Towards a self-adaptive agent-based simulation model
Agent-based simulation (ABS) modelling has been a widely applied approach for simulating domain-specific phenomena. Currently, parameters and environments are simulated by a domain-specific model that is strictly used proprietarily by the ABS model developer. This causes inflexibility towards extens...
Saved in:
Main Authors: | , , , |
---|---|
Other Authors: | |
Format: | Article |
Published: |
Asian Research Publishing Network
2023
|
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
my.uniten.dspace-22742 |
---|---|
record_format |
dspace |
spelling |
my.uniten.dspace-227422023-05-29T14:11:58Z Towards a self-adaptive agent-based simulation model Loo Y.L. Tang A.Y.C. Ahmad A. Mustapha A. 57188931634 36806985400 55390963300 57200530694 Agent-based simulation (ABS) modelling has been a widely applied approach for simulating domain-specific phenomena. Currently, parameters and environments are simulated by a domain-specific model that is strictly used proprietarily by the ABS model developer. This causes inflexibility towards extension of the developed ABS model, which will further result in difficulties for validation and verification of the robustness and reliability of the ABS model. To address this issue, this paper proposes a self-adaptive ABS model that is capable of modelling cross-domain phenomena by selecting the required parameters based on the environment. The capability to self-adapt will allow the model to be easily extended and replicated. The self-adapt capability is enabled by a governing algorithm within the model and is conceptually illustrated through a case study of crime report process ABS modelling. � 2005 - 2016 JATIT & LLS. All rights reserved. Final 2023-05-29T06:11:58Z 2023-05-29T06:11:58Z 2016 Article 2-s2.0-84964239610 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84964239610&partnerID=40&md5=0a1457c81cba5c3c2563a53685da2adf https://irepository.uniten.edu.my/handle/123456789/22742 86 2 240 249 Asian Research Publishing Network Scopus |
institution |
Universiti Tenaga Nasional |
building |
UNITEN Library |
collection |
Institutional Repository |
continent |
Asia |
country |
Malaysia |
content_provider |
Universiti Tenaga Nasional |
content_source |
UNITEN Institutional Repository |
url_provider |
http://dspace.uniten.edu.my/ |
description |
Agent-based simulation (ABS) modelling has been a widely applied approach for simulating domain-specific phenomena. Currently, parameters and environments are simulated by a domain-specific model that is strictly used proprietarily by the ABS model developer. This causes inflexibility towards extension of the developed ABS model, which will further result in difficulties for validation and verification of the robustness and reliability of the ABS model. To address this issue, this paper proposes a self-adaptive ABS model that is capable of modelling cross-domain phenomena by selecting the required parameters based on the environment. The capability to self-adapt will allow the model to be easily extended and replicated. The self-adapt capability is enabled by a governing algorithm within the model and is conceptually illustrated through a case study of crime report process ABS modelling. � 2005 - 2016 JATIT & LLS. All rights reserved. |
author2 |
57188931634 |
author_facet |
57188931634 Loo Y.L. Tang A.Y.C. Ahmad A. Mustapha A. |
format |
Article |
author |
Loo Y.L. Tang A.Y.C. Ahmad A. Mustapha A. |
spellingShingle |
Loo Y.L. Tang A.Y.C. Ahmad A. Mustapha A. Towards a self-adaptive agent-based simulation model |
author_sort |
Loo Y.L. |
title |
Towards a self-adaptive agent-based simulation model |
title_short |
Towards a self-adaptive agent-based simulation model |
title_full |
Towards a self-adaptive agent-based simulation model |
title_fullStr |
Towards a self-adaptive agent-based simulation model |
title_full_unstemmed |
Towards a self-adaptive agent-based simulation model |
title_sort |
towards a self-adaptive agent-based simulation model |
publisher |
Asian Research Publishing Network |
publishDate |
2023 |
_version_ |
1806426171158560768 |
score |
13.222552 |