Case-Based Reasoning and Learning Systems

2016 ◽  
Vol 11 (02) ◽  
Author(s):  
Vandana Jindal ◽  
Vandana Jindal

Case based reasoning (CBR) technology presents a foundation for a new technology of building intelligent systems for teaching, learning and training. This Technology directly addresses the problems found in the traditional artificial intelligence (AI) techniques, e.g. the problems of knowledge acquisition, remembering, robust and maintenance. This paper discusses the CBR methodology, the research issues and technical aspects of implementing effective intelligent e-learning systems. Some examples of successful applications in different domain are also given in the paper.

2017 ◽  
Vol 12 (01) ◽  
Author(s):  
Vandana Jindal ◽  
Pankaj Kumar Jain

Case based reasoning (CBR) technology presents a foundation for a new technology of building intelligent systems for teaching, learning and training. This Technology directly addresses the problems found in the traditional artificial intelligence (AI) techniques, e.g. the problems of knowledge acquisition, remembering, robust and maintenance. This paper discusses the CBR methodology, the research issues and technical aspects of implementing effective intelligent e-learning systems. Some examples of successful applications in different domain are also given in the paper.


Author(s):  
Natalia Martínez Sánchez ◽  
María M. García Lorenzo ◽  
Zoila Zenaida García Valdivia ◽  
Gheisa Ferreira Lorenzo

Los Sistemas de Enseñanza-Aprendizaje Inteligentes son programas que portan conocimientos de cierto contenido mediante un proceso interactivo individualizado.En este trabajo se expone un modelo que integra el paradigma del Razonamiento Basado en Casos y los Sistemas de Enseñanza-Aprendizaje Inteligentes que favorece la concepción de estos sistemas a usuarios no expertos en informática, teniendo en cuenta las facilidades y naturalidad del enfoque basado en casos. AbstractThe Intelligent Teaching-Learning Systems are programs which carry knowledge about certain subject through an individualized interactive process.In the present work, a model which integrates the case-based reasoning paradigm and the Intelligent Teaching-Learning Systems is proposed, the model favors the design of these systems by users no necessarily experts in the informatics field, taking into account the facilities and naturalness of the case-based approach.


Author(s):  
Brahim Faqihi ◽  
Najima Daoudi ◽  
Rachida Ajhoun

In the field of learning, we are witnessing more and more the introduction of new environments in order to better meet the specific needs of the main actors of the process. The shift from face-to-face learning to distance learning or e-learning has overcome some of the challenges of availability, location, prerequisites, but has been rapidly impacted by the development of mobile technology. As a result, m-learning appeared and quickly evolved into p-learning. The arrival of the "Open Software" concept has given birth to several "open-something" initiatives, among which are the Open Educational Resource (OER) and the Massive Online Open Course (MOOC). These learning resources have also made progress, although they are fairly recent. Admittedly, this diversity of environments offers a wealth and a multitude of pedagogical resources. However, the question of the capitalization of contents, knowledge and know-how of each of these environments is necessary. How can the exchange and reuse of pedagogical resources be guaranteed between these different learning environ-ments? otherwise-said how to guarantee the interoperability of these resources? In order to contribute to the creation of an pedagogical heritage, we propose to design a case-based system allowing the author, when creating a course in a particular context and environment, to exploit the resources that are already available. The goal is to put in place an intelligent production system based on case-based reasoning. It is based on four phases ranging from indexing to reuse, through the similarity measurement and the evaluation. In the first part, we will detail the evolution of learning environments. In the second part, we will review the existing course production platforms, their prin-ciples and their challenges. In the third part, we will present case-based reasoning systems, and then we will introduce our target system.


The focus of algorithmic design is to solve composite problems. Intelligent systems use intellectual concepts like evolutionary computation, artificial neural networks, fuzzy systems, and swarm intelligence to process natural intelligence models. Artificial intelligence is used as a part of intelligent systems to perform logic- and case-based reasoning. Systems like mechanical and electrical support systems are operated by utilizing Supervisory Control and Data Acquisition (SCADA) systems. These systems cannot accomplish their purpose, provided the control system deals with the reliability of it. In CPSs, dimensions of physical processes are taken by sensors and are processed in cyber subsystems to drive the actuators that affect the physical processors. CPSs are closed-loop systems. The adaptation and the prediction are the properties to be followed by the control strategies that are implemented in cyber subsystems. This chapter explores cyber physical control systems.


Author(s):  
Stephen R. Chastain ◽  
Jason Caudill

Podcasting has quickly emerged as a leading technology in the new field of mobile learning. Tracing this new technology’s history over the past two years reveals just how broadly the use of digital audio files may become in the fields of education and training. The ease of use, low cost of creation and hosting, and most importantly pervasiveness of user access to compatible hardware combine to make podcasting a major force in both traditional and distance education. This chapter explores the history, technology, and application of podcasting as an instructional tool.


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