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Optimising Social Welfare in Multi-Resource Threshold Task Games

Research output: Chapter in Book/Report/Conference proceedingOther chapter contribution

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Optimising Social Welfare in Multi-Resource Threshold Task Games. / Habib, Fatma R.; Polukarov, Maria; Gerding, Enrico H.

PRIMA 2017: Principles and Practice of Multi-Agent Systems - 20th International Conference, Proceedings. Vol. 10621 LNAI Springer Verlag, 2017. p. 110-126 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10621 LNAI).

Research output: Chapter in Book/Report/Conference proceedingOther chapter contribution

Harvard

Habib, FR, Polukarov, M & Gerding, EH 2017, Optimising Social Welfare in Multi-Resource Threshold Task Games. in PRIMA 2017: Principles and Practice of Multi-Agent Systems - 20th International Conference, Proceedings. vol. 10621 LNAI, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10621 LNAI, Springer Verlag, pp. 110-126, 20th International Conference on Principles and Practice of Multi-Agent Systems, PRIMA 2017, Nice, France, 30/10/2017. https://doi.org/10.1007/978-3-319-69131-2_7

APA

Habib, F. R., Polukarov, M., & Gerding, E. H. (2017). Optimising Social Welfare in Multi-Resource Threshold Task Games. In PRIMA 2017: Principles and Practice of Multi-Agent Systems - 20th International Conference, Proceedings (Vol. 10621 LNAI, pp. 110-126). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10621 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-69131-2_7

Vancouver

Habib FR, Polukarov M, Gerding EH. Optimising Social Welfare in Multi-Resource Threshold Task Games. In PRIMA 2017: Principles and Practice of Multi-Agent Systems - 20th International Conference, Proceedings. Vol. 10621 LNAI. Springer Verlag. 2017. p. 110-126. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-69131-2_7

Author

Habib, Fatma R. ; Polukarov, Maria ; Gerding, Enrico H. / Optimising Social Welfare in Multi-Resource Threshold Task Games. PRIMA 2017: Principles and Practice of Multi-Agent Systems - 20th International Conference, Proceedings. Vol. 10621 LNAI Springer Verlag, 2017. pp. 110-126 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

Bibtex Download

@inbook{a23446d52ecb4b1b86991c1eac177b45,
title = "Optimising Social Welfare in Multi-Resource Threshold Task Games",
abstract = "In this paper, we introduce a discrete model for overlapping coalition formation called the multi-resource threshold task game (MR-TTG), which generalises the model introduced in [6]. Furthermore, we define the coalition structure generation (CSG) Problem for MR-TTGs. Towards the efficient solution of CSG problems for MR-TTGs, we provide two reductions to the well-known knapsack problems: the bounded multidimensional knapsack problem and the multiple-choice multidimensional knapsack problem. We then propose two branch and bound algorithms to compare between these reductions. Empirical evaluation shows that the latter reduction is more efficient in solving difficult instances of the problem.",
keywords = "Coalition formation, Cooperative games, Overlapping coalitions",
author = "Habib, {Fatma R.} and Maria Polukarov and Gerding, {Enrico H.}",
year = "2017",
month = "10",
day = "5",
doi = "10.1007/978-3-319-69131-2_7",
language = "English",
isbn = "9783319691305",
volume = "10621 LNAI",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "110--126",
booktitle = "PRIMA 2017",
address = "Germany",

}

RIS (suitable for import to EndNote) Download

TY - CHAP

T1 - Optimising Social Welfare in Multi-Resource Threshold Task Games

AU - Habib, Fatma R.

AU - Polukarov, Maria

AU - Gerding, Enrico H.

PY - 2017/10/5

Y1 - 2017/10/5

N2 - In this paper, we introduce a discrete model for overlapping coalition formation called the multi-resource threshold task game (MR-TTG), which generalises the model introduced in [6]. Furthermore, we define the coalition structure generation (CSG) Problem for MR-TTGs. Towards the efficient solution of CSG problems for MR-TTGs, we provide two reductions to the well-known knapsack problems: the bounded multidimensional knapsack problem and the multiple-choice multidimensional knapsack problem. We then propose two branch and bound algorithms to compare between these reductions. Empirical evaluation shows that the latter reduction is more efficient in solving difficult instances of the problem.

AB - In this paper, we introduce a discrete model for overlapping coalition formation called the multi-resource threshold task game (MR-TTG), which generalises the model introduced in [6]. Furthermore, we define the coalition structure generation (CSG) Problem for MR-TTGs. Towards the efficient solution of CSG problems for MR-TTGs, we provide two reductions to the well-known knapsack problems: the bounded multidimensional knapsack problem and the multiple-choice multidimensional knapsack problem. We then propose two branch and bound algorithms to compare between these reductions. Empirical evaluation shows that the latter reduction is more efficient in solving difficult instances of the problem.

KW - Coalition formation

KW - Cooperative games

KW - Overlapping coalitions

UR - http://www.scopus.com/inward/record.url?scp=85034239381&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-69131-2_7

DO - 10.1007/978-3-319-69131-2_7

M3 - Other chapter contribution

AN - SCOPUS:85034239381

SN - 9783319691305

VL - 10621 LNAI

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 110

EP - 126

BT - PRIMA 2017

PB - Springer Verlag

ER -

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