In this paper we will present a design model (in the sense of KADS) for the domain of technical diagnosis. Based on this we will describe the fully implemented expert system shell MOLTKE 3.0, which integrates common knowledge acquisition methods with techniques developed in the fields of Model-Based Diagnosis and Machine Learning, especially Case-Based Reasoning.

It is useful for an intelligent software agent to be able to adapt to new demands from an environment. Such adaptation can be viewed as a redesign problem; an agent has some original functionality but the environment demands an agent with a slightly different functionality, so the agent redesigns itself. It is possible to take a case-based approach to this redesign task. Furthermore, one class ...

A framework integrating case-based reasoning (CBR) and meta-learning is proposed in this paper as the underlying methodology enabling self-improving intelligent tutoring systems (ITSs). Pedagogical strategies are stored in cases, each dictating, given a specific situation, which tutoring action to make next. Reinforcement learning is used to improve various aspects of the CBR module – cases are...

Pixed (Project Integrating eXperience in Distance Learning) is a research project attempting to use learners’ interaction logs gathered as learning episodes to provide contextual help for learners trying to navigate their way through an ontology-based Intelligent Tutoring System (ITS). First, we propose a model to describe a learning session, a way to log learners’ interaction and to decompose ...

In E-learning, 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 tools proposed, very few systems concentrate on a real time learner‟s 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 l...

The project integrates work in natural language processing, machine learning, and the semantic web, bringing together these diverse disciplines in a novel way to address a real problem. The objective is to extract and categorize machine components and subsystems and their associated failures using a novel approach that combines text analysis, unsupervised text clustering, and domain models. Thr...

The foundations of an area are concerned with the basic elements underlying the problems, methods, results and applications of the field. Sometimes this is easy to determine, e.g. when one is concerned with the foundations of logic. In CBR it is not as simple because CBR is in the intersection and the interest of different disciplines of a rather heterogeneous nature. Each discipline has its ow...

How can we best find project changes that most improve project estimates? Prior solutions to this problem required the use of standard software process models that may not be relevant to some new project. Also, those prior solutions suffered from limited verification (the only way to assess the results of those studies was to run the recommendations back through the standard process models). Co...

Deep neural networks are widely used for classification. These deep models often suffer from a lack of interpretability – they are particularly difficult to understand because of their non-linear nature. As a result, neural networks are often treated as “black box” models, and in the past, have been trained purely to optimize the accuracy of predictions. In this work, we create a novel network ...

Automatically acquiring knowledge in complex and possibly dynamic domains is an interesting, non-trivial problem. Case-based reasoning (CBR) systems are particularly well suited to the tasks of knowledge discovery and exploitation, and a rich set of methodologies and techniques exist to exploit the existing knowledge in a CBR system. However, the process of automatic knowledge discovery appears...