Bioinformatics: a promising field for case-based reasoning

Case Based Reasoning has been applied in different fields such as medicine, industry, tutoring systems and others, but in the CBR there are many areas to explore. Nowadays, some research works in Bioinformatics are attempting to use CBR like a tool for classifying DNA genes. Specially the microarrays have been applied increasingly to improve medical decision-making, and to the diagnosis of different diseases like cancer. This research work analyzes the Microarrays structure, and the initial concepts to understand how DNA structure is studied in the Bioinformatics' field. In last years the CBR has been related to Bioinformatics and Microarrays. In this report, our interest is to find out how the Microarrays technique could help in the CBR field, and specially in the Case-Based Maintenance policies.Postprint (published version

Domus tutor: A CBR tutoring agent for student support

The changes introduced by the Bologna process in the educational paradigm, moving from a lecturer centered paradigm to a learner centered paradigm, involves a more supported learning process based on learning outcomes and the adoption of new pedagogical methodologies. In this paper we present our strategy of integration of tutoring agents in learning environments, using the features of intelligent tutoring systems adapted to collaborative environments. The Domus Tutor agent is the face of the adaptive learning environment that integrates Learning Design, groupware and collaborative work technologies. The adaptation of the system to the learner profile is based on case-based reasoning methodology; witch is one of the major reasoning paradigms in artificial intelligence.- (undefined

Intelligent Tutoring System: Expert-Knowledge Module Menggunakan Case-Based Reasoning

Modul sistem pakar telah dikembangkan untuk intelligent tutoring system berbasis case-based reasoning (CBR) dalam ranah pemrograman komputer. Sistem ini bertujuan untuk membantu pelajar dalam mempelajari bahasa pemrograman terutama praktik pemrograman karena sistem yang dibuat dilengkapi modul untuk mencari solusi bagi pesan-pesan kesalahan yang muncul. Data kasus untuk CBR diambil dari kasus-kasus kesalahan yang terjadi pada saat pemrograman. Pengujian menunjukan bahwa tingkat akurasi sistem tergantung dari banyaknya kasus-kasus yang tersimpan dalam basis data kasus. Semakin banyak kasus yang tersimpan, maka tingkat akurasi sistem akan semakin meningkat

EINO The Answer

This study investigated the various methods involved in creating an intelligent tutor for the University of Central Florida Web Applets (UCF Web Applets), an online environment where student can perform and/or practice experiments.  After conducting research into various methods, two major models emerged.  These models include: 1) solving the problem for the student 2) helping the student when they become stymied and unable to solve the problem.  A storyboard was created to show the interactions between the student and system along with a list of features that were desired to be included in the tutoring system.  From the storyboard and list of features, an architecture was created to handle all of the interactions and features.  After the initial architecture was designed, the development of the actual system was started.  The architecture underwent a several iterations to conclude with a working system, EINO.   EINO is an intelligent tutoring system integrated into the UCF Web Applets.  The final architecture of EINO incorporated a case-based reasoning system to perform pattern recognition on the student’s input into the UCF Web Applets.  The interface that the student interacts with was created using Flash™.  EINO was implemented in three of the experiments from the UCF Web Applets.  A series of tests were performed on the EINO tutoring system to determine that the system could actually perform each and every one of the features listed initially.  The final test was a simulation of how the EINO would perform in “real life.”  Test subjects with the same educational level as the target group were chosen to spend an unlimited time using each of the three experiments.  A single experiment is designed to reinforce a topic currently being covered by the book.  Each of the test subjects filled out a survey on every lab to determine if the EINO system produced a helpful output

Collaborative CBR-based agents in the preparation of varied training lessons

International audienceCase‐Based Reasoning (CBR) is widely used as a means of intelligent tutoring and elearning systems. Indeed, course lessons are elaborated by analogy: this kind of system produces sets of exercises with respect to student level and class objective. Nevertheless, CBR systems always result in the same solution to a given problem description, whereas teaching requires that monotony be broken in order to maintain student motivation and attention. This is particularly true for sports where trainers must propose different exercises to practice the same skills for many weeks. We designed a system based on CBR that takes into account any previous lessons offered and designs new ones so as to vary the exercises each time: this system takes into account the solutions previously proposed so as to avoid giving the same lesson twice. In addition, this system is based on collaborative agents, each taking into account the exercises proposed by others so that each activity is proposed only once during a lesson. A sports trainer tested and evaluated the ability of this system as a means to design varied aïkido training lessons and proved that our system is capable of creating classroom activities that are diverse, changing, pertinent and consistent

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 learner’s 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