Patrick Person

Data representation layer in a MultiAgent decision support system

This article presents a system designed to help deciders manage cases of crisis. The system represents, characterises and interprets the dynamic evolution of a given situation and displays the results of its analysis. The core of the system is made of three multiagent systems (MAS): the data representation MAS for the static and dynamic representation of the current situation, the characterisation MAS for dynamically regrouping sets of agents of the data representation MAS and the interpretation MAS for matching results between the characterisation MAS and previous scenarios stored in the persistent memory of the system in order to have a deeper analysis of the situation. The case based reasoning of the interpretation MAS sends its results to the user as a view of the current situation linked to some views of similar situations. This article will focus on the data representation MAS.

Intelligent tutoring systems founded on the multi-agent incremental dynamic case based reasoning

In E-learning, there is still the problem of knowing how to ensure an individualized and continuous learners follow-up during learning process, indeed among the numerous tools proposed, very few systems concentrate on a real time learners follow-up. Our work in this field develops the design and implementation of a Multi-Agent Systems Based on Dynamic Case Based Reasoning which can initiate learning and provide an individualized follow-up of learner. When interacting with the platform, every learner leaves his/her traces in the machine. These traces are stored in a basis under the form of scenarios which enrich collective past experience. The system monitors, compares and analyses these traces to keep a constant intelligent watch and therefore detect difficulties hindering progress and/or avoid possible dropping out. The system can support any learning subject. The success of a case-based reasoning system depends critically on the performance of the retrieval step used and, more specifically, on similarity measure used to retrieve scenarios that are similar to the course of the learner (traces in progress). We propose a complementary similarity measure, named Inverse Longest Common Sub-Sequence (ILCSS). To help and guide the learner, the system is equipped with combined virtual and human tutors.

MultiAgent case-based reasoning and individualized follow-up of learner in remote learning

In distance learning/training in a Computing Environment for Human Learning (CEHL), among the numerous methods proposed, very few concentrate on a real time follow-up of learner/trainee. Our work develops the design and implementation of a MultiAgent System based on case based reasoning which can initiate learning and provide an individualized monitoring of learner/trainee. When interacting with the platform, every learner/trainee leaves his/her traces in the machine. They are stored in a basis under the form of scenarios thus enriching collective past experience. The system monitors, compares and analyses these traces to keep a constant intelligent watch and therefore detect difficulties hindering progress and/or avoid possible dropping out. To help and guide the learner the system is equipped with combined virtual and human tutors.

Modelisation and implementation of our system incremental dynamic case based reasoning founded In the MAS under JADE plate-form

The aim of this paper is to present our approach in the field of Intelligent Tutoring System (ITS), in fact there is still the problem of knowing how to ensure an individualized and continuous learners follow-up during learning process, indeed among the numerous methods proposed, very few systems concentrate on a real time learners follow-up. Our contribution in these areas is to design and develop an adaptive Multi-Agent Systems Based on Dynamic Case Based Reasoning which can initiate learning and provide an individualized follow-up of learner. This approach involves 1) the use of Dynamic Case Based Reasoning to retrieve the past experiences that are similar to the learners traces, and 2) the use of Multi-Agents System. Our Work focuses on the use of the learner traces. When interacting with the platform, every learner leaves his/her traces in the machine. The traces are stored in database, this operation enriches collective past experiences. The traces left by the learner during the learning session evolve dynamically over time; the case-based reasoning must take into account this evolution in an incremental way. In other words, we do not consider each evolution of the traces as a new target, so the use of classical cycle Case Based reasoning in this case is insufficient and inadequate. In order to solve this problem, we propose a dynamic retrieving method based on a complementary similarity measure, named Inverse Longest Common Sub-Sequence (ILCSS). Through monitoring, comparing and analyzing these traces, the system keeps a constant intelligent watch on the platform, and therefore it detects the difficulties hindering progress, and it avoids possible dropping out. The system can support any learning subject.

Our System IDCBR-MAS: from the Modelisation by AUML to the Implementation under JADE Platform

This paper presents our work in the field of Intelligent Tutoring System (ITS), in fact there is still the problem of knowing how to ensure an individualized and continuous learners follow-up during learning process, indeed among the numerous methods proposed, very few systems concentrate on a real time learners follow-up. Our work in this field develops the design and implementation of a Multi-Agents System Based on Dynamic Case Based Reasoning which can initiate learning and provide an individualized follow-up of learner. This approach involves 1) the use of Dynamic Case Based Reasoning to retrieve the past experiences that are similar to the learners traces (traces in progress), and 2) the use of Multi-Agents System. Our Work focuses on the use of the learner traces. When interacting with the platform, every learner leaves his/her traces on the machine. The traces are stored in database, this operation enriches collective past experiences. The traces left by the learner during the learning session evolve dynamically over time; the case-based reasoning must take into account this evolution in an incremental way. In other words, we do not consider each evolution of the traces as a new target, so the use of classical cycle Case Based reasoning in this case is insufficient and inadequate. In order to solve this problem, we propose a dynamic retrieving method based on a complementary similarity measure, named Inverse Longest Common Sub-Sequence (ILCSS). Through monitoring, comparing and analyzing these traces, the system keeps a constant intelligent watch on the platform, and therefore it detects the difficulties hindering progress, and it avoids possible dropping out. The system can support any learning subject. To help and guide the learner, the system is equipped with combined virtual and human tutors.

Dynamic Representation and Interpretation in a Multiagent 3D Tutoring System

In this paper we present an intelligent tutoring system which aims at decreasing students’ dropout rate by offering the possibility of a personalized follow-up. We address the specific problem of the evolution of the large amount of data to be processed and interpreted in an intelligent tutoring system. In this regard we detail the architecture and experimental results of our decision support system used as the core of the intelligent tutor—which could be applied to a variety of teaching fields. The first part presents an overview of the characteristics of intelligent tutors, the chosen data organization—composed of a composite factual semantic feature descriptive representation associated to a multiagent system—and two examples used to illustrate the architecture of our prototype. The second and last part describes all the components of the prototype: student interface, dynamic representation layer, characterization, and interpretation layers. First, for the student interface, the system shows our 3D virtual campus named GE3D to be connected to the intelligent tutor. Then we explain how the agents of the first layer represent the evolution of the situation being analyzed. Next, we specify the use of the characterization layer to cluster the agents of representation layer and to compute compound parameters. Finally, we expose how—using compound parameters—the third layer can measure similarity between current target case and past cases to constitute an interpretation of cases according to a case-based reasoning paradigm.

Dynamic Case-Based Reasoning Based on the Multi-Agent Systems: Individualized Follow-Up of Learners in Distance Learning

In a Computing Environment for Human Learning (CEHL), there is still the problem of knowing how to ensure an individualized and continuous learner’s follow-up during learning process, indeed among the numerous methods proposed, very few systems concentrate on a real time learner’s followup. Our work in this field develops the design and implementation of a Multi-Agent Systems Based on Dynamic Case Based Reasoning which can initiate learning and provide an individualized follow-up of learner. When interacting with the platform, every learner leaves his/her traces in the machine. These traces are stored in a basis under the form of scenarios which enrich collective past experience. The system monitors, compares and analyses these traces to keep a constant intelligent watch and therefore detect difficulties hindering progress and avoid possible dropping out. The system can support any learning subject. To help and guide the learner, the system is equipped with combined virtual and human tutors.