  For language-capable interactive robots to be effectively introduced into
human society, they must be able to naturally and efficiently communicate about
the objects, locations, and people found in human environments. An important
aspect of natural language communication is the use of pronouns. Ac-cording to
the linguistic theory of the Givenness Hierarchy(GH), humans use pronouns due
to implicit assumptions about the cognitive statuses their referents have in
the minds of their conversational partners. In previous work, Williams et al.
presented the first computational implementation of the full GH for the purpose
of robot language understanding, leveraging a set of rules informed by the GH
literature. However, that approach was designed specifically for language
understanding,oriented around GH-inspired memory structures used to assess what
entities are candidate referents given a particular cognitive status. In
contrast, language generation requires a model in which cognitive status can be
assessed for a given entity. We present and compare two such models of
cognitive status: a rule-based Finite State Machine model directly informed by
the GH literature and a Cognitive Status Filter designed to more flexibly
handle uncertainty. The models are demonstrated and evaluated using a
silver-standard English subset of the OFAI Multimodal Task Description Corpus.
