One of the most interesting aspects of the Amazon Alexa Prize competition is
that the framing of the competition requires the development of new
computational models of dialogue and its structure. Traditional computational
models of dialogue are of two types: (1) task-oriented dialogue, supported by
AI planning models,or simplified planning models consisting of frames with
slots to be filled; or (2)search-oriented dialogue where every user turn is
treated as a search query that may elaborate and extend current search results.
Alexa Prize dialogue systems such as SlugBot must support conversational
capabilities that go beyond what these traditional models can do. Moreover,
while traditional dialogue systems rely on theoretical computational models,
there are no existing computational theories that circumscribe the expected
system and user behaviors in the intended conversational genre of the Alexa
Prize Bots. This paper describes how UCSC's SlugBot team has combined the
development of a novel computational theoretical model, Discourse Relation
Dialogue Model, with its implementation in a modular system in order to test
and refine it. We highlight how our novel dialogue model has led us to create a
novel ontological resource, UniSlug, and how the structure of UniSlug determine
show we curate and structure content so that our dialogue manager implements
and tests our novel computational dialogue model.

UCSC's SlugBot team has developed a novel Discourse Relation Dialogue Model and UniSlug ontological resource to create a modular dialogue system that supports conversational capabilities beyond traditional computational dialogue models, in response to the requirements of the Amazon Alexa Prize Bot competition.