Abdelhamid Zouhair

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.

Our Architecture of an Adaptive Learning System Based on the Dynamic Case-Based Reasoning and the Learner Traces

The adaptation of the Computing Environment for Human Learning allows adapting the process of learning to needs, to rhythms of every learner, to styles of learning and to preferences, but they do not guarantee an individualized real time follow-up, by favoring the learning of a domain of the acquisition of the knowledge by a learner. One of the objectives of the systems based on the acquisition of the knowledge is to build computer systems allowing the sharing and the re-use of the past experiences, to ensure personalized learning in real-time, by basing on the profile and the rhythm of learning of every learner. The approach of Case-Based Reasoning seems to be a good candidate for the sharing and the re-use of the experience. In this article, we are going to present our architecture of a System of Adaptive Learning by using a Dynamic Case-Based Reasoning and the traces of interactions of the learner with the learning system as the support of reasoning.

Using the CBR Dynamic Method to Correct the Generates Learning Path in the Adaptive Learning System

The adaptive learning systems have the capacity to adapt the learning process to the needs/the rhythms of each learner, the learning styles and the preferences, but they do not ensure an individualized follow-up in real time. In this article, we will present our architecture of an Adaptive Learning System using Dynamic Case-Based Reasoning. This architecture is based on the learning styles of Felder-Silverman and the Bayesian Network to propose the learning path according to the adaptive style and on the other hand on the approach of the Dynamic Case-Based Reasoning to ensure a prediction of the dynamic situation during the learning process, when the learner has difficulty learning. This approach is based on the reuse of past similar experiences of learning (learning path) by analyzing learners’ traces.

Big incremental dynamic case-based reasoning-multi-agents system (BIDCBR-MAS) based on cloud computing

In this paper we present our approach in the fields of Case-Based Reasoning (CBR), Big Data and Cloud computing. This approach is based on the reuse of previous traces that are similar to the current situation in a dynamic way. Several approaches have been used in this area, but they suffer from some limitations related to real-time dynamic parameters (identify the different situations possible that need to be defined by the designer beforehand). Our approach based Big Data Multi-Agent System and Cloud Computing is able to study dynamic situations (recognition, prediction, and learning situations). We propose a generic approach able to learn automatically from them experiences in order to acquire the knowledge automatically. Based on the Case-Based Reasoning and multi-agent paradigm, we propose a modification of the static CBR cycle in order to introduce a dynamic process of Case-Based Reasoning based on a dynamic similarity measure able to evaluate in real time the similarity between a dynamic situation (target case) and past experiences stored in the memory (sources case) in order to predict the target case in the future.

Incremental dynamic case-based reasoning-MAS: From static to dynamic cycle

In this paper we present our approach in the field of Case-Based Reasoning (CBR). This approach is based on the reuse of previous traces that are similar to the current situation in a dynamic way. Several approaches have been used in this area, but they suffer from some limitations in automated real-time management dynamic parameters (the designer should be defined beforehand the different situations possible). We propose a multi-agent multi-layer architecture based on Incremental Dynamic Case-Based Reasoning (IDCBR) able to study dynamic situations (recognition, prediction, and learning situations). We propose a generic approach able to learn automatically from their experiences in order to acquire the knowledge automatically. Based on the Case-Based Reasoning and multi-agent paradigm, we propose a modification of the static CBR cycle in order to introduce a dynamic process of Case-Based Reasoning based on a dynamic similarity measure able to evaluate in real time the similarity between a dynamic situation (target case) and past experiences stored in the memory (sources case) in order to predict the target case in the future.

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.