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

Information Systems

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

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: Advances in Artificial Intelligence
Subtitle of host publication	28th Australasian Joint Conference Proceedings
Editors	Jochen Renz, Bernhard Pfahringer
Place of Publication	Australia
Publisher	Springer
Pages	149-156
Number of pages	8
Volume	9457
ISBN (Electronic)	9783319263502
ISBN (Print)	9783319263496
DOIs	https://doi.org/10.1007/978-3-319-26350-2_13
Publication status	Published - 22 Nov 2015
Event	28th Australasian Joint Conference on Artificial Intelligence, AI 2015: Advances in Artificial Intelligence (AI 2015) - Canberra, 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
Abbreviated title	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 (Vol. 9457, pp. 149-156). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9457). Springer. 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. Vol. 9457 Australia : Springer, 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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title = "A Comparative Study on Vector Similarity Methods for Offer Generation in Multi-attribute Negotiation",

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

author = "Aodah DIAMAH and Michael WAGNER and {van den Briel}, Menkes",

year = "2015",

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

language = "English",

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volume = "9457",

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

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editor = "Jochen Renz and Bernhard Pfahringer",

booktitle = "AI 2015: Advances in Artificial Intelligence",

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note = "28th Australasian Joint Conference on Artificial Intelligence, AI 2015 : Advances in Artificial Intelligence (AI 2015), AI 2015 ; Conference date: 30-11-2015 Through 04-12-2015",

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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. vol. 9457, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9457, Springer, Australia, 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. Vol. 9457 Australia: Springer, 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: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

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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 - Multi-attribute negotiation

KW - Offer generation

KW - Vector similarity

KW - Cosine distance

KW - Euclidean distance

KW - Pareto-optimal solutions

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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. Vol. 9457. Australia: Springer. 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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