| 1. |
- Cherif, Redha, et al.
(author)
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Software Development Process Simulation : Multi Agent-Based Simulation versus System Dynamics
- 2009
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Conference paper (peer-reviewed)abstract
- We present one of the first actual applications of Multi Agent-Based Simulation (MABS) to the field of software process simulation modelling (SPSM). Although there are some recent attempts to do this, we argue that these fail to take full advantage of the agency paradigm. Our model of the software development process integrates individual-level performance, cognition and artefact quality models in a common simulation framework. In addition, this framework allows the implementation of both MABS and System Dynamics (SD) simulators using the same basic models. As SD is the dominating approach within SPSM, we are able to make relevant and unique comparisons between it and MABS. This enabled us to uncover quite interesting properties of these approaches, e.g., that MABS re-flects the problem domain more realistically than SD.
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| 2. |
- Cherif, Redha, et al.
(author)
-
Software Development Process Simulation : Multi Agent-Based Simulation versus System Dynamics
- 2010
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In: Multi-Agent-Based Simulation X. - Berlin, Heidelberg : Springer. ; , s. 73-85
-
Conference paper (peer-reviewed)abstract
- We present one of the first actual applications of Multi Agent-Based Simulation (MABS) to the field of software process simulation modelling (SPSM). Although there are some recent attempts to do this, we argue that these fail to take full advantage of the agency paradigm. Our model of the software development process integrates individual-level performance, cognition and artefact quality models in a common simulation framework. In addition, this framework allows the implementation of both MABS and System Dynamics (SD) simulators using the same basic models. As SD is the dominating approach within SPSM, we are able to make relevant and unique comparisons between it and MABS. This enabled us to uncover quite interesting properties of these approaches, e.g., that MABS reflects the problem domain more realistically than SD.
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| 3. |
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| 4. |
- Lavesson, Niklas, et al.
(author)
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APPrOVE : Application-oriented Validation and Evaluation of Supervised Learners
- 2010
-
Conference paper (peer-reviewed)abstract
- Learning algorithm evaluation is usually focused on classification performance. However, the characteristics and requirements of real-world applications vary greatly. Thus, for a particular application, some evaluation criteria are more important than others. In fact, multiple criteria need to be considered to capture application-specific trade-offs. Many multi-criteria methods can be used for the actual evaluation but the problems of selecting appropriate criteria and metrics as well as capturing the trade-offs still persist. This paper presents a framework for application-oriented validation and evaluation (APPrOVE). The framework includes four sequential steps that together address the aforementioned problems and its use in practice is demonstrated through a case study.
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| 5. |
- Mengistu, Dawit, et al.
(author)
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A grid based simulation environment for mobile distributed applications
- 2007
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In: 2007 International Conference on Multimedia and Ubiquitous Engineering. - Los Alamitos, Calif. : IEEE Computer Society. - 9780769527772 ; , s. 1093-1099
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Conference paper (peer-reviewed)abstract
- Using simulation environments to support mobile distributed application development has many benefits. Since mobile devices can be modeled as autonomous intelligent agents, the multi agent based simulation (MABS) approach is useful for such applications. In this paper, we propose a MABS platform on the computational Grid for mobile distributed computing. A prototype of the proposed platform is implemented and its performance characteristics are studied The findings of the experiment show that a Grid based MABS platform can provide a scalable simulation environment for mobile distributed applications.
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| 6. |
- Mengistu, Dawit, et al.
(author)
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Middleware support for performance improvement of MABS applications in the Grid environment
- 2008
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In: Multi-agent-based simulation VIII. - Berlin : Springer Berlin/Heidelberg. - 9783540709152 - 9783540709169 ; , s. 20-35
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Conference paper (peer-reviewed)abstract
- The computational Grid is an infrastructure which enables the execution of applications demanding huge computing resources. Hence, it can be the right environment for large-scale Multi-agent based simulation (MABS) applications. However, due to the nature of the Grid and the characteristics of MABS, achieving optimum performance poses a great challenge. Performance study of MABS applications is therefore a necessary undertaking which requires an understanding of these characteristics and the extent of their influence. Moreover, owing to the dynamicity and heterogeneity of the Grid, it is difficult to achieve performance gains without a middleware support for application deployment and dynamic reconfiguration. This paper presents a study of the key features of MABS applications that affect performance and proposes a supportive middleware to MABS platforms. Experiments show that the proposed middleware can bring performance improvement for MABS applications on the Grid.
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| 7. |
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| 8. |
- Tegen, Agnes, et al.
(author)
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A Taxonomy of Interactive Online Machine Learning Strategies
- 2020
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In: ECML PKDD 2020. - Cham : Springer. - 9783030676605 - 9783030676612 ; , s. 1-17
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Conference paper (peer-reviewed)abstract
- In interactive machine learning, human users and learning algorithms work together in order to solve challenging learning problems, e.g. with limited or no annotated data or trust issues. As annotating data can be costly, it is important to minimize the amount of annotated data needed for training while still getting a high classification accuracy. This is done by attempting to select the most informative data instances for training, where the amount of instances is limited by a labelling budget. In an online learning setting, the decision of whether or not to select an instance for labelling has to be done on-the-fly, as the data arrives in a sequential order and is only valid for a limited time period. We present a taxonomy of interactive online machine learning strategies. An interactive learning strategy determines which instances to label in an unlabelled dataset. In the taxonomy we differentiate between interactive learning strategies when the computer controls the learning process (active learning) and those when human users control the learning process (machine teaching). We then make a distinction between what triggers the learning: active learning could be triggered by uncertainty, time, or randomly, whereas machine teaching could be triggered by errors, state changes, time, or factors related to the user. We also illustrate the taxonomy by implementing versions of the different strategies and performing experiments on a benchmark dataset as well as on a synthetically generated dataset. The results show that the choice of interactive learning strategy affects performance, especially in the beginning of the online learning process, when there is a limited amount of labelled data.
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| 9. |
- Muccini, Henry, et al.
(author)
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An IoT Software Architecture for an Evacuable Building Architecture
- 2019
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In: Proceedings of the 52nd Hawaii International Conference on System Sciences. - Honolulu : ScholarSpace. - 9780998133126
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Conference paper (peer-reviewed)abstract
- This paper presents a computational componentdesigned to improve and evaluate emergency handlingplans.In real-time, the component operates as thecore of an Internet of Things (IoT) infrastructureaimed at crowd monitoring and optimum evacuationpaths planning. In this case, a software architecturefacilitates achieving the minimum time necessary toevacuate people from a building.In design-time,the component helps discovering the optimal buildingdimensions for a safe emergency evacuation, evenbefore (re-) construction of a building. The space andtime dimension are discretized according to metrics andmodels in literature. The component formulates andsolves a linearized, time-indexed flow problem on anetwork that represents feasible movements of people ata suitable frequency. The CPU time to solve the modelis compliant with real-time use. The application of themodel to a real location with real data testifies the modelcapability to optimize the safety standards by smallchanges in the building dimensions, and guarantees anoptimal emergency evacuation performance.
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| 10. |
- Alkhabbas, Fahed, et al.
(author)
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ART4FL : An Agent-based Architectural Approach for Trustworthy Federated Learning in the IoT
- 2023
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In: 8th International Conference on Fog and Mobile Edge Computing, FMEC 2023. - : Institute of Electrical and Electronics Engineers (IEEE). - 9798350316971 - 9798350316988 ; , s. 270-275
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Conference paper (peer-reviewed)abstract
- The integration of the Internet of Things (IoT) and Machine Learning (ML) technologies has opened up for the development of novel types of systems and services. Federated Learning (FL) has enabled the systems to collaboratively train their ML models while preserving the privacy of the data collected by their IoT devices and objects. Several FL frameworks have been developed, however, they do not enable FL in open, distributed, and heterogeneous IoT environments. Specifically, they do not support systems that collect similar data to dynamically discover each other, communicate, and negotiate about the training terms (e.g., accuracy, communication latency, and cost). Towards bridging this gap, we propose ART4FL, an end-to-end framework that enables FL in open IoT settings. The framework enables systems’ users to configure agents that participate in FL on their behalf. Those agents negotiate and make commitments (i.e., contractual agreements) to dynamically form federations. To perform FL, the framework deploys the needed services dynamically, monitors the training rounds, and calculates agents’ trust scores based on the established commitments. ART4FL exploits a blockchain network to maintain the trust scores, and it provides those scores to negotiating agents’ during the federations’ formation phase. © 2023 IEEE.
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