Planning for Goal-Oriented Dialogue Systems
  Generating complex multi-turn goal-oriented dialogue agents is a difficult
problem that has seen a considerable focus from many leaders in the tech
industry, including IBM, Google, Amazon, and Microsoft. This is in large part
due to the rapidly growing market demand for dialogue agents capable of
goal-oriented behaviour. Due to the business process nature of these
conversations, end-to-end machine learning systems are generally not a viable
option, as the generated dialogue agents must be deployable and verifiable on
behalf of the businesses authoring them.
  In this work, we propose a paradigm shift in the creation of goal-oriented
complex dialogue systems that dramatically eliminates the need for a designer
to manually specify a dialogue tree, which nearly all current systems have to
resort to when the interaction pattern falls outside standard patterns such as
slot filling. We propose a declarative representation of the dialogue agent to
be processed by state-of-the-art planning technology. Our proposed approach
covers all aspects of the process; from model solicitation to the execution of
the generated plans/dialogue agents. Along the way, we introduce novel planning
encodings for declarative dialogue synthesis, a variety of interfaces for
working with the specification as a dialogue architect, and a robust executor
for generalized contingent plans. We have created prototype implementations of
all components, and in this paper, we further demonstrate the resulting system
empirically.
