Dissimmetric fusion of incomplete data for classification of underwater objects

To classify objects located in their environment, underwater mobile robots use sequential sensory data (sonar) . These pieces of information are imperfect, that means imprecise, uncertain and incomplete . Incompleteness is defined as the unavailability of some parameters which makes some classification criteria impossible to compute and which delays the decisions . The paper proposes to model data in the framework of possibility theory, and to apply fuzzy calculus to evaluate criteria even in the case of incompleteness . Results are sequentially fused by a dissymmetric combination process . The different dissymmetric fusion rules are reviewed and a specific dissymmetric operator is proposed to solve the incompleteness problem .Afin de classer les objets présents dans leur environnement, les robots mobiles sous-marins peuvent exploiter des informations sensorielles acquises séquentiellement (sonar). Elles sont généralement qualifiées d'imparfaites, c'est-à-dire qu'elles sont imprécises, incertaines et incomplètes. L'incomplétude est vue ici comme l'indisponibilité d'un jeu de paramètres rendant impossible le calcul des critères de classification qui en dépendent, retardant ainsi la prise de décision. L'article propose de modéliser les informations dans le cadre de la théorie des possibilités, et d'appliquer le calcul flou afin d'évaluer des critères même en présence d'incomplétude. Les résultats ainsi obtenus sont fusionnés séquentiellement par un processus de combinaison dissymétrique. Les différentes lois de fusion dissymétrique sont passées en revue et une loi spécifique au traitement de l'incomplétude est proposée

Updating with belief functions, ordinal conditional functions and possibility measures

International audienceThis paper discusses how a measure of uncertainty representing a state of knowledge can be updated when a new information, which may be pervaded with uncertainty, becomes available. This problem is considered in various framework, namely: Shafer's evidence theory, Zadeh's possibility theory, Spohn's theory of epistemic states. In the two first cases, analogues of Jeffrey's rule of conditioning are introduced and discussed. The relations between Spohn's model and possibility theory are emphasized and Spohn's updating rule is contrasted with the Jeffrey-like rule of conditioning in possibility theory. Recent results by Shenoy on the combination of ordinal conditional functions are reinterpreted in the language of possibility theory. It is shown that Shenoy's combination rule has a well-known possibilistic counterpart