Amen Ajroud

Institut Supérieur d'Informatique et des Techniques de communication, Hammam sousse, Tunisia · Informatique

Min-based (or qualitative) possibilistic networks are important tools to efficiently and compactly represent and analyze uncertain information. Inference is a crucial task in min-based networks, which consists of propagating information through the network structure to answer queries. Exact inference computes posteriori possibility distributions, g...

Min-based (or qualitative) possibilistic networks ap- pear to be important tools to efficiently and compactly represent and analyze uncertain information. Inference is the crucial task which consists in propagating information through the network structure. Exact inference calculates posterior possibilistic dis- tributions given an observed evidenc...

Most artificial intelligence applications, especially expert systems, have to reason and make decisions based on uncertain data and uncertain models. For this reason, several methods have been proposed for reasoning with different kinds of uncertainty. This paper discusses and illustrates some of the reasoning methods proposed by the artificial int...

Most artificial intelligence applications, especially expert systems, have to reason and make decisions based on uncertain data and uncertain models. For this reason, several methods have been proposed for reasoning with different kinds of uncertainty. This paper discusses and illustrates some of the reasoning methods proposed by the artificial int...

Possibilistic networks are useful tools for reasoning under uncertainty. Uncertain pieces of information can be described by different measures: possibility measures, necessity measures and more recently, guaranteed possibility measures, denoted by Delta. This paper first proposes the use of guaranteed possibility measures to define so-called Delta...

We present a novel inference algorithm which is an adaptation of Loopy Belief Propagation applied on Product-Based Possibilistic Networks. Without any transformation of the initial graph, the basic idea of this adaptation is to propagate evidence into network by passing messages between nodes until a convergence state is reached (if ever). Product-...

Product-Based Possibilistic Networks appear to be important tools to efficiently and compactly represent possibility distributions. The inference process is a crucial task to propagate information into network when new pieces of information, called evidence, are observed. However, this inference process is known to be a hard task especially for mul...

In this paper we present a synthesis of the work performed on two inference algorithms: the Pearl's belief propagation (BP) algorithm applied to Bayesian networks without loops (i.e. polytree) and the Loopy belief propagation (LBP) algorithm (inspired from the BP) which is applied to networks containing undirected cycles. It is known that the BP al...

In this paper we present a synthesis of the work performed on two inference algorithms: the Pearl's belief propagation (BP) algorithm applied to Bayesian networks without loops (i.e. polytree) and the Loopy belief propagation (LBP) algorithm (inspired from the BP) which is applied to networks containing undirected cycles. It is known that the BP al...