A comparative study on vector similarity methods for offer generation in multi-attribute negotiation

Aodah Diamah; Michael Wagner; Menkes van den Briel

doi:10.1007/978-3-319-26350-2_13

A comparative study on vector similarity methods for offer generation in multi-attribute negotiation

Aodah Diamah^*, Michael Wagner, Menkes van den Briel

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Citations (Scopus)

Abstract

Offer generation is an important mechanism in automated negotiation, in which a negotiating agent needs to select bids close to the opponent preference to increase their chance of being accepted. The existing offer generation approaches are either random, require partial knowledge of opponent preference or are domain-dependent. In this paper, we investigate and compare two vector similarity functions for generating offer vectors close to opponent preference. Vector similarities are not domain-specific, do not require different similarity functions for each negotiation domain and can be computed in incomplete-information negotiation. We evaluate negotiation outcomes by the joint gain obtained by the agents and by their closeness to Pareto-optimal solutions.

Original language	English
Title of host publication	AI 2015
Subtitle of host publication	Advances in Artificial Intelligence - 28th Australasian Joint Conference, Proceedings
Editors	Jochen Renz, Bernhard Pfahringer
Publisher	Springer Verlag
Pages	149-156
Number of pages	8
ISBN (Print)	9783319263496
DOIs	https://doi.org/10.1007/978-3-319-26350-2_13
Publication status	Published - 2015
Event	28th Australasian Joint Conference on Artificial Intelligence, AI 2015 - Canberra, Australia Duration: 30 Nov 2015 → 4 Dec 2015

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9457
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	28th Australasian Joint Conference on Artificial Intelligence, AI 2015
Country/Territory	Australia
City	Canberra
Period	30/11/15 → 4/12/15

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10.1007/978-3-319-26350-2_13

Cite this

Diamah, A., Wagner, M., & van den Briel, M. (2015). A comparative study on vector similarity methods for offer generation in multi-attribute negotiation. In J. Renz, & B. Pfahringer (Eds.), AI 2015: Advances in Artificial Intelligence - 28th Australasian Joint Conference, Proceedings (pp. 149-156). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9457). Springer Verlag. https://doi.org/10.1007/978-3-319-26350-2_13

Diamah, Aodah ; Wagner, Michael ; van den Briel, Menkes. / A comparative study on vector similarity methods for offer generation in multi-attribute negotiation. AI 2015: Advances in Artificial Intelligence - 28th Australasian Joint Conference, Proceedings. editor / Jochen Renz ; Bernhard Pfahringer. Springer Verlag, 2015. pp. 149-156 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Offer generation is an important mechanism in automated negotiation, in which a negotiating agent needs to select bids close to the opponent preference to increase their chance of being accepted. The existing offer generation approaches are either random, require partial knowledge of opponent preference or are domain-dependent. In this paper, we investigate and compare two vector similarity functions for generating offer vectors close to opponent preference. Vector similarities are not domain-specific, do not require different similarity functions for each negotiation domain and can be computed in incomplete-information negotiation. We evaluate negotiation outcomes by the joint gain obtained by the agents and by their closeness to Pareto-optimal solutions.",

keywords = "Cosine distance, Euclidean distance, Multi-attribute negotiation, Offer generation, Pareto-optimal solutions, Vector similarity",

author = "Aodah Diamah and Michael Wagner and {van den Briel}, Menkes",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 28th Australasian Joint Conference on Artificial Intelligence, AI 2015 ; Conference date: 30-11-2015 Through 04-12-2015",

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doi = "10.1007/978-3-319-26350-2_13",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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Diamah, A, Wagner, M & van den Briel, M 2015, A comparative study on vector similarity methods for offer generation in multi-attribute negotiation. in J Renz & B Pfahringer (eds), AI 2015: Advances in Artificial Intelligence - 28th Australasian Joint Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9457, Springer Verlag, pp. 149-156, 28th Australasian Joint Conference on Artificial Intelligence, AI 2015, Canberra, Australia, 30/11/15. https://doi.org/10.1007/978-3-319-26350-2_13

A comparative study on vector similarity methods for offer generation in multi-attribute negotiation. / Diamah, Aodah; Wagner, Michael; van den Briel, Menkes.
AI 2015: Advances in Artificial Intelligence - 28th Australasian Joint Conference, Proceedings. ed. / Jochen Renz; Bernhard Pfahringer. Springer Verlag, 2015. p. 149-156 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9457).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - A comparative study on vector similarity methods for offer generation in multi-attribute negotiation

AU - Diamah, Aodah

AU - Wagner, Michael

AU - van den Briel, Menkes

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2015.

PY - 2015

Y1 - 2015

N2 - Offer generation is an important mechanism in automated negotiation, in which a negotiating agent needs to select bids close to the opponent preference to increase their chance of being accepted. The existing offer generation approaches are either random, require partial knowledge of opponent preference or are domain-dependent. In this paper, we investigate and compare two vector similarity functions for generating offer vectors close to opponent preference. Vector similarities are not domain-specific, do not require different similarity functions for each negotiation domain and can be computed in incomplete-information negotiation. We evaluate negotiation outcomes by the joint gain obtained by the agents and by their closeness to Pareto-optimal solutions.

AB - Offer generation is an important mechanism in automated negotiation, in which a negotiating agent needs to select bids close to the opponent preference to increase their chance of being accepted. The existing offer generation approaches are either random, require partial knowledge of opponent preference or are domain-dependent. In this paper, we investigate and compare two vector similarity functions for generating offer vectors close to opponent preference. Vector similarities are not domain-specific, do not require different similarity functions for each negotiation domain and can be computed in incomplete-information negotiation. We evaluate negotiation outcomes by the joint gain obtained by the agents and by their closeness to Pareto-optimal solutions.

KW - Cosine distance

KW - Euclidean distance

KW - Multi-attribute negotiation

KW - Offer generation

KW - Pareto-optimal solutions

KW - Vector similarity

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DO - 10.1007/978-3-319-26350-2_13

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

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BT - AI 2015

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Diamah A, Wagner M, van den Briel M. A comparative study on vector similarity methods for offer generation in multi-attribute negotiation. In Renz J, Pfahringer B, editors, AI 2015: Advances in Artificial Intelligence - 28th Australasian Joint Conference, Proceedings. Springer Verlag. 2015. p. 149-156. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-26350-2_13

A comparative study on vector similarity methods for offer generation in multi-attribute negotiation

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