2021
DOI: 10.5381/jot.2021.20.3.a8
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Modeling Objects with Uncertain Behaviors.

Abstract: Modeling the behavior of complex systems that operate in real environments, deal with physical elements, or interact with humans is a challenging task. It involves the explicit representation of aspects of behavioral uncertainty that are inherent in the system, but generally neglected in software models. In this paper, we focus on the explicit representation of the behavior of objects of complex systems, considering their motivations, randomness, and the different types of underlying uncertainty that affect th… Show more

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Cited by 4 publications
(3 citation 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%
See 1 more Smart Citation
“…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%
“…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%
“…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%