Jean-Paul A. Barthès

An advanced collaborative environment for software development

Collaborative software development is a complex activity. An important factor that needs to receive attention in collaborative software development is software quality. High quality software reduces the development and the maintenance; improves delivery schedules; and reduces repairs and rework. In order to measure, evaluate, control and improve the software quality, software metrics can be used. In this research we present an advanced collaborative environment for software development currently being built, called ACE4SD, which intends to support the improvement of the code quality during collaborative software development. ACE4SD is a system of systems composed of a software development environment, a multi-agent system and a platform to capitalize and manage knowledge, all of them being integrated in the same environment. ACE4SD can provide personalized support to team members to improve the code quality and encourage its reuse, it can answer questions or doubts arisen during the development, record document problems and solutions, and improve the awareness and collaboration between the participants.

Facilitating Collaboration and Information Retrieval: Collaborative Traces Based SWOT Analysis and Implications

In a web-based Collaborative Working Environment (CWE), people’s activities are principally concentrated in the group shared work space. Any collaborative interactions could leave a series of collaborative traces. Based on our proposed concept Collaborative Trace and the corresponding Model, this chapter addresses the issue related to exploit and reuse the collaborative traces in consideration of supporting the group collaboration work in different aspects. In this process, obviously, some complex filters are required to extract more potential information both from the trace set and the data base. They can be defined by the rules according to the practical collaboration object and the application formalism. Furthermore, a general traces exploitation framework is introduced and implemented in CWE with a comprehensive case: the traces based SWOT Analysis.

CONSIGNELA: A multidisciplinary patient-centered project to improve drug prescription comprehension and execution in elderly people and parkinsonian patients

Abstract Many older patients and patients with Parkinson’s disease (PD) do not respect medication prescriptions. Non-adherence is caused by several factors among which three play an important role: Treatment complexity, cognitive decline, and patient-provider communication. This article presents an ongoing multidisciplinary project, the CONSIGNELA project, which adopts a patient-centered approach for improving medication adherence in these patients. The first objective of the project is to identify the best way to communicate medication prescriptions with tablets and touch-screen devices. The second objective is to improve medication adherence by creating a collaborative application (app) designed both for older and PD patients and for healthcare professionals in charge of them. Two solutions are combined: A cognitive and a technological solutions. The cognitive solution consists in studying in real time older and PD patients while consulting and executing prescriptions presented in different formats to select the most efficient one. The technological solution aims to improve collaboration between health professionals and patients by developing System of Systems (SoS). The research model used in the project includes three phases. Only the results of phase 1 are presented in the article. A research app, CONSIGNELA-Appli-R was programmed to analyze patient cognitive processes while consulting a prescription on a tablet. A pilot study was carried out with young adults to test it. It confirmed a facilitating effect of table format. In parallel, the prototype of a second app, CONSIGNELA-Appli-P, for patients and healthcare providers was developed. It is based on an SoS architecture connecting a virtual pillbox, a Multi-Agent System (MAS) and a knowledge platform. The virtual pillbox is used by patients and providers to improve mutual understanding of medication prescriptions. The MAS provides agents that extract and analyze indicators, actions and information resulting from the interaction between patients and virtual pillboxes. All the information is capitalized and stored in the knowledge platform. Providers can receive charts and dashboards showing information about the following of the medication regimen, for instance, if patients are following it correctly or not.

Recommending Competent Users from Semantic Traces Using a Bayes Classifier

These traces in return offer a clue whether a user is competent enough on a subject. This helps further collaboration because knowing the specialization of users helps to distribute tasks reasonably. In this article, we propose a semantic model of traces and analyze classified traces using a Bayes classifier. We exploit the results to offer recommendation on competent users accordingly.

ONTOCODESIGN: An exercise in using agents to cooperatively develop a CSCWD ontology

This paper presents the ONTOCODESIGN platform for collaboratively designing an ontology for CSCWD. The ontology is meant to help SMC/CSCWD Committee members to better interact and organize their scientific production, and to help the Steering Committee make more informed decisions. Indeed, organized research groups usually have steering committees supporting their actions, and making decisions implementing the groups strategy. A big picture that reveals a network of topics, exposing how the community reacts to changes (trends, discoveries, social impacts, etc) is critical for making good decisions. We believe that a collaborative approach for building a CSCWD ontology will help keeping track of CSCWD themes and allow developing more consensual and effective initiatives. The ONTOCODESIGN platform uses a multi-agent environment and a multilingual context The paper describes its architecture and its first implementation.

Trace-based computer supported cooperative work as support for learners group design

Group work, in certain circumstances, could encourage peer learning and provide the learners an opportunity to clarify and refine their knowledge. However, randomly grouping learners of different level of knowledge and activeness could decrease the effectiveness of a group. In this paper, we present a criterion for recommending the formation of groups of learners. This criterion is based on traces collected from interactive digital platforms. Traces are then processed with Bayes Classifier. We implemented this prototype using the MEMORAe approach.

A system of systems architecture for supporting decision-making

Good Decision Support Systems require three main features: (i) a good handling of the domain data and information; (ii) an efficient user interface; and (iii) a good knowledge of past decisions. Usually such features are handled by different specialized systems difficult to integrate. In this research we keep specialized systems independent, focusing on interoperability. We propose a system of systems architecture (SoS) integrating a domain system in which users interact, a multi-agent system implementing an efficient user interface and taking into account results from the domain system, and a platform to capitalize and manage knowledge. Our approach extracts indicators from the interaction or behavior of users within the domain system, and provides them analyses, statistics and recommendations to help them reach good decisions. We built a prototype and applied it to two different domains: collaborative software development and healthcare. In this paper, we will focus on the multi-agent system which is a key component of the SoS architecture.

Activité collective et personnages autonomes : moteur décisionnel reposant sur des relations de confiance

When working in teams, people make mistakes. To train someone in a collaborative virtual environment to adapt to teammates that bahave non optimally, we propose (1) an augmentation of the ACTIVITY-Description Language as well as mechanisms of propagation of constraints that will facilitate agents’ reasoning; and (2) an agent model in which each agent is described through three dimensions (integrity, benevolence, abilities) corresponding to the MDS trust model. Besides each agent has different personal and collective goals and has beliefs about others’ integrity, benevolence and abilities. This agent model is associated to a decision-making system that allows agents to adopt human-like behaviors. In particular, agents take others into account and are able to reason on their beliefs about others both when choosing which goal (collective or individual) to focus on and when selecting a task. We conducted a preliminary evaluation in which participants evaluated the behaviors produced with our system. MOTS-CLÉS : systèmes multi-agents, prise de décision, confiance, activité collective.

Recommending competent person in a digial ecosystem

Users leave traces when they participate in activities in a collaborative digital environment. These traces in return offer a clue for recommending a user by competency on a subject. This mechanism helps further collaboration because knowing the specialization of users ameliorates distributing tasks reasonably. In this article we propose a semantic model containing activity, trace of interaction and competency. Applying Logistic Regression, we exploit this model to offer recommendation of a competent person in a digial ecosystem.

Trust-based decision-making system for action selection by autonomous agents

Virtual agents can play the trainees teammates in Collaborative Virtual Environments for Training (CVET). In such teams, trust is essential since people rely on each others. We propose a decision making system for agents in CVET based on an agent model inspired from works on trust in organizational psychology. Agents dimensions are integrity, benevolence and abilities, and agents have trust-beliefs about their teammates integrity, benevolence and abilities. Based on their personal dimensions and trust beliefs, agents compute the importance of their goals and then the utility of actions to select the most appropriate action.