Introducing B-Sequenced Petri Nets as a CPN Sub-class for Safe Train Control

Boudi, Zakaryae; Wakrime, Abderrahim Ait; Collart-Dutilleul, Simon; Haloua, Mohamed

doi:10.5220/0007725103500358

“…By repeatedly adjusting the environmental model and our own model, we eventually learn to operate in an unknown environment. This also ensure the security of systems [18,19,6,20].…”

mentioning

confidence: 98%

The Verification and Testing on Reactive Robot Cooperating Systems

Pavakoamiea

2023

Preprint

0

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<p>In this paper, an robot parallel evolution design algorithm is proposed, based on the idea of module network, and the learning method is verified and tested as well. The learning method of collision avoidance, approach and wall switching behavior of evolutionary robots based on neural network method is optimized. The evolutionary robot can autonomously realize the behaviors of collision avoidance, movement, replication and attack. In the simulation environment of this paper, the obstacles in the environment are represented by a randomly generated rectangular region with the largest side length. The shape and location of obstacles are evenly distributed. The simulated environment has an approach target but no obstacles, and these approach targets are placed randomly and scattered in the environment. The formation of behavior is integrated with basic behaviors such as collision avoidance and approach, so that the robot is gradually enhanced based on the growth of neural network.</p>

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“…By repeatedly adjusting the environmental model and our own model, we eventually learn to operate in an unknown environment. This also ensure the security of systems [18,19,6,20].…”

mentioning

confidence: 98%

The Verification and Testing on Reactive Robot Cooperating Systems

Pavakoamiea

2023

Preprint

0

View full text Add to dashboard Cite

<p>In this paper, an robot parallel evolution design algorithm is proposed, based on the idea of module network, and the learning method is verified and tested as well. The learning method of collision avoidance, approach and wall switching behavior of evolutionary robots based on neural network method is optimized. The evolutionary robot can autonomously realize the behaviors of collision avoidance, movement, replication and attack. In the simulation environment of this paper, the obstacles in the environment are represented by a randomly generated rectangular region with the largest side length. The shape and location of obstacles are evenly distributed. The simulated environment has an approach target but no obstacles, and these approach targets are placed randomly and scattered in the environment. The formation of behavior is integrated with basic behaviors such as collision avoidance and approach, so that the robot is gradually enhanced based on the growth of neural network.</p>

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“…This method has been effectively used in different systems where no bugs were detected. For instance, the driverless meteor in Paris worked correctly and no bugs were identified after the theoretical proofs, neither at the functional approval (Boudi et al 2019). This achievement encourages Alstom and Siemens Transportation Systems to use this method to develop future metros (Vistbakka and Troubitsyna 2018).…”

mentioning

confidence: 99%

Formal approach to model complex adaptive computing systems

Jarrar¹,

Wakrime

²

,

Balouki³

2020

Complex Adapt Syst Model

Self Cite

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Complex adaptive systems provide a significant number of concepts such as reaction, interaction, adaptation, and evolution. In general, these concepts are modelled employing different techniques which give an inexplicit vision on the system. Therefore, all concepts must be carefully modelled using the same approach to avoid contradiction and guarantee system homogeneity and correctness. However, developing a computing system that includes all these concepts using the same approach is not an easy task and requires a perfect understanding of the system’s behaviour. In this paper, we contribute as stepwise towards proposing an approach to model the most important concepts of complex adaptive systems while ensuring homogeneity and the correctness of models. For this aim, we present five standard agent-based models formalizing agent properties, reaction, interaction, adaptation, and evolution. These models are adapted to all cases of complex adaptive systems since they include an abstract description of these concepts. To implement our approach formally, we choose the Event-B method due to the strong assurance of bugs’ absence that it guarantees. Besides, it supports horizontal and vertical refinement which facilitates the specification process. Furthermore, the approach of this paper addresses the very abstract level of modelling which expand the use of this approach to other formal methods and tools.

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Introducing B-Sequenced Petri Nets as a CPN Sub-class for Safe Train Control

Cited by 6 publications

References 0 publications

The Verification and Testing on Reactive Robot Cooperating Systems

The Verification and Testing on Reactive Robot Cooperating Systems

The Verification and Testing on Reactive Robot Cooperating Systems

Formal approach to model complex adaptive computing systems

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