Study of communication in a multi-agent system for collaborative learning scenarios

Riera, Adolfo; Lama, Manuel; Sánchez, Eduardo; Amorim, Ricardo; Vila, Xosé A.; Barro, Senén

doi:10.1109/empdp.2004.1271450

Cited by 7 publications

(2 citation statements)

References 8 publications

(5 reference statements)

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“…The study of Thaiupathump et al [8] shows that applying agent technology to elearning holds promise of improving learners' satisfaction and motivation. By using such technology the teaching process can be moved from human instructor to artificial agents [9]. Shi et al [10] propose an intelligent solution based on a connectionist approach.…”

Section: Architecturementioning

confidence: 99%

Developing e-Learning System using Mobile Agent Technology

Al-Sakran

2006 2nd International Conference on Information &Amp; Communication Technologies

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The purpose of this paper is to formulate a functional architecture that supports the e-learning objectives: study individually and cooperatively, anytime and anywhere, guide specific individuals to their most relevant community or communities of knowledge. The proposed architecture is based on using mobile agent technology integrated with Case-Based Reasoning (CBR) approach. This architecture gives users the ability to collect, share, distribute and reuse e-learning knowledge from heterogenous knowledge bases. The mobile agent technology is particularly suitable for developing distributed e-learning systems because it supports intelligent and distributed storage, allows an optimal personalized e-learning environment, and enhances modularity, reusability, flexibility and reliability. The model is employing CBR method to find a case in case base that is similar to the current problem.

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Section: Architecturementioning

confidence: 99%

Developing e-Learning System using Mobile Agent Technology

Al-Sakran

2006 2nd International Conference on Information &Amp; Communication Technologies

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“…• Autonomy is the agent's ability of either acting without human intervention [35] or of learning from experiences [52].…”

Section: Literature Surveymentioning

confidence: 99%

Multiagent approach for power system reconfiguration

Tulpule¹

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The objective of this thesis is to develop an agent based reconfiguration algorithm for power system application with termination detection and to provide communication architecture for these agents. The underlying maximum flow algorithm based on graph theory has proven the concept of autonomous distributed reconfiguration using multiagent systems. The new rule-based algorithm takes into account the requirement of bidirectional power flows, and special power routing for DC devices. Even though the algorithm is self-stabilizing and agents become idle after finite time, implementation of the solution within the actual power system requires that all agents be in an idle state. The process of detecting the idle state of all agents in a distributed environment is called termination detection. After surveying various methods for termination detection, a modified method that is suitable for reconfiguration algorithm is developed. The method uses wave algorithm with tree topology and provides the ability of detecting termination to each of the agents. These reconfiguration agents can perform satisfactorily only if they can communicate and share information with other agents in the system. Thus, it is extremely important to have a uniform agent communication language and messaging scheme among all the agents. With the Controller Area Network as hardware backbone for communication, a new message structure and communication protocol is developed based on CANOpen and standards specified by Foundation for Intelligent Physical Agents. The communication architecture provides easy interface between reconfiguration agents and other agent based systems. The designed algorithms are applied to the reconfiguration of power system on all electric ships. The system is simulated on multiple computers and includes a command and control center to monitor and supervise the entire operation of the system. The integrated system is tested for specific scenarios and statistics are recorded to analyze the algorithm performance and robustness. The evaluation of the system for correctness of reconfiguration solutions and the time required to complete the reconfiguration show promising results.

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