Using Semantic Negotiation for Ontology Enrichment in e-Learning Multi-agent Systems

Comi, Antonello; Fotia, Lidia; Messina, Fabrizio; Pappalardo, Giuseppe; Rosaci, Domenico; Sarné, Giuseppe M. L.

doi:10.1109/cisis.2015.70

Cited by 9 publications

(6 citation statements)

References 17 publications

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“…As stated in the Section 1, the aim of agent-based modeling and simulation (ABMS) is to model the collective behaviors of interacting autonomous entities 11 . A wide range of applications can be mentioned, as supply chains, consumer markets 4 , IoT applications 12-13 , complex networks simulation 5,14-20 , mobile agents systems 21,22 , threat of bio-warfare and multi-robot simulations 6,23-26 .…”

Section: Related Workmentioning

confidence: 99%

“…Listing 1 reports a frame of the JavaScript code written for the simulation where two types of agents are defined: UAVs and Plants (lines [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35]. The agents are linked to the shared Communication Bus, and coupled with a Movement Worker (the UAVs) and a shared Collision Worker.…”

Section: Case Study: Photo-voltaic Plant Aerial Inspection By Uav Flockmentioning

confidence: 99%

“…The goal is to select the set of agents within a certain range. To this end, an array is created by computing the relative distance between the last known position of each object (lines [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]. If an object is placed within the chosen range (the array has a length greater than 0), then the Collision worker will send a message containing those information to the involved agents (lines 20-34).…”

Section: Case Study: Photo-voltaic Plant Aerial Inspection By Uav Flockmentioning

confidence: 99%

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Web-based Simulations of Multi-agent Systems

et al. 2017

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This paper describes the architecture of AgentSimJs, a Javascript-based multi-agent simulator intended to execute and visualize simulations through a Web browser. It includes the needed capabilities to render a 3D scene with objects and agents. AgentSimJs has a modular architecture, the several different components provide a set of flexible primitives to program the agent behaviour. With AgentSimJs, a user can perform a fine tuning of all the aspects related to communication, motion, and group formation. Users can also define a custom network topology and communication model.

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Section: Related Workmentioning

confidence: 99%

Section: Case Study: Photo-voltaic Plant Aerial Inspection By Uav Flockmentioning

confidence: 99%

Section: Case Study: Photo-voltaic Plant Aerial Inspection By Uav Flockmentioning

confidence: 99%

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Web-based Simulations of Multi-agent Systems

et al. 2017

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“…There are many approaches to construct a student's model. For example, the overlay model that represents the student's knowledge level in Gaudioso et al (2012); statistical, data mining and machine learning techniques to understand and improve the performance of the student's learning process (Campagni et al, 2015;Natek & Zwilling, 2014;Pena-Ayala, 2014); cognitive theories to explain human behavior (Alepis & Virvou, 2011); fuzzy logic modeling techniques and Bayesian networks to deal with the uncertainty of students' diagnosing (Jeremic et al, 2012); and ontologies to reuse students' models (Comi et al, 2015b;Clemente et al, 2011). These approaches can be used on its own or be combined, thus building a hybrid model to personalize contents according to the students' needs and available resources (Kyriacou, 2008).…”

Section: Related Workmentioning

confidence: 99%

On the use of case-based planning for e-learning personalization

Garrido

Morales

Serina

2016

Expert Systems with Applications

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In this paper we propose myPTutor, a general and effective approach which uses Al planning techniques to create fully tailored learning routes, as sequences of Learning Objects (LOs) that fit the pedagogical and students' requirements.\ud \ud myPTutor has a potential applicability to support e-learning personalization by producing, and automatically solving, a planning model from (and to) e-learning standards in a vast number of real scenarios, from small to medium large e-learning communities. Our experiments demonstrate that we can solve scenarios with large courses and a high number of students. Therefore, it is perfectly valid for schools, high schools and universities, especially if they already use Moodle, on top of which we have implemented myPTutor. It is also of practical significance for repairing unexpected discrepancies (while the students are executing their learning routes) by using a Case-Based Planning adaptation process that reduces the differences between the original and the new route, thus enhancing the learning process

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“…Not to forget that our agent model is based on semantic negotiation technique of (Morge and Routier, 2007), our protocol can be seen as an extension of Foundation for Intelligent Physical Agents FIPA-request 1 . In fact, in a semantic negotiation context we have situations similar to those of the human discussions, where human beings try to solve those situations in which the involved terms are not mutually understandable, by negotiating the semantics of these terms (Comi et al, 2015). This paper introduces a simple benchmark production system that will be used throughout this article to illustrate our contribution which is developed as robotbased application.…”

Section: Introductionmentioning

confidence: 99%

Agent-based Semantic Negotiation Protocol for Semantic Heterogeneity Solving in Multi-agent System

Noureddine

Gharbi

Ahmed

2017

Proceedings of the 12th International Conference on Evaluation of Novel Approaches to Software Engineering

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In this article, we propose an interactive agent model in an open and heterogeneous multi-agent system (MAS). Our model allows agents to autonomously communicate between each other through semantic heterogeneity. The communication problem can be expressed by the calculation based on the abilities acquired in the receiver agent, compared to the message sent by the sender agent. Hence, the semantic heterogeneity should be resolved in the message processing. The agent can autonomously enrich its own ontology by using semantic negotiation approach in several steps. We develop firstly, a model using an ontology alignment framework. Then, we enhance a similarity measure to select the most similar pairs by combining a psychological knowledge of the relevance, the resemblance, and the non-symmetry of similarity. At the end, we suggest a protocol for supporting semantic negotiation. In order to explain our approach, we implement a simple benchmark production system on JADE.

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Using Semantic Negotiation for Ontology Enrichment in e-Learning Multi-agent Systems

Cited by 9 publications

References 17 publications

Web-based Simulations of Multi-agent Systems

Web-based Simulations of Multi-agent Systems

On the use of case-based planning for e-learning personalization

Agent-based Semantic Negotiation Protocol for Semantic Heterogeneity Solving in Multi-agent System

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