The University of Southampton
University of Southampton Institutional Repository

Severity-sensitive norm-governed multi-agent planning

Severity-sensitive norm-governed multi-agent planning
Severity-sensitive norm-governed multi-agent planning
In making practical decisions, agents are expected to comply with ideals of behaviour, or norms. In reality, it may not be possible for an individual, or a team of agents, to be fully compliant – actual behaviour often differs from the ideal. The question we address in this paper is how we can design agents that act in such a way that they select collective strategies to avoid more critical failures (norm violations), and mitigate the effects of violations that do occur. We model the normative requirements of a system through contrary-to-duty obligations and violation severity levels, and propose a novel multi-agent planning mechanism based on Decentralised POMDPs that uses a qualitative reward function to capture levels of compliance: N-Dec-POMDPs. We develop mechanisms for solving this type of multi-agent planning problem and show, through empirical analysis, that joint policies generated are equally as good as those produced through existing methods but with significant reductions in execution time.
norms, Multi-agent planning, Dec-POMDP
1387-2532
Gasparini, Luca
15ef6336-0f4e-4acf-8989-c6797c2e6120
Norman, Timothy
663e522f-807c-4569-9201-dc141c8eb50d
Kollingbaum, Martin J.
015a7895-2e8f-4e21-a2fc-a2fa1fae37df
Gasparini, Luca
15ef6336-0f4e-4acf-8989-c6797c2e6120
Norman, Timothy
663e522f-807c-4569-9201-dc141c8eb50d
Kollingbaum, Martin J.
015a7895-2e8f-4e21-a2fc-a2fa1fae37df

Gasparini, Luca, Norman, Timothy and Kollingbaum, Martin J. (2018) Severity-sensitive norm-governed multi-agent planning. Autonomous Agents and Multi-Agent Systems. (doi:10.1007/s10458-017-9372-x).

Record type: Article

Abstract

In making practical decisions, agents are expected to comply with ideals of behaviour, or norms. In reality, it may not be possible for an individual, or a team of agents, to be fully compliant – actual behaviour often differs from the ideal. The question we address in this paper is how we can design agents that act in such a way that they select collective strategies to avoid more critical failures (norm violations), and mitigate the effects of violations that do occur. We model the normative requirements of a system through contrary-to-duty obligations and violation severity levels, and propose a novel multi-agent planning mechanism based on Decentralised POMDPs that uses a qualitative reward function to capture levels of compliance: N-Dec-POMDPs. We develop mechanisms for solving this type of multi-agent planning problem and show, through empirical analysis, that joint policies generated are equally as good as those produced through existing methods but with significant reductions in execution time.

Text accepted-version - Accepted Manuscript
Available under License Creative Commons Attribution.
Download (519kB)

More information

Accepted/In Press date: 19 June 2017
e-pub ahead of print date: 7 July 2017
Published date: January 2018
Keywords: norms, Multi-agent planning, Dec-POMDP

Identifiers

Local EPrints ID: 412260
URI: https://eprints.soton.ac.uk/id/eprint/412260
ISSN: 1387-2532
PURE UUID: f05bb8d5-0ab3-43c2-b435-187e5b05200f
ORCID for Timothy Norman: ORCID iD orcid.org/0000-0002-6387-4034

Catalogue record

Date deposited: 14 Jul 2017 16:30
Last modified: 06 Jun 2018 12:19

Export record

Altmetrics

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 https://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.

×