Towards a virtual personal assistant based on a user-defined portfolio of multi-domain vocal applications

This paper proposes a novel approach to defining and simulating a new generation of virtual personal assistants as multi-application multi-domain distributed dialogue systems. The first contribution is the assistant architecture, composed of independent third-party applications handled by a Dispatcher. In this view, applications are black-boxes responding with a self-scored answer to user requests. Next, the Dispatcher distributes the current request to the most relevant application, based on these scores and the context (history of interaction etc.), and conveys its answer to the user. To address variations in the user-defined portfolio of applications, the second contribution, a stochastic model automates the online optimisation of the Dispatcher's behaviour. To evaluate the learnability of the Dispatcher's policy, several parametrisations of the user and application simulators are enabled, in such a way that they cover variations of realistic situations. Results confirm in all considered configurations of interest, that reinforcement learning can learn adapted strategies.