

References 35 publications

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“…In practice, SBM is supported in a variety of textual and visual frameworks. Notable examples include the language of live sequence charts (LSC), where SBM concepts are applied to produce modal sequence dia-grams (Damm and Harel, 2001;Harel and Marelly, 2003); implementations in various high level languages, such as Java, C, C++, Erlang, JavaScript, and Python (see, e.g., (Harel et al, 2012b)); and various domain specific languages (Greenyer et al, 2016a) and extensions (Harel et al, 2020;Katz et al, 2019).…”

Section: Scenario-based Modelingmentioning

confidence: 99%

Scenario-Assisted Deep Reinforcement Learning

Yerushalmi¹,

Amir²,

Elyasaf³

et al. 2022

Preprint

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Deep reinforcement learning has proven remarkably useful in training agents from unstructured data. However, the opacity of the produced agents makes it difficult to ensure that they adhere to various requirements posed by human engineers. In this work-in-progress report, we propose a technique for enhancing the reinforcement learning training process (specifically, its reward calculation), in a way that allows human engineers to directly contribute their expert knowledge, making the agent under training more likely to comply with various relevant constraints. Moreover, our proposed approach allows formulating these constraints using advanced model engineering techniques, such as scenario-based modeling. This mix of black-box learning-based tools with classical modeling approaches could produce systems that are effective and efficient, but are also more transparent and maintainable. We evaluated our technique using a case-study from the domain of internet congestion control, obtaining promising results.

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Section: Scenario-based Modelingmentioning

confidence: 99%

Scenario-Assisted Deep Reinforcement Learning

Yerushalmi¹,

Amir²,

Elyasaf³

et al. 2022

Preprint

Self Cite

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“…In practice, the discovery of an assignment that satisfies the given constraints can be performed using various automated solvers, such as LP or SMT solvers (Harel et al, 2020). Because we restrict our constraints to first-order, quantifier-free linear real arithmetic, they can be resolved in polynomial time (Barrett and Tinelli, 2018).…”

Section: Scenario-based Modeling With Rich Eventsmentioning

confidence: 99%

Towards Repairing Scenario-Based Models with Rich Events

Katz¹

2021

Preprint

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Repairing legacy systems is a difficult and error-prone task: often, limited knowledge of the intricacies of these systems could make an attempted repair result in new errors. Consequently, it is desirable to repair such systems in an automated and sound way. Here, we discuss our ongoing work on the automated repair of scenario-based models: fully executable models that describe a system using scenario objects that model its individual behaviors. We show how rich, scenario-based models can be model-checked, and then repaired to prevent various safety violations. The actual repair is performed by adding new scenario objects to the model, and without altering existing ones -in a way that is well aligned with the principles of scenario-based modeling. In order to automate our repair approach, we leverage off-the-shelf SMT solvers. We describe the main principles of our approach, and discuss our plans for future work.

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Section: Related Workmentioning

confidence: 99%

“…These aspects include the automatic repair (Harel et al, 2012a), verification (Harel et al, 2015b), synthesis (Greenyer et al, 2016a) and optimization (Harel et al, 2015a;Greenyer et al, 2016b;Steinberg et al, 2017;Steinberg et al, 2018;Harel et al, 2020) Harel et al, 2018), which seeks to transform the computer into a proactive team member, capable of developing complex models alongside human engineers.…”

Section: Related Workmentioning

confidence: 99%

Guarded Deep Learning using Scenario-based Modeling

Katz

2020

Proceedings of the 8th International Conference on Model-Driven Engineering and Software Development

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Deep neural networks (DNNs) are becoming prevalent, often outperforming manually-created systems. Unfortunately, DNN models are opaque to humans, and may behave in unexpected ways when deployed. One approach for allowing safer deployment of DNN models calls for augmenting them with hand-crafted override rules, which serve to override decisions made by the DNN model when certain criteria are met. Here, we propose to bring together DNNs and the well-studied scenario-based modeling paradigm, by expressing these override rules as simple and intuitive scenarios. This approach can lead to override rules that are comprehensible to humans, but are also sufficiently expressive and powerful to increase the overall safety of the model. We describe how to extend and apply scenario-based modeling to this new setting, and demonstrate our proposed technique on multiple DNN models.

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Executing Scenario-Based Specification with Dynamic Generation of Rich Events

Cited by 7 publications

References 35 publications

Scenario-Assisted Deep Reinforcement Learning

Scenario-Assisted Deep Reinforcement Learning

Towards Repairing Scenario-Based Models with Rich Events

Guarded Deep Learning using Scenario-based Modeling

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