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Autonomously Learning About Meaningful Actions from Exploratory Behaviour

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thesis
posted on 2021-11-15, 00:50 authored by Newton, Heidi

The thesis addresses the problem of creating an autonomous agent that is able to learn about and use meaningful hand motor actions in a simulated world with realistic physics, in a similar way to human infants learning to control their hand. A recent thesis by Mugan presented one approach to this problem using qualitative representations, but suffered from several important limitations. This thesis presents an alternative design that breaks the learning problem down into several distinct learning tasks. It presents a new method for learning rules about actions based on the Apriori algorithm. It also presents a planner inspired by infants that can use these rules to solve a range of tasks. Experiments showed that the agent was able to learn meaningful rules and was then able to successfully use them to achieve a range of simple planning tasks.

History

Copyright Date

2015-01-01

Date of Award

2015-01-01

Publisher

Te Herenga Waka—Victoria University of Wellington

Rights License

Author Retains Copyright

Degree Discipline

Computer Science

Degree Grantor

Te Herenga Waka—Victoria University of Wellington

Degree Level

Masters

Degree Name

Master of Science

ANZSRC Type Of Activity code

970108 Expanding Knowledge in the Information and Computing Sciences

Victoria University of Wellington Item Type

Awarded Research Masters Thesis

Language

en_NZ

Victoria University of Wellington School

School of Engineering and Computer Science

Advisors

Andreae, Peter