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A multi-choice offer strategy for bilateral multi-issue negotiations using modified DWM learning

Published: 03 August 2011 Publication History

Abstract

This paper introduces a "multi-choice" offer strategy for an automated agent conducting bilateral multi-issue negotiations in an agent-to-human negotiation setting. Assuming that a rational human counterpart is more likely to concede on less important issues, we developed a modified dynamic weighted majority (DWM) learning algorithm for the negotiation agent to estimate the issue weights and issue ranks of the human counterpart. The agent then utilizes these estimates to strategically propose counter-offers with multiple choices to the human counterpart. This strategy allows the agent to expedite the negotiation process and increase the chance of agreement by improving the satisfaction level of the counterpart. We validated this offer strategy using two sets of buyer behavior data: one simulated based on time-dependent behavior models used in the literature, and another collected from a human experiment on automated negotiations. Results indicate that, when compared to other offer strategies described in the literature with similar learning speeds, (i) the modified DWM-based learning algorithm estimates the counterpart's issue weight/rank more accurately, and (ii) the multi-choice offer strategy utilizing the learning algorithm makes more attractive offers to the counterpart while maintaining the same utility for the agent.

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Cited By

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  • (2020)Lifecycle Model of a Negotiation Agent: A Survey of Automated Negotiation TechniquesGroup Decision and Negotiation10.1007/s10726-020-09704-zOnline publication date: 12-Sep-2020
  • (2012)A review of strategy design and evaluation of software negotiation agentsProceedings of the 14th Annual International Conference on Electronic Commerce10.1145/2346536.2346565(155-156)Online publication date: 7-Aug-2012
  • (undefined)Effects of Communicating Issue Priority for Preference Tradeoffs in Agent-Human NegotiationsSSRN Electronic Journal10.2139/ssrn.2117894

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  1. A multi-choice offer strategy for bilateral multi-issue negotiations using modified DWM learning

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      cover image ACM Other conferences
      ICEC '11: Proceedings of the 13th International Conference on Electronic Commerce
      August 2011
      261 pages
      ISBN:9781450314282
      DOI:10.1145/2378104
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      Published: 03 August 2011

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      Author Tags

      1. agent-to-human negotiation
      2. automated bilateral multi-issue negotiation
      3. counterpart preference modeling
      4. ensemble method
      5. issue rank
      6. issue weight
      7. multiple offers
      8. online learning

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      ICEC '11
      ICEC '11: 13th International Conference on Electronic Commerce
      August 3 - 5, 2011
      Liverpool, United Kingdom

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      View all
      • (2020)Lifecycle Model of a Negotiation Agent: A Survey of Automated Negotiation TechniquesGroup Decision and Negotiation10.1007/s10726-020-09704-zOnline publication date: 12-Sep-2020
      • (2012)A review of strategy design and evaluation of software negotiation agentsProceedings of the 14th Annual International Conference on Electronic Commerce10.1145/2346536.2346565(155-156)Online publication date: 7-Aug-2012
      • (undefined)Effects of Communicating Issue Priority for Preference Tradeoffs in Agent-Human NegotiationsSSRN Electronic Journal10.2139/ssrn.2117894

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