N2 - In which uncertainty in natural language dialogue is introduced asthe central problem in the research described in this thesis. The ideaof using of Bayesian networks is hypothesised as a possible solution to this problem. Dialogue acts are presented as the central notion in our approach to dialogue modelling and the task of recognising dialogue act types as the central topic in the experiments.
AB - In which uncertainty in natural language dialogue is introduced asthe central problem in the research described in this thesis. The ideaof using of Bayesian networks is hypothesised as a possible solution to this problem. Dialogue acts are presented as the central notion in our approach to dialogue modelling and the task of recognising dialogue act types as the central topic in the experiments.
Thefollowing have been heavily used in my group: support vector machines, decisiontree learning, artificial neural networks, Bayesian networks, genetic andmimetic algorithms.
In this article, basic definitions were explored for different components in terms of rules, organizations and needs; which can be quite useful for countries that are still in the early stage of creating a National Spatial Infrastructure design.
Key words: NSDI, Data, Metadata, Clearinghouse, Standard, Partnership,Geo-data
 Al Shamsi, S.
The developed software is analysed by an illustrative example for loading and unloading operation with same data.
Key words: Attribute, selection of robot, MADM approach, rank according to reqirement
 Zeshui Xu, "a method for multiple attribute decision making with incomplete weight information in linguistic setting", Elsevier Science Publishers, Vol.-20,Issue 8, 719-725
For instance, an instantiation may correspond to a physical object or it may describe a set of entities that occur at the same instant of time (a dynamic Bayesian network (Kjærulff 1992) is a special case of an OOBN).
The papers span work in which Bayesian networks are merely used as a modelling tool (Paper I), work where models are specially designed to utilize the inference algorithms of Bayesian networks (Paper II and Paper III), and work where the focus has been on extending the applicability of Bayesian networks to very large domains (Paper IV and Paper V).Paper I is in this respect an application paper, where model building, estimation and inference in a complex time-evolving model is simplified by focusing on the conditional independence statements embedded in the model; it is written with the reliability data analyst in mind.
Our model is represented by a Bayesian network, and we use the conditional independence relations encoded in the network structure in the calculation scheme employed to generate parameter estimates.In Paper II we target the problem of fault diagnosis, i.e., to efficiently generate an inspection strategy to detect and repair a complex system.
The PhD thesis of Nils Jansen titled “Counterexamples in Probabilistic Verification” has recently been published online at the and can also be found . The thesis covers multiple approaches and new concepts to the generation and representation of counterexamples for refuted properties on probabilistic systems. All results have been published at several conference proceedings and journals.
This thesis introduces a method of applying Bayesian Networks to combine information from a range of data sources for effective decision support systems. It develops a set of techniques in development, validation, visualisation, and application of Complex Systems models, with a working demonstration in an Australian airport environment. The methods presented here have provided a modelling approach that produces highly flexible, informative and applicable interpretations of a system's behaviour under uncertain conditions. These end-to-end techniques are applied to the development of model based dashboards to support operators and decision makers in the multi-stakeholder airport environment. They provide highly flexible and informative interpretations and confidence in these interpretations of a system's behaviour under uncertain conditions.
Chapter 6 presents the reconstruction of a key metabolic pathway which plays an important role in ripening of tomatoes, thus showing the versatility of the use of Bayesian networks in metabolomics data analysis.
In Paper IV and Paper V we work with learning of parameters and specifying the structure in the OOBN definition of Bangsø and Wuillemin (2000).Paper IV describes a method for parameter learning in OOBNs.
In chapter 5 the use of Bayesian networks is shown on the combination of gene expression data and clinical parameters, to determine the effect of smoking on gene expression and which genes are responsible for the DNA damage and the raise in plasma cotinine levels of blood of a smoking population.