Alain Mille

A Trace-Based System for Technology-Enhanced Learning Systems Personalisation

This paper deals with the subject of personalisation in Technology-Enhanced Learning (TEL) Systems. As we want user centred solutions, we claim that considerable advantage can be gained from allowing observation and interpretation of learners’ interactions traces in TEL systems. The key concepts of our approach are modelled traces, and trace transformations. These transformations such as abstraction or filtering enable a better understanding of the interactions taking place in the TEL systems and allow to provide services for personalisation.

From case-based reasoning to traces-based reasoning

Abstract CBR is an original paradigm to adapt solutions of past problems in order to solve new similar problems. A case is a problem with its solution and cases are stored in a case library. The reasoning process obeys to a cycle allowing to “learn” from new solved cases. This approach is also viewed as a lazy learning method when applied for classification. This AI technology is applied for various tasks as designing, planning, diagnosing, information retrieving, etc. The talk will be the occasion to go a step further in reusing past experience, by considering traces of computer use as experience knowledge containers for contextual and situation based problem solving.

Trace-Based framework for experience management and engineering

The paper deals with experience management in computer-mediated environments. It particularly focuses on complex tasks whose support relies more on experience than on knowledge. The presented approach is based on the “use traces” concept to investigate the “activity reflexivity” as a first step in experience management, and experience sharing and reusing as possible applications. The paper also outlines the framework supporting Trace-Based Systems creation. Traces, trace models and trace life cycle are formally defined. The main parts of the framework architecture: collecting, transformation, visualization, and query systems are also detailed.

A collaboration-centred approach to manage engineering knowledge: a case study of an engineering SME

In small and medium-sized enterprises (SMEs), knowledge management systems cannot be considered in the same way as in large companies. In this paper, it is argued that knowledge management can be achieved within SMEs by the creation of a knowledge-sharing culture. In this context, the aim is not to formalise specific technical job know-how, but rather to formalise simple contextual knowledge descriptors associated with the technical objects handled by actors. A prototype software tool, called KALIS, has been developed to foster collaboration within teams of professionals. The framework of the tool is built to support information and knowledge-sharing between engineers. The tool is based on knowledge repository principles and on three kinds of workspaces associated with a showroom concept to improve awareness and information retrieval. The paper describes the integration of this approach in a mechanical engineering office following a three-month deployment period. It also details observations about collaborative behaviour and the impact of the approach on knowledge construction and sharing.

Operator Decision Aiding by Adaption of Supervision Strategies

This paper presents a CBR application in the domain of industrial supervision. The domain knowledge is acquired at design stage through different models and some critical prototypical situations. At operating stage, new situations and their associated supervision strategy complete the supervision system and are reused by adaptation in later situations in similar contexts. The system can be viewed as an artificial operator who collects experiences from the operators in order to propose relevant variants in similar situations. First, we present current approaches in process supervision. Then, knowledge and cases representation that support case-based reasoning and the different stages of the reasoning process are presented. We focus on case adaptation, and show different degrees of case reuse, depending on available knowledge.

Supporting activity modelling from activity traces

We present a new method and tool for activity modelling through qualitative sequential data analysis. In particular, we address the question of constructing a symbolic abstract representation of an activity from an activity trace. We use knowledge engineering techniques to help the analyst build an ontology of the activity, that is, a set of symbols and hierarchical semantics that supports the construction of activity models. The ontology construction is pragmatic, evolutionist and driven by the analyst in accordance with their modelling goals and their research questions. Our tool helps the analyst define transformation rules to process the raw trace into abstract traces based on the ontology. The analyst visualizes the abstract traces and iteratively tests the ontology, the transformation rules and the visualization format to confirm the models of activity. With this tool and this method, we found innovative ways to represent a car-driving activity at different levels of abstraction from activity traces collected from an instrumented vehicle. As examples, we report two new strategies of lane changing on motorways that we have found and modelled with this approach.

Engineering and learning of adaptation knowledge in case-based reasoning

Case-based reasoning (CBR) uses various knowledge containers for problem solving: cases, domain, similarity, and adaptation knowledge. These various knowledge containers are characterised from the engineering and learning points of view. We focus on adaptation and similarity knowledge containers that are of first importance, difficult to acquire and to model at the design stage. These difficulties motivate the use of a learning process for refining these knowledge containers. We argue that in an adaptation guided retrieval approach, similarity and adaptation knowledge containers must be mixed. We rely on a formalisation of adaptation for highlighting several knowledge units to be learnt, i.e. dependencies and influences between problem and solution descriptors. Finally, we propose a learning scenario called “active approach” where the user plays a central role for achieving the learning steps.

A Knowledge-Level Task Model of Adaption in Case-Based Reasoning

The adaptation step is central in case-based reasoning (CBR), because it conditions the obtaining of a solution to a problem. This step is difficult from the knowledge acquisition and engineering points of view. We propose a knowledge level analysis of the adaptation step in CBR using the reasoning task concept. Our proposal is based on the study of several CBR systems for complex applications which imply the adaptation task. Three of them are presented to illustrate our analysis. We sketch from this study a generic model of the adaptation process using the task concept. This model is in conformity with other CBR formal models.

Helping children with cognitive disabilities through serious games: project CLES

Our work addresses the development of a Serious Game for the diagnostic and learning of persons with cognitive disabilities. In reality, many studies have shown that young people, especially children, are attracted towards computer games. Often, they play these games with great interest and attention. Thus, the idea of using serious games to provide education is attractive for most of them. This work is situated in the context of Project CLES. This project, in collaboration with many research laboratories, aims at developing an Adaptive Serious Game to treat a variety of cognitive handicaps. In this context, this article presents a system that generates learning scenarios keeping into account the users profile and their learning objectives. The users profile is used to represent the cognitive abilities and the domain competences of the user. The system also records the users activities during his/her interaction with the Serious Game and represents them in interaction traces. These traces are used as knowledge sources in the generation of learning scenarios.

A complete chronicle discovery approach: application to activity analysis

Discovering temporal patterns hidden in a sequence of events has applications in numerous areas like network failure analysis, customer behaviour analysis, web navigation pattern discovery, etc. In this article, we present an approach to the discovery of chronicles hidden in the interaction traces of a human activity with the intention of characterizing some interesting tasks. Chronicles are a special type of temporal patterns, where temporal orders of events are quantified with numerical bounds. The algorithm we present is the first existing chronicle discovery algorithm that is complete. It is a chronicle discovery framework that can be configured to behave exactly as non-complete algorithms existing in litterature with no reduction of performance, but it can also be extended to other useful chronicle discovery problems like hybrid episode discovery. We show that the complete chronicle discovery problem has a very high complexity but we argue and illustrate that this high complexity is acceptable when the knowledge discovery process in which our algorithm takes part is real time and interactive. The platform Scheme Emerger, also presented in this paper, has been developed in order to implement the algorithm and to support graphically the real time and interactive chronicle discovery process.