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This research has produced the following outcomes.
A novel approach and its materialisation are proposed to support the BNKE process through visualisation and verbal explanation of the qualitative part of BNs independently of the quantitative part.
Using V-NET, the proposed materialisation of the approach proved to be useful and satisfactory with regard to its goals:
This thesis is organised as follows:
In chapter 2 I review the current literature, covering the mathematical basis on which BNs are based, and describing the different aspects of constructing a BN, the problems involved, the existing tools to support the process and what is missing.
In chapter 3 I propose an approach for further supporting of the KE process and the materialisation of this approach.
In chapter 4 I describe a tool I constructed (V-NET) to evaluate some aspects of this approach.
In chapter 5 I present and analyse the results of the evaluation of the approach, through V-NET, using qualitative case studies done in different settings. Chapter 5 is concluded with a discussion on the outcome of the evaluation, particularly with regard to the provision of practical answers to the questions posed in section 1.2.
The conclusions from my research and the suggested future work are presented in chapter 6.
This thesis describes a proposed approach for investigating the structure of Bayesian networks (BNs) and supporting the knowledge engineering (KE) process. To this end, in this chapter I review of the background literature regarding the various aspects of BNs, associated tools and methods of explaining BN models.
I start with a description of the mathematical background for models in artificial intelligence. In particular, I focus on BNs, models that are based on probability theory and graph theory. I review the process of constructing BNs, i.e. the KE process. I describe both aspects of the KE process, namely knowledge elicitation from experts and learning the structure from data using algorithms, which is a different aspect of the process of KE of BNs.
I then review the existing BN software tools, describing their advantages and limitations. Finally, I conclude with a description of explanation methods in BNs, since they can be utilised in the KE process of BNs.
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