Date of Award

2016

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Statistics

First Advisor

Jeff Buzas

Second Advisor

Josh Bongard

Abstract

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.

Language

en

Number of Pages

38 p.