In this paper, we introduce B2DI model that extends BDI model to perform Bayesian inference under uncertainty. For scalability and flexibility purposes, Multiply Sectioned Bayesian Network (MSBN) technology has been selected and adapted to BDI agent reasoning. A belief update mechanism has been defined for agents, whose belief models are connected by public shared beliefs, and the certainty of ...

Bayesian belief networks or causal probabilistic networks may reach a certain size and complexity where the computations involved in exact probabilistic inference on the network tend to become rather time consuming. Methods for approximating a network by a simpler one allow the computational complexity of probabilistic inference on the network to be reduced at least to some extend. We propose a...

The fuzzy belief Petri net we propose in this paper propagates fuzzy beliefs from observations at nodes that represent measured parameters to fuzzy beliefs of the truths of parameters at hidden and decision nodes. The fuzzy influences spread from the observation nodes throughout our new enhanced bidirectional fuzzy belief Petri net. Compared with Bayesian belief networks, it is simpler and fast...

We apply a Bayesian belief network (BN) approach to vowel duration modelling, whereby vowel segment duration is modelled as a hybrid Bayesian network consisting of discrete and continuous nodes, with the nodes in the network representing linguistic factors that affect segment duration. Factor interaction is modelled in a concise way by causal relationships among the nodes in a directed acyclic ...

estimating the final price of products is of great importance. for manufacturing companies proposing a final price is only possible after the design process over. these companies propose an approximate initial price of the required products to the customers for which some of time and money is required. here using the existing data of already designed transformers and utilizing the bayesian anal...

estimating the final price of products is of great importance. for manufacturing companies proposing a final price is only possible after the design process over. these companies propose an approximate initial price of the required products to the customers for which some of time and money is required. here using the existing data of already designed transformers and utilizing the bayesian anal...

We present the infinite dynamic Bayesian network model (iDBN), a nonparametric, factored state-space model that generalizes dynamic Bayesian networks (DBNs). The iDBN can infer every aspect of a DBN: the number of hidden factors, the number of values each factor can take, and (arbitrarily complex) connections and conditionals between factors and observations. In this way, the iDBN generalizes o...

The Recurrence Local Computation Method (RLCM) for nding the most probable explanations (MPE) in a Bayesian belief network is valuable in assisting human beings to explain the possible causes of a set of evidences. However, RLCM works only on singly connected belief networks. This paper presents an extension of the RLCM which can be applied to multiply connected belief networks for nding arbitr...

The common belief is that a Bayesian network may achieve better performance with a more complex structure than a simpler structure which usually sacrifice the power of knowledge representation. It is often thought that having some additional nodes than necessary for knowledge representation does not, at least, deteriorate the performance of a Bayesian network. In practice, people often add some...

In the mid-1980s a new trend in probabilistic reasoning with uncertainty in knowledge-based systems became discernable taking a graphical representation of knowledge as a point of departure. We use the phrase network models to denote this type of model In the preceding sections, we have concentrated primarily on models for plausible reasoning that were developed especially for knowledge-based s...