7,970 research outputs found
On the likelihood of non-terrestrial artifacts in the Solar System
Extraterrestrial technology may exist in the Solar System without our
knowledge. This is because the vastness of space, combined with our limited
searches to date, implies that any remote unpiloted exploratory probes of
extraterrestrial origin would likely remain unnoticed. Here we develop a
probabilistic approach to quantify our certainty (or uncertainty) of the
existence of such technology in the Solar System. We discuss some possible
strategies for improving this uncertainty that include analysis of moon- and
Mars-orbiting satellite data as well as continued exploration of the Solar
System.Comment: Accepted for publication in Acta Astronautic
Using Wittgenstein’s family resemblance principle to learn exemplars
The introduction of the notion of family resemblance represented a major shift in Wittgenstein’s thoughts on the meaning of words, moving away from a belief that words
were well defined, to a view that words denoted less well defined categories of meaning.
This paper presents the use of the notion of family resemblance in the area of machine learning as an example of the benefits that can accrue from adopting the kind of paradigm shift taken by Wittgenstein. The paper presents a model capable of learning exemplars using the principle of family resemblance and adopting Bayesian networks for a representation of exemplars. An empirical evaluation is presented on three data sets and shows promising results that suggest that previous assumptions about the way we categories need reopening
Robot Navigation in Unseen Spaces using an Abstract Map
Human navigation in built environments depends on symbolic spatial
information which has unrealised potential to enhance robot navigation
capabilities. Information sources such as labels, signs, maps, planners, spoken
directions, and navigational gestures communicate a wealth of spatial
information to the navigators of built environments; a wealth of information
that robots typically ignore. We present a robot navigation system that uses
the same symbolic spatial information employed by humans to purposefully
navigate in unseen built environments with a level of performance comparable to
humans. The navigation system uses a novel data structure called the abstract
map to imagine malleable spatial models for unseen spaces from spatial symbols.
Sensorimotor perceptions from a robot are then employed to provide purposeful
navigation to symbolic goal locations in the unseen environment. We show how a
dynamic system can be used to create malleable spatial models for the abstract
map, and provide an open source implementation to encourage future work in the
area of symbolic navigation. Symbolic navigation performance of humans and a
robot is evaluated in a real-world built environment. The paper concludes with
a qualitative analysis of human navigation strategies, providing further
insights into how the symbolic navigation capabilities of robots in unseen
built environments can be improved in the future.Comment: 15 pages, published in IEEE Transactions on Cognitive and
Developmental Systems (http://doi.org/10.1109/TCDS.2020.2993855), see
https://btalb.github.io/abstract_map/ for access to softwar
- …