At the heart of statistical learning lies the concept of uncertainty.
Similarly, embodied agents such as robots
and animals must likewise address uncertainty, as sensation
is always only a partial reflection of reality. This
thesis addresses the role that uncertainty can play in
a central building block of intelligence: categorization.
Cognitive agents are able to perform tasks like categorical perception
through physical interaction (active categorical perception; ACP),
or passively at a distance (distal categorical perception; DCP).
It is possible that the former scaffolds the learning of
the latter. However, it is unclear whether DCP indeed scaffolds
ACP in humans and animals, nor how a robot could be trained
to likewise learn DCP from ACP. Here we demonstrate a method
for doing so which involves uncertainty: robots perform
ACP when uncertain and DCP when certain.
Furthermore, we demonstrate that robots trained
in such a manner are more competent at categorizing novel
objects than robots trained to categorize in other ways.
This suggests that such a mechanism would also be
useful for humans and animals, suggesting that they
may be employing some version of this mechanism.
| Identifer | oai:union.ndltd.org:uvm.edu/oai:scholarworks.uvm.edu:graddis-1580 |
| Date | 01 January 2016 |
| Creators | Powell, Nathaniel V. |
| Publisher | ScholarWorks @ UVM |
| Source Sets | University of Vermont |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Graduate College Dissertations and Theses |
Page generated in 0.002 seconds