iOOBN: a Bayesian network modelling tool using object oriented Bayesian networks with inheritance

Md. Samiullah, Thao Xuan Hoang, David Albrecht, Ann Nicholson, Kevin Korb

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

Abstract

The construction of Bayesian Networks (BNs) to model large-scale real-life problems is challenging. One approach to scaling up is Object Oriented Bayesian Networks (OOBNs). These provide modellers with the ability to define classes and construct models with a compositional and hierarchical structure, enabling reuse and supporting maintenance. In the OO programming paradigm, a key concept is inheritance, the ability to derive attributes and behavior from pre-existing classes, which enables an even higher level of reusability and scalability. However, inheritance in OOBNs has yet to be fully defined and implemented. Here we present iOOBN, a tool which provides fully defined inheritance for OOBNs. We provide guidance on modelling in iOOBN, describe our prototype implementation with an existing BN software tool, Hugin, and demonstrate its applicability and usefulness via a case study of re-engineering an existing large complex dynamic OOBN.

Original languageEnglish
Title of host publicationProceedings - 2017 International Conference on Tools with Artificial Intelligence - ICTAI 2017
Subtitle of host publicationBoston, USA 6-8 November 2017
EditorsMaria Virvou
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages1218-1225
Number of pages8
ISBN (Electronic)9781538638767
ISBN (Print)9781538638774
DOIs
Publication statusPublished - 2017
EventInternational Conference on Tools with Artificial Intelligence 2017 - Boston, United States of America
Duration: 6 Nov 20178 Nov 2017
Conference number: 29th
http://ictai2017.org/

Conference

ConferenceInternational Conference on Tools with Artificial Intelligence 2017
Abbreviated titleICTAI 2017
CountryUnited States of America
CityBoston
Period6/11/178/11/17
Internet address

Keywords

  • Artificial Intelligence
  • Bayesian Network
  • Inheritance
  • modeling application
  • Object Oriented Bayesian Network
  • Object Oriented Paradigm
  • tools of ai

Cite this

Samiullah, M., Xuan Hoang, T., Albrecht, D., Nicholson, A., & Korb, K. (2017). iOOBN: a Bayesian network modelling tool using object oriented Bayesian networks with inheritance. In M. Virvou (Ed.), Proceedings - 2017 International Conference on Tools with Artificial Intelligence - ICTAI 2017: Boston, USA 6-8 November 2017 (pp. 1218-1225). Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICTAI.2017.00185
Samiullah, Md. ; Xuan Hoang, Thao ; Albrecht, David ; Nicholson, Ann ; Korb, Kevin. / iOOBN : a Bayesian network modelling tool using object oriented Bayesian networks with inheritance. Proceedings - 2017 International Conference on Tools with Artificial Intelligence - ICTAI 2017: Boston, USA 6-8 November 2017 . editor / Maria Virvou. Piscataway NJ USA : IEEE, Institute of Electrical and Electronics Engineers, 2017. pp. 1218-1225
@inproceedings{8534804b873c4d3a8511f2ad7baa3e63,
title = "iOOBN: a Bayesian network modelling tool using object oriented Bayesian networks with inheritance",
abstract = "The construction of Bayesian Networks (BNs) to model large-scale real-life problems is challenging. One approach to scaling up is Object Oriented Bayesian Networks (OOBNs). These provide modellers with the ability to define classes and construct models with a compositional and hierarchical structure, enabling reuse and supporting maintenance. In the OO programming paradigm, a key concept is inheritance, the ability to derive attributes and behavior from pre-existing classes, which enables an even higher level of reusability and scalability. However, inheritance in OOBNs has yet to be fully defined and implemented. Here we present iOOBN, a tool which provides fully defined inheritance for OOBNs. We provide guidance on modelling in iOOBN, describe our prototype implementation with an existing BN software tool, Hugin, and demonstrate its applicability and usefulness via a case study of re-engineering an existing large complex dynamic OOBN.",
keywords = "Artificial Intelligence, Bayesian Network, Inheritance, modeling application, Object Oriented Bayesian Network, Object Oriented Paradigm, tools of ai",
author = "Md. Samiullah and {Xuan Hoang}, Thao and David Albrecht and Ann Nicholson and Kevin Korb",
year = "2017",
doi = "10.1109/ICTAI.2017.00185",
language = "English",
isbn = "9781538638774",
pages = "1218--1225",
editor = "Virvou, {Maria }",
booktitle = "Proceedings - 2017 International Conference on Tools with Artificial Intelligence - ICTAI 2017",
publisher = "IEEE, Institute of Electrical and Electronics Engineers",
address = "United States of America",

}

Samiullah, M, Xuan Hoang, T, Albrecht, D, Nicholson, A & Korb, K 2017, iOOBN: a Bayesian network modelling tool using object oriented Bayesian networks with inheritance. in M Virvou (ed.), Proceedings - 2017 International Conference on Tools with Artificial Intelligence - ICTAI 2017: Boston, USA 6-8 November 2017 . IEEE, Institute of Electrical and Electronics Engineers, Piscataway NJ USA, pp. 1218-1225, International Conference on Tools with Artificial Intelligence 2017, Boston, United States of America, 6/11/17. https://doi.org/10.1109/ICTAI.2017.00185

iOOBN : a Bayesian network modelling tool using object oriented Bayesian networks with inheritance. / Samiullah, Md.; Xuan Hoang, Thao; Albrecht, David; Nicholson, Ann; Korb, Kevin.

Proceedings - 2017 International Conference on Tools with Artificial Intelligence - ICTAI 2017: Boston, USA 6-8 November 2017 . ed. / Maria Virvou. Piscataway NJ USA : IEEE, Institute of Electrical and Electronics Engineers, 2017. p. 1218-1225.

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

TY - GEN

T1 - iOOBN

T2 - a Bayesian network modelling tool using object oriented Bayesian networks with inheritance

AU - Samiullah, Md.

AU - Xuan Hoang, Thao

AU - Albrecht, David

AU - Nicholson, Ann

AU - Korb, Kevin

PY - 2017

Y1 - 2017

N2 - The construction of Bayesian Networks (BNs) to model large-scale real-life problems is challenging. One approach to scaling up is Object Oriented Bayesian Networks (OOBNs). These provide modellers with the ability to define classes and construct models with a compositional and hierarchical structure, enabling reuse and supporting maintenance. In the OO programming paradigm, a key concept is inheritance, the ability to derive attributes and behavior from pre-existing classes, which enables an even higher level of reusability and scalability. However, inheritance in OOBNs has yet to be fully defined and implemented. Here we present iOOBN, a tool which provides fully defined inheritance for OOBNs. We provide guidance on modelling in iOOBN, describe our prototype implementation with an existing BN software tool, Hugin, and demonstrate its applicability and usefulness via a case study of re-engineering an existing large complex dynamic OOBN.

AB - The construction of Bayesian Networks (BNs) to model large-scale real-life problems is challenging. One approach to scaling up is Object Oriented Bayesian Networks (OOBNs). These provide modellers with the ability to define classes and construct models with a compositional and hierarchical structure, enabling reuse and supporting maintenance. In the OO programming paradigm, a key concept is inheritance, the ability to derive attributes and behavior from pre-existing classes, which enables an even higher level of reusability and scalability. However, inheritance in OOBNs has yet to be fully defined and implemented. Here we present iOOBN, a tool which provides fully defined inheritance for OOBNs. We provide guidance on modelling in iOOBN, describe our prototype implementation with an existing BN software tool, Hugin, and demonstrate its applicability and usefulness via a case study of re-engineering an existing large complex dynamic OOBN.

KW - Artificial Intelligence

KW - Bayesian Network

KW - Inheritance

KW - modeling application

KW - Object Oriented Bayesian Network

KW - Object Oriented Paradigm

KW - tools of ai

UR - http://www.scopus.com/inward/record.url?scp=85048471958&partnerID=8YFLogxK

U2 - 10.1109/ICTAI.2017.00185

DO - 10.1109/ICTAI.2017.00185

M3 - Conference Paper

SN - 9781538638774

SP - 1218

EP - 1225

BT - Proceedings - 2017 International Conference on Tools with Artificial Intelligence - ICTAI 2017

A2 - Virvou, Maria

PB - IEEE, Institute of Electrical and Electronics Engineers

CY - Piscataway NJ USA

ER -

Samiullah M, Xuan Hoang T, Albrecht D, Nicholson A, Korb K. iOOBN: a Bayesian network modelling tool using object oriented Bayesian networks with inheritance. In Virvou M, editor, Proceedings - 2017 International Conference on Tools with Artificial Intelligence - ICTAI 2017: Boston, USA 6-8 November 2017 . Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers. 2017. p. 1218-1225 https://doi.org/10.1109/ICTAI.2017.00185