QML-AiNet: An Immune-inspired Network Approach to Qualitative Model Learning

Wei Pang; George MacLeod Coghill

doi:10.1007/978-3-642-14547-6_18

QML-AiNet: An Immune-inspired Network Approach to Qualitative Model Learning

Wei Pang, George MacLeod Coghill

Research output: Chapter in Book/Report/Conference proceeding › Published conference contribution

7 Citations (Scopus)

Abstract

In this paper we continue the research on applying immune- inspired algorithms as search strategies to Qualitative Model Learning (QML). A new search strategy based on opt-AiNet is proposed, and this results in the development of a novel QML system called QML-AiNet. The performance of QML-AiNet is compared with previous work us- ing the CLONALG approach. Experimental results shows that although not as efficient as CLONALG, the opt-AiNet based approach still shows promising results for learning qualitative models. In addition, possible fu- ture work to further improve the efficiency of QML-AiNet is also pointed out.

Original language	English
Title of host publication	proc. of 8th International Conference on Artificial Immune Systems (ICARIS 2010)
Editors	Emma Hart, Chris McEwan, Jon Timmis, Andy Hone
Place of Publication	Berlin Heidelberg
Publisher	Springer-Verlag
Pages	223-236
Number of pages	14
ISBN (Print)	978-3-642-14546-9
DOIs	https://doi.org/10.1007/978-3-642-14547-6_18
Publication status	Published - 2010

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer-Verlag
Volume	6209

Bibliographical note

A paid open access option is available for this journal.
Authors own final version only can be archived
Publisher's version/PDF cannot be used
On author's website immediately
On any open access repository after 12 months from publication
Published source must be acknowledged
Must link to publisher version
Set phrase to accompany link to published version (The original publication is available at www.springerlink.com)
Articles in some journals can be made Open Access on payment of additional charge

Access to Document

10.1007/978-3-642-14547-6_18

http://www.springerlink.com/content/134623807045v7u2/fulltext.pdf

Cite this

Pang, W., & Coghill, G. M. (2010). QML-AiNet: An Immune-inspired Network Approach to Qualitative Model Learning. In E. Hart, C. McEwan, J. Timmis, & A. Hone (Eds.), proc. of 8th International Conference on Artificial Immune Systems (ICARIS 2010) (pp. 223-236). (Lecture Notes in Computer Science; Vol. 6209). Springer-Verlag. https://doi.org/10.1007/978-3-642-14547-6_18

QML-AiNet: An Immune-inspired Network Approach to Qualitative Model Learning. / Pang, Wei; Coghill, George MacLeod.
proc. of 8th International Conference on Artificial Immune Systems (ICARIS 2010). ed. / Emma Hart; Chris McEwan; Jon Timmis; Andy Hone. Berlin Heidelberg: Springer-Verlag, 2010. p. 223-236 (Lecture Notes in Computer Science; Vol. 6209).

Research output: Chapter in Book/Report/Conference proceeding › Published conference contribution

Pang, W & Coghill, GM 2010, QML-AiNet: An Immune-inspired Network Approach to Qualitative Model Learning. in E Hart, C McEwan, J Timmis & A Hone (eds), proc. of 8th International Conference on Artificial Immune Systems (ICARIS 2010). Lecture Notes in Computer Science, vol. 6209, Springer-Verlag, Berlin Heidelberg, pp. 223-236. https://doi.org/10.1007/978-3-642-14547-6_18

@inproceedings{c5fd9fe2fdfb4626a9e8e3cfe26e3026,

title = "QML-AiNet: An Immune-inspired Network Approach to Qualitative Model Learning",

abstract = "In this paper we continue the research on applying immune- inspired algorithms as search strategies to Qualitative Model Learning (QML). A new search strategy based on opt-AiNet is proposed, and this results in the development of a novel QML system called QML-AiNet. The performance of QML-AiNet is compared with previous work us- ing the CLONALG approach. Experimental results shows that although not as efficient as CLONALG, the opt-AiNet based approach still shows promising results for learning qualitative models. In addition, possible fu- ture work to further improve the efficiency of QML-AiNet is also pointed out.",

author = "Wei Pang and Coghill, {George MacLeod}",

note = "A paid open access option is available for this journal. Authors own final version only can be archived Publisher's version/PDF cannot be used On author's website immediately On any open access repository after 12 months from publication Published source must be acknowledged Must link to publisher version Set phrase to accompany link to published version (The original publication is available at www.springerlink.com) Articles in some journals can be made Open Access on payment of additional charge",

year = "2010",

doi = "10.1007/978-3-642-14547-6_18",

language = "English",

isbn = "978-3-642-14546-9",

series = "Lecture Notes in Computer Science",

publisher = "Springer-Verlag",

pages = "223--236",

editor = "Emma Hart and Chris McEwan and Jon Timmis and Hone, {Andy }",

booktitle = "proc. of 8th International Conference on Artificial Immune Systems (ICARIS 2010)",

}

TY - GEN

T1 - QML-AiNet

T2 - An Immune-inspired Network Approach to Qualitative Model Learning

AU - Pang, Wei

AU - Coghill, George MacLeod

N1 - A paid open access option is available for this journal. Authors own final version only can be archived Publisher's version/PDF cannot be used On author's website immediately On any open access repository after 12 months from publication Published source must be acknowledged Must link to publisher version Set phrase to accompany link to published version (The original publication is available at www.springerlink.com) Articles in some journals can be made Open Access on payment of additional charge

PY - 2010

Y1 - 2010

N2 - In this paper we continue the research on applying immune- inspired algorithms as search strategies to Qualitative Model Learning (QML). A new search strategy based on opt-AiNet is proposed, and this results in the development of a novel QML system called QML-AiNet. The performance of QML-AiNet is compared with previous work us- ing the CLONALG approach. Experimental results shows that although not as efficient as CLONALG, the opt-AiNet based approach still shows promising results for learning qualitative models. In addition, possible fu- ture work to further improve the efficiency of QML-AiNet is also pointed out.

AB - In this paper we continue the research on applying immune- inspired algorithms as search strategies to Qualitative Model Learning (QML). A new search strategy based on opt-AiNet is proposed, and this results in the development of a novel QML system called QML-AiNet. The performance of QML-AiNet is compared with previous work us- ing the CLONALG approach. Experimental results shows that although not as efficient as CLONALG, the opt-AiNet based approach still shows promising results for learning qualitative models. In addition, possible fu- ture work to further improve the efficiency of QML-AiNet is also pointed out.

U2 - 10.1007/978-3-642-14547-6_18

DO - 10.1007/978-3-642-14547-6_18

M3 - Published conference contribution

SN - 978-3-642-14546-9

T3 - Lecture Notes in Computer Science

SP - 223

EP - 236

BT - proc. of 8th International Conference on Artificial Immune Systems (ICARIS 2010)

A2 - Hart, Emma

A2 - McEwan, Chris

A2 - Timmis, Jon

A2 - Hone, Andy

PB - Springer-Verlag

CY - Berlin Heidelberg

ER -

QML-AiNet: An Immune-inspired Network Approach to Qualitative Model Learning

Abstract

Publication series

Bibliographical note

Access to Document

Fingerprint

Cite this