  To engage in human-like dialogue, robots require the ability to describe the
objects, locations, and people in their environment, a capability known as
"Referring Expression Generation." As speakers repeatedly refer to similar
objects, they tend to re-use properties from previous descriptions, in part to
help the listener, and in part due to cognitive availability of those
properties in working memory (WM). Because different theories of working memory
"forgetting" necessarily lead to differences in cognitive availability, we
hypothesize that they will similarly result in generation of different
referring expressions. To design effective intelligent agents, it is thus
necessary to determine how different models of forgetting may be differentially
effective at producing natural human-like referring expressions. In this work,
we computationalize two candidate models of working memory forgetting within a
robot cognitive architecture, and demonstrate how they lead to cognitive
availability-based differences in generated referring expressions.

Artificial Intelligence;Computation and Language;Robotics