13 research outputs found
A Unified Framework for Planning in Adversarial and Cooperative Environments
Users of AI systems may rely upon them to produce plans for achieving desired
objectives. Such AI systems should be able to compute obfuscated plans whose
execution in adversarial situations protects privacy, as well as legible plans
which are easy for team members to understand in cooperative situations. We
develop a unified framework that addresses these dual problems by computing
plans with a desired level of comprehensibility from the point of view of a
partially informed observer. For adversarial settings, our approach produces
obfuscated plans with observations that are consistent with at least k goals
from a set of decoy goals. By slightly varying our framework, we present an
approach for goal legibility in cooperative settings which produces plans that
achieve a goal while being consistent with at most j goals from a set of
confounding goals. In addition, we show how the observability of the observer
can be controlled to either obfuscate or clarify the next actions in a plan
when the goal is known to the observer. We present theoretical results on the
complexity analysis of our problems. We demonstrate the execution of obfuscated
and legible plans in a cooking domain using a physical robot Fetch. We also
provide an empirical evaluation to show the feasibility and usefulness of our
approaches using IPC domains.Comment: 8 pages, 2 figure
Communicative Robot Signals: Presenting a New Typology for Human-Robot Interaction
© 2023 The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/We present a new typology for classifying signals from robots when they communicate with humans. For inspiration, we use ethology, the study of animal behaviour and previous efforts from literature as guides in defining the typology. The typology is based on communicative signals that consist of five properties: the origin where the signal comes from, the deliberateness of the signal, the signal's reference, the genuineness of the signal, and its clarity (i.e. how implicit or explicit it is). Using the accompanying worksheet, the typology is straightforward to use to examine communicative signals from previous human-robot interactions and provides guidance for designers to use the typology when designing new robot behaviours