Control/Monitoring System of APS Using Multi–Agent Techniques

Hammami, Salah; Moalla, E.; Tnazefti-Kerkeni, Insaf

doi:10.1016/s1474-6670(17)39500-9

Cited by 1 publication

(1 citation statement)

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“…The architectural design is critical to managing the increasing size and complexity of adaptive multi-agent E-Learning systems. Therefore, we model and specify our system using SART/ OPN [26]. The SA-RT/OPN methodology is proposed for the specification and design of complex systems.…”

Section: The Sa-rt/opn Modelmentioning

confidence: 99%

Adaptive e‐learning system based on agents and object petri nets (AELS‐A/OPN)

Hammami

Mathkour

2013

Comp Applic In Engineering

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Agent technology plays an important role in today's software development at the industrial level and brings advantages to the development of educational applications. The main focus of the multi‐agent systems community has been on the development of concepts, architectures, interaction techniques, and general approaches to the analysis and specification of multi‐agent systems. We introduce herein a novel concept involving the development of an e‐Learning system architecture that explores several recent technologies, including multi‐agent technology and adaptive e‐Learning. The main concern of our research is twofold: to build a multi‐agent architecture that provides the ability to adapt to the learner's preferences in the e‐Learning environment and to analyze and control the communication and interactions among the different agents of the proposed system. Toward this aim, we present a distributed intelligent blackboard agent that ensures the communication among the participating agents. Furthermore, we describe a formal model that can be used to specify the message exchanges and their synchronization using Object Petri Nets. An exploratory implementation has been developed and conducted in practice. © 2013 Wiley Periodicals, Inc. Comput Appl Eng Educ 23:170–190, 2015; View this article online at http://wileyonlinelibrary.com/journal/cae; DOI

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Section: The Sa-rt/opn Modelmentioning

confidence: 99%