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...

Two apparently contrasting theories have been proposed to account for the development of children's theory of mind (ToM): theory-theory and simulation theory. We present a Bayesian framework that rationally integrates both theories for false belief reasoning. This framework exploits two internal models for predicting the belief states of others: one of self and one of others. These internal mod...

The problems of database imbalance and factor interaction make modelling of segment duration in text-to-speech systems a challenging task. We therefore propose a probabilistic Bayesian belief network (BN) approach to tackle data sparsity and factor interaction problems. The belief network approach makes good estimations in cases of missed or incomplete data. Also, it captures factor interaction...

This paper presents a Bayesian method for constructing Bayesian belief networks from a database of cases. Potential applications include computer-assisted hypothesis testing, automated scientific discovery, and automated construction of probabilistic expert systems. Results are presented of a preliminary evaluation of an algorithm for constructing a belief network from a database of cases. We r...

Evaporation phenomena is a effective climate component on water resources management and has special importance in agriculture. In this paper, Bayesian belief networks (BBNs) as a non-linear modeling technique provide an evaporation estimation  method under uncertainty. As a case study, we estimated the surface water evaporation of the Persian Gulf and worked with a dataset of observations ...