The University of Southampton
University of Southampton Institutional Repository

Learning and adaptation of skills in autonomous physical agents

Learning and adaptation of skills in autonomous physical agents
Learning and adaptation of skills in autonomous physical agents
A skills learning methodology is presented for autonomous physical agents. Adaptation of skills and learning is a fundamental part of the simple agent behaviours outlined. A general framework of skills learning is described that uses skill macros to define simple behaviours by agents that communicate, sense and act in the physical world. Programmed playfulness can be easily implemented in this framework that plays an important part in acquiring sophisticated skills. Reusability of results in learning algorithms is supported by ontology based classification of learning in skills. Ontologies provide references to object instances that enable modularization of software and easy interfacing of skills with learning algorithms.
intelligent physical agents, formal methods, learning, adaptive control modelling for control
2671-2676
Veres, Sandor M.
909c60a0-56a3-4eb6-83e4-d52742ecd304
Veres, Aron G.
09b8a37e-f16a-43fb-ac24-f4a15f75d832
Veres, Sandor M.
909c60a0-56a3-4eb6-83e4-d52742ecd304
Veres, Aron G.
09b8a37e-f16a-43fb-ac24-f4a15f75d832

Veres, Sandor M. and Veres, Aron G. (2008) Learning and adaptation of skills in autonomous physical agents. 17th World Congress of International Federation of Automatic Control (IFAC), Seoul, Korea. 05 - 09 Jul 2008. pp. 2671-2676 .

Record type: Conference or Workshop Item (Paper)

Abstract

A skills learning methodology is presented for autonomous physical agents. Adaptation of skills and learning is a fundamental part of the simple agent behaviours outlined. A general framework of skills learning is described that uses skill macros to define simple behaviours by agents that communicate, sense and act in the physical world. Programmed playfulness can be easily implemented in this framework that plays an important part in acquiring sophisticated skills. Reusability of results in learning algorithms is supported by ontology based classification of learning in skills. Ontologies provide references to object instances that enable modularization of software and easy interfacing of skills with learning algorithms.

This record has no associated files available for download.

More information

Published date: 6 July 2008
Venue - Dates: 17th World Congress of International Federation of Automatic Control (IFAC), Seoul, Korea, 2008-07-05 - 2008-07-09
Keywords: intelligent physical agents, formal methods, learning, adaptive control modelling for control
Organisations: Astronautics Group

Identifiers

Local EPrints ID: 66022
URI: http://eprints.soton.ac.uk/id/eprint/66022
PURE UUID: 73f8947f-7b43-4940-a184-fa3cc41380e7

Catalogue record

Date deposited: 20 Apr 2009
Last modified: 08 Jan 2022 05:23

Export record

Contributors

Author: Sandor M. Veres
Author: Aron G. Veres

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×