3 research outputs found
Augmenting Robot Knowledge Consultants with Distributed Short Term Memory
Human-robot communication in situated environments involves a complex
interplay between knowledge representations across a wide variety of
modalities. Crucially, linguistic information must be associated with
representations of objects, locations, people, and goals, which may be
represented in very different ways. In previous work, we developed a Consultant
Framework that facilitates modality-agnostic access to information distributed
across a set of heterogeneously represented knowledge sources. In this work, we
draw inspiration from cognitive science to augment these distributed knowledge
sources with Short Term Memory Buffers to create an STM-augmented algorithm for
referring expression generation. We then discuss the potential performance
benefits of this approach and insights from cognitive science that may inform
future refinements in the design of our approach.Comment: International Conference on Social Robotics (ICSR) 201
Enabling Morally Sensitive Robotic Clarification Requests
The design of current natural language oriented robot architectures enables
certain architectural components to circumvent moral reasoning capabilities.
One example of this is reflexive generation of clarification requests as soon
as referential ambiguity is detected in a human utterance. As shown in previous
research, this can lead robots to (1) miscommunicate their moral dispositions
and (2) weaken human perception or application of moral norms within their
current context. We present a solution to these problems by performing moral
reasoning on each potential disambiguation of an ambiguous human utterance and
responding accordingly, rather than immediately and naively requesting
clarification. We implement our solution in the DIARC robot architecture,
which, to our knowledge, is the only current robot architecture with both moral
reasoning and clarification request generation capabilities. We then evaluate
our method with a human subjects experiment, the results of which indicate that
our approach successfully ameliorates the two identified concerns.Comment: Accepted for nonarchival presentation at Advances in Cognitive
Systems (ACS) 202
Toward Forgetting-Sensitive Referring Expression Generationfor Integrated Robot Architectures
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.Comment: Accepted for (nonarchival) presentation at Advances in Cognitive
Systems (ACS) 202