The University of Southampton
University of Southampton Institutional Repository

Learning when and how to co-ordinate

Learning when and how to co-ordinate
Learning when and how to co-ordinate
This paper examines the potential and the impact of introducing learning capabilities into autonomous agents that make decisions at run-time about which mechanism to exploit in order to coordinate their activities. Specifically, the efficacy of learning is evaluated for making the decisions that are involved in determining when and how to coordinate. Our motivating hypothesis is that to deal with dynamic and unpredictable environments it is important to have agents that can learn the right situations in which to attempt to coordinate and the right method to use in those situations. This hypothesis is evaluated empirically, using reinforced based algorithms, in a grid-world scenario in which a) an agent's predictions about the other agents in the environment are approximately correct and b) an agent can not correctly predict the others' behaviour. The results presented show when, where and why learning is effective when it comes to making a decision about selecting a coordination mechanism.
203-218
Excelente-Toledo, C.B.
20ddd7e4-1e7d-4b11-9ce2-fa8bd6676984
Jennings, N. R.
ab3d94cc-247c-4545-9d1e-65873d6cdb30
Excelente-Toledo, C.B.
20ddd7e4-1e7d-4b11-9ce2-fa8bd6676984
Jennings, N. R.
ab3d94cc-247c-4545-9d1e-65873d6cdb30

Excelente-Toledo, C.B. and Jennings, N. R. (2003) Learning when and how to co-ordinate. International Journal of Web Intelligence and Agent Systems, 1 (3-4), 203-218.

Record type: Article

Abstract

This paper examines the potential and the impact of introducing learning capabilities into autonomous agents that make decisions at run-time about which mechanism to exploit in order to coordinate their activities. Specifically, the efficacy of learning is evaluated for making the decisions that are involved in determining when and how to coordinate. Our motivating hypothesis is that to deal with dynamic and unpredictable environments it is important to have agents that can learn the right situations in which to attempt to coordinate and the right method to use in those situations. This hypothesis is evaluated empirically, using reinforced based algorithms, in a grid-world scenario in which a) an agent's predictions about the other agents in the environment are approximately correct and b) an agent can not correctly predict the others' behaviour. The results presented show when, where and why learning is effective when it comes to making a decision about selecting a coordination mechanism.

Text
WIAS-03.pdf - Other
Download (286kB)

More information

Published date: 2003
Organisations: Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 258844
URI: http://eprints.soton.ac.uk/id/eprint/258844
PURE UUID: de436448-3b5e-43f2-9e40-fbea9d519b16

Catalogue record

Date deposited: 16 Mar 2004
Last modified: 29 Jan 2020 15:33

Export record

Contributors

Author: C.B. Excelente-Toledo
Author: N. R. Jennings

University divisions

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.

×