Given a belief network with evidence, the task of finding the l most probable ex­ planations (MPE) in the belief network is that of identifying and ordering the l most probable instantiations of the non-evidence nodes of the belief network. Although many approaches have been proposed for solving this problem, most work only for restricted topologies (i.e., singly connected belief net­ works). I...

Bayesian models typically assume that agents are rational, logically omniscient and opinionated. The last of these has little descriptive or normative appeal, however, and limits our ability to describe how agents make up their minds (as opposed to changing them) or how they can suspend or withdraw their opinions. To address these limitations this paper represents the attitudinal states of non-...

In this paper we focus on the problems associated with distributed approaches to exact belief propagation in multi agent systems. In particular, we discuss the Multiply Sectioned Bayesian networks (MSBN) and Distributed Perception Networks (DPNs). While MSBNs support modeling of more complex domains than DPNs, we argue that MSBN approach is not suitable for large and changing agent societies. D...

Resilient stormwater infrastructure is one of the fundamental components resilient and sustainable cities. For this, resilience assessment against earthquake hazards crucial for municipal authorities. The objective this study to develop a framework pipe seismic hazards. A Bayesian belief network (BBN)-based model constructed based on published literature expert knowledge. developed implemented ...

Civil infrastructure supported by expansive clays is severely affected extensive volumetric deformations. The reliability prediction of such facilities quite challenging because the complex interactions between several contributing factors, as a scarcity data, lack analytical equations, correlations quantitative and qualitative information, data integration. main contribution this research deve...

Qualitative probabilistic networks (QPNs) [13] are an abstraction of in uence diagrams and Bayesian belief networks replacing numerical relations by qualitative in uences and synergies. To reason in a QPN is to nd the e ect of decision or new evidence on a variable of interest in terms of the sign of the change in belief (increase or decrease). We review our work on qualitative belief propagati...

Hidden Markov Models (HMMs) are learning methods for pattern recognition. The probabilistic HMMs have been one of the most used techniques based on the Bayesian model. First-order probabilistic HMMs were adapted to the theory of belief functions such that Bayesian probabilities were replaced with mass functions. In this paper, we present a second-order Hidden Markov Model using belief functions...

The interactions among peers in Peer-to-Peer systems as a distributed collaborative system are based on asynchronous and unreliable communications. Trust is an essential and facilitating component in these interactions specially in such uncertain environments. Various attacks are possible due to large-scale nature and openness of these systems that affects the trust. Peers has not enough inform...

This paper proposes a continuous speech driven photo realistic visual speech synthesis approach based on an articulatory dynamic Bayesian network model (AF_AVDBN) with constrained asynchrony. In the training of the AF_AVDBN model, the perceptual linear prediction (PLP) features and YUV features are extracted as acoustic and visual features respectively. Given an input speech and the trained AF_...