A PARALLEL ALGORITHM FOR BUILDING POSSIBILISTIC CAUSAL NETWORKS

Among the several representations of uncertainty, possibility theory a llows also for the management of imprecision coming from data. Domain models with inherent uncertainty and imprecision can be represented by means of possibilistic causal networks that, the possibilistic counte rpart of Bayesian belief networks. Only recently the definition of pos sibilistic network has been clearly stated and the corresponding infer ence algorithms developed. However, and in contrast to the correspondi ng developments in Bayesian networks, learning methods for possibilist ic networks are still few. We present here a new approach that hybridi zes two of the most used approaches in uncertain network learning: tho se methods based on conditional dependency information and those based on information quality measures. The resulting algorithm, POSSCAUSE, admits easily a parallel formulation. In the present paper POSSCAUSE i s presented and its main features discussed together with the underlyi ng new concepts used. (C) 1998 Elsevier Science Inc, All rights reserv ed.