Modeling Objects with Uncertain Behaviors.

Muñoz, Paula; Karkhanis, Priyanka; Brand, Mark van den; Vallecillo, Antonio

doi:10.5381/jot.2021.20.3.a8

Cited by 4 publications

(3 citation statements)

References 65 publications

(92 reference statements)

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“…Muñoz et al. [ 34 ] describe the use of UML and some bespoke modelling languages for capturing the structure and behaviour in an agent-based model of autonomically driving vehicles and pedestrians, developed for purposes of training and testing autonomic-driving AI systems. This work proposes some domain-modelling DSMLs—for example, the authors provide a DSML for specifying different levels of uncertainty of agents about road conditions and other agents based on levels of visibility, etc.…”

Section: Related Workmentioning

confidence: 99%

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Trustworthy agent-based simulation: the case for domain-specific modelling languages

Zschaler

Polack

2023

Softw Syst Model

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Simulation is a key tool for researching complex system behaviour. Agent-based simulation has been applied across domains, such as biology, health, economics and urban sciences. However, engineering robust, efficient, maintainable, and reliable agent-based simulations is challenging. We present a vision for engineering agent simulations comprising a family of domain-specific modelling languages (DSMLs) that integrates core software engineering, validation and simulation experimentation. We relate the vision to examples of principled simulation, to show how the DSMLs would improve robustness, efficiency, and maintainability of simulations. Focusing on how to demonstrate the fitness for purpose of a simulator, the envisaged approach supports bi-directional transparency and traceability between the original domain understanding to the implementation, interpretation of results and evaluation of hypotheses.

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Section: Related Workmentioning

confidence: 99%

“…Thus, the approach has aspects that are more low-level than the agent-modelling approaches in [13,16,21,41,42] mixed with approaches that are at a similar abstraction level to the languages introduced in [37] and proposed in our vision. Explicit models of fitness-for-purpose arguments are again not considered in [34].…”

Section: Domain Modellingmentioning

confidence: 99%

Trustworthy agent-based simulation: the case for domain-specific modelling languages

Zschaler

Polack

2023

Softw Syst Model

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“…Dataset authors may not always be completely sure about some aspects of the dataset (e.g., the provenance or the quality of some attributes). We plan to leverage existing works on expressing uncertainties in models (see [18] for instance) to enable the annotation of our DSL elements with uncertain values and expressions. DSL manipulation operations.…”

Section: Preliminary Evaluationmentioning

confidence: 99%

A domain-specific language for describing machine learning datasets

Giner-Miguelez¹,

Gómez²,

Cabot³

2022

Preprint

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Datasets play a central role in the training and evaluation of machine learning (ML) models. But they are also the root cause of many undesired model behaviors, such as biased predictions. To overcome this situation, the ML community is proposing a datacentric cultural shift where data issues are given the attention they deserve, and more standard practices around the gathering and processing of datasets start to be discussed and established.So far, these proposals are mostly high-level guidelines described in natural language and, as such, they are difficult to formalize and apply to particular datasets. In this sense, and inspired by these proposals, we define a new domain-specific language (DSL) to precisely describe machine learning datasets in terms of their structure, data provenance, and social concerns. We believe this DSL will facilitate any ML initiative to leverage and benefit from this data-centric shift in ML (e.g., selecting the most appropriate dataset for a new project or better replicating other ML results). The DSL is implemented as a Visual Studio Code plugin, and it has been published under an open source license.

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