  This paper presented an XAI system specification and an interactive dialogue
model to facilitate the creation of Explanation Experiences (EE). Such
specifications combine the knowledge of XAI, domain and system experts of a use
case to formalise target user groups and their explanation needs and to
implement explanation strategies to address those needs. Formalising the XAI
system promotes the reuse of existing explainers and known explanation needs
that can be refined and evolved over time using user evaluation feedback. The
abstract EE dialogue model formalised the interactions between a user and an
XAI system. The resulting EE conversational chatbot is personalised to an XAI
system at run-time using the knowledge captured in its XAI system
specification. This seamless integration is enabled by using Behaviour Trees
(BT) to conceptualise both the EE dialogue model and the explanation
strategies. In the evaluation, we discussed several desirable properties of
using BTs over traditionally used STMs or FSMs. BTs promote the reusability of
dialogue components through the hierarchical nature of the design. Sub-trees
are modular, i.e. a sub-tree is responsible for a specific behaviour, which can
be designed in different levels of granularity to improve human
interpretability. The EE dialogue model consists of abstract behaviours needed
to capture EE, accordingly, it can be implemented as a conversational,
graphical or text-based interface which caters to different domains and users.
There is a significant computational cost when using BTs for modelling
dialogue, which we mitigate by using memory. Overall, we find that the ability
to create robust conversational pathways dynamically makes BTs a good candidate
for designing and implementing conversation for creating explanation
experiences.

Artificial Intelligence