This thesis presents a framework for the formulation of knowledge models to sup¬
port the generation of explanations for engineering systems that are represented by the
resulting models. Such models are automatically assembled from instantiated generic
component descriptions, known as modelfragments. The model fragments are of suffi¬
cient detail that generally satisfies the requirements of information content as identified
by the user asking for explanations.
Through a combination of fuzzy logic based evidence preparation, which exploits the
history of prior user preferences, and an approximate reasoning inference engine, with
a Bayesian evidence propagation mechanism, different uncertainty sources can be han¬
dled. Model fragments, each representing structural or behavioural aspects of a com¬
ponent of the domain system of interest, are organised in a library. Those fragments
that represent the same domain system component, albeit with different representation
detail, form parts of the same assumption class in the library. Selected fragments are
assembled to form an overall system model, prior to extraction of any textual infor¬
mation upon which to base the explanations. The thesis proposes and examines the
techniques that support the fragment selection mechanism and the assembly of these
fragments into models.
In particular, a Bayesian network-based model fragment selection mechanism is de¬
scribed that forms the core of the work. The network structure is manually determined
prior to any inference, based on schematic information regarding the connectivity of
the components present in the domain system under consideration. The elicitation
of network probabilities, on the other hand is completely automated using probability
elicitation heuristics. These heuristics aim to provide the information required to select
fragments which are maximally compatible with the given evidence of the fragments
preferred by the user. Given such initial evidence, an existing evidence propagation
algorithm is employed. The preparation of the evidence for the selection of certain
fragments, based on user preference, is performed by a fuzzy reasoning evidence fab¬
rication engine. This engine uses a set of fuzzy rules and standard fuzzy reasoning
mechanisms, attempting to guess the information needs of the user and suggesting the selection of fragments of sufficient detail to satisfy such needs. Once the evidence
is propagated, a single fragment is selected for each of the domain system compo¬
nents and hence, the final model of the entire system is constructed. Finally, a highly
configurable XML-based mechanism is employed to extract explanation content from
the newly formulated model and to structure the explanatory sentences for the final
explanation that will be communicated to the user.
The framework is illustratively applied to a number of domain systems and is compared
qualitatively to existing compositional modelling methodologies. A further empirical
assessment of the performance of the evidence propagation algorithm is carried out to
determine its performance limits. Performance is measured against the number of frag¬
ments that represent each of the components of a large domain system, and the amount
of connectivity permitted in the Bayesian network between the nodes that stand for
the selection or rejection of these fragments. Based on this assessment recommenda¬
tions are made as to how the framework may be optimised to cope with real world
applications
