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A Comparative Study on Vector Similarity Methods for Offer Generation in Multi-attribute Negotiation

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AI 2015: Advances in Artificial Intelligence (AI 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9457))

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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.

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Notes

  1. 1.

    In this paper, agents do not reveal their utility function to their opponent.

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Author information

Authors and Affiliations

  1. Faculty of ESTeM, University of Canberra, Canberra, Australia

    Aodah Diamah & Michael Wagner

  2. College of Engineering and Computer Science, Australian National University, Canberra, Australia

    Michael Wagner & Menkes van den Briel

  3. National ICT Australia, Eveleigh, Australia

    Menkes van den Briel

Authors
  1. Aodah Diamah
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  2. Michael Wagner
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  3. Menkes van den Briel
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Corresponding author

Correspondence to Aodah Diamah .

Editor information

Editors and Affiliations

  1. The University of Waikato, Hamilton, New Zealand

    Bernhard Pfahringer

  2. The Australian National University, Canberra, Aust Capital Terr, Australia

    Jochen Renz

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© 2015 Springer International Publishing Switzerland

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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: Pfahringer, B., Renz, J. (eds) AI 2015: Advances in Artificial Intelligence. AI 2015. Lecture Notes in Computer Science(), vol 9457. Springer, Cham. https://doi.org/10.1007/978-3-319-26350-2_13

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  • DOI: https://doi.org/10.1007/978-3-319-26350-2_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26349-6

  • Online ISBN: 978-3-319-26350-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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