Gestion de l'incertitude dans les systèmes de raisonnement à base de cas en utilisant la théorie des fonctions de croyance : Maintenance et évaluation

Les travaux de recherche, développés dans cette thèse, se placent dans le contexte du raisonnement à base decas (CBR), et plus précisément sur la maintenance et sur l'évaluation des performances de ce système tout en gérantl'incertitude. En effet, les connaissances dans ces systèmes se réfèrent à des situations réelles, qui ne sont jamaisexactes. Nous utilisons donc des outils et des techniques proposés dans le cadre de la théorie des fonctions de croyancepour gérer des connaissances partielles et peu fiables.Ce type de connaissances, ainsi que le bruit et la redondance, peuvent entraîner une diminution de la compétence ainsique la performance des systèmes CBR. En fait, l’estimation de la compétence de ces systèmes n’est pas évidente puisqu'elleest influencée par divers facteurs comme la taille de la base de cas (BC) et leur densité. Par conséquent, nous proposons,dans un premier temps, un nouveau modèle de compétence qui prend en compte ces facteurs pour l'évaluationde la compétence.Par ailleurs, pour conserver un système CBR de haute qualité, nous devons principalement maintenir ses conteneurs deconnaissances tels que la BC et le vocabulaire qui peuvent être limités à l'ensemble des attributs décrivant les cas. Nousproposons, alors, dans cette thèse, deux stratégies de maintenance dans le cadre de la théorie des fonctions decroyance, où la première vise à maintenir les BCs et la seconde concerne le maintien du vocabulaire. En outre, pour nepas être affecté par les données bruitées de chaque type de connaissance, la BC et le vocabulaire sont simultanémentmaintenus, dans la dernière partie de cette thèse, en utilisant une nouvelle méthode intégrée de maintenance.Nous mentionnons, enfin, que toutes les méthodes que nous proposons pour l’évaluation ou la maintenance peuventêtre appliquées lorsque des connaissances préalables sont disponibles auprès des experts. Par conséquent, nous construisonspour chaque méthode proposée une version contrainte qui exploite ces connaissances préalables, sous formede contraintes par paire, pour aider le processus automatique d'apprentissage.Our research, in this thesis, are carried on the context of Case-Based Reasoning (CBR), more precisely, on itsmaintenance and evaluation with uncertainty management. Knowledge within such systems refer to real-world situations,which are never exact. Hence, we use tools and techniques offered in a theoretical framework based on the belieffunction theory to manage partial and unreliable knowledge.This type of knowledge along with noisiness and redundancy may cause a decreasing on CBR systems’ competence andperformance. Actually, the competence of such systems is hardly estimated since it is influenced by various factors likethe size of the Case Base (CB) and cases density that refers to problem space coverage. Therefore, we propose, a newcompetence model that takes into account such influencing factors for competence assessment.To conserve a high-quality CBR system, we need mainly to maintain its knowledge containers such as the CB and theVocabulary that may be restricted to the set of attributes describing cases. Hence, we propose, in this thesis, twomaintaining strategies under the belief function theory, where the first one consists in Case Base Maintenance (CBM)and the second one is about Vocabulary Maintenance. Besides, to be not affected by each others’ noisiness, CB andvocabulary are simultaneously maintained, in the last part of this thesis, using a new evidential and integrated maintenancemethod.We mention, finally, that all our proposed methods, for even evaluation or maintenance, may be applied where priorknowledge are available from the experts. Hence, we build for every proposed method a constrained version that exploitssuch background knowledge, in form of pairwise constraints, to aid the automatic learning process

Un protocole de communication pour enchères temps réel

Dans cet article, nous proposons une architecture de communication distribuée offrant les services de communication adéquats aux applications d'enchère temps réel. Nous proposons aussi un protocole de communication supportant les interactions entre le site d'enchères et les participants à une enchère. Ce protocole a été implémenté sur IRC afin d'en exploiter les fonctionnalité adéquates en l'occurrence des communication de groupe synchrone et en mode Push. Une évaluation de performances par simulation met en évidence sa scalabilité et sa fiabilité. La contribution de ce travail réside dans sa généricité vis-à-vis des types d'enchères et sa flexibilité par rapport au protocole de communication sous jacent et des technologies déployées au niveau des applications

CIMMEP: constrained integrated method for CBR maintenance based on evidential policies

International audienceThe quality of the proposed solutions by Case-Based Reasoning (CBR) systems is highly dependent on recorded experiences and their describing attributes. Hence, to keep them offering accurate and efficient responses for a long time frame, the maintenance of Case Bases (CB) and Vocabulary knowledge is required. However, maintenance operations are usually unable to exploit provided domain-experts knowledge although this kind of systems are widely applied in several real-life contexts. This offered prior knowledge is handled, in our work, in form of pairwise constraints: Regarding cases, Must-Link (ML) affirms that two given problems should have the same solution, and Cannot-Link (CL) informs that two problems cannot have the same solution. These constraints may also regard vocabulary knowledge in such a way that ML is generated when prior knowledge affirm that two given features offer correlated values, therefore, similar information, and CL is built when they provide different information. This paper proposes a new constrained & integrated method, named CIMMEP, encoding Constrained & Integrated Maintaining Method based on Evidential Policies, for maintaining both vocabulary and CB through eliminating redundancy and noisiness. Since CBR systems handle real-world experiences, which are full of uncertainty, CIMMEP manages this imperfection using a powerful tool called the belief function theory

Managing uncertainty during CBR systems vocabulary maintenance using Relational Evidential C-Means

International audienceDue to the incremental learning of Case-Based Reasoning (CBR) systems, there is a colossal need to maintain their knowledge containers which are (1) the case base, (2) similarity measures, (3) adaptation, and (4) vocabulary knowledge. Actually, the vocabulary presents the basis of all the other knowledge containers since it is used for their description. Besides, CBR systems store real-world experiences which are full of uncertainty and imprecision. Therefore, we propose, in this paper, a new policy to maintain vocabulary knowledge using one of the most powerful tools for uncertainty management called the belief function theory, as well as the machine learning technique called Relational Evidential C-Means (RECM). We restrict the vocabulary knowledge to be the set of features describing cases, and we aim to eliminate noisy and redundant attributes by taking into account the correlation between them

An evidential integrated method for maintaining case base and vocabulary containers within CBR systems

International audienceCases and vocabulary maintenance presents a crucial task to preserve high competent Case-Based Reasoning (CBR) systems, since the accuracy of their offered solutions are strongly dependent on stored cases and their describing attributes quality. The maintenance aims generally at eliminating two types of undesirable knowledge which are noisy and redundant data. However, inexpedient Case Base Maintenance (CBM) or vocabulary maintenance may not only greatly decrease CBR competence in solving new problems, but also reduce its performance in term of retrieval time. Besides, to provide a high maintenance quality, it is necessary to manage uncertainty within knowledge since "real-world data are never perfect" and stored cases within a CBR system's Case Base (CB) describe realworld experiences. Hence, we propose, in this paper, a new integrated method that maintains both of the CB and the vocabulary knowledge containers of CBR systems by offering a new alternating technique to properly detect noisiness and redundancy whether in cases or features. During the learning steps of our new integrated maintenance policy, which drives the decision making about cases and attributes selection, we manage uncertainty using one among the most powerful tools called the Belief Function Theory

CEC-Model: A new competence model for CBR systems based on the belief function theory

International audienceThe high influence of case bases quality on Case-Based Reasoning success gives birth to an important study on cases competence for problems resolution. The competence of a case base (CB), which presents the range of problems that it can successfully solve, depends on various factors such as the CB size and density. Besides, it is not obvious to specify the exactly relationship between the individual and the overall cases competence. Hence, numerous Competence Models have been proposed to evaluate CBs and predict their actual coverage and competence on problem-solving. However, to the best of our knowledge, all of them are totally neglecting the uncertain aspect of information which is widely presented in cases since they involve real world situations. Therefore, this paper presents a new competence model called CEC-Model (Coverage & Evidential Clustering based Model) which manages uncertainty during both of cases clustering and similarity measurement using a powerful tool called the belief function theory

Pancreatic surgery outcomes: multicentre prospective snapshot study in 67 countries

Background: Pancreatic surgery remains associated with high morbidity rates. Although postoperative mortality appears to have improved with specialization, the outcomes reported in the literature reflect the activity of highly specialized centres. The aim of this study was to evaluate the outcomes following pancreatic surgery worldwide.Methods: This was an international, prospective, multicentre, cross-sectional snapshot study of consecutive patients undergoing pancreatic operations worldwide in a 3-month interval in 2021. The primary outcome was postoperative mortality within 90 days of surgery. Multivariable logistic regression was used to explore relationships with Human Development Index (HDI) and other parameters.Results: A total of 4223 patients from 67 countries were analysed. A complication of any severity was detected in 68.7 percent of patients (2901 of 4223). Major complication rates (Clavien-Dindo grade at least IIIa) were 24, 18, and 27 percent, and mortality rates were 10, 5, and 5 per cent in low-to-middle-, high-, and very high-HDI countries respectively. The 90-day postoperative mortality rate was 5.4 per cent (229 of 4223) overall, but was significantly higher in the low-to-middle-HDI group (adjusted OR 2.88, 95 per cent c.i. 1.80 to 4.48). The overall failure-to-rescue rate was 21 percent; however, it was 41 per cent in low-to-middle-compared with 19 per cent in very high-HDI countries.Conclusion: Excess mortality in low-to-middle-HDI countries could be attributable to failure to rescue of patients from severe complications. The authors call for a collaborative response from international and regional associations of pancreatic surgeons to address management related to death from postoperative complications to tackle the global disparities in the outcomes of pancreatic surgery (NCT04652271; ISRCTN95140761)