Alternating Offers Protocols for Multilateral Negotiation

Aydoğan, Reyhan; Festen, David; Hindriks, Koen V.; Jonker, Catholijn M.

doi:10.1007/978-3-319-51563-2_10

Cited by 68 publications

(35 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our negotiation environment consists of 2 agents negotiating on multiple issues (e.g., travel destination, location, and date) within a certain time limit. The agents take their actions in a turn-taking fashion by following the alternating offers protocol [8]. There are 2 possible actions, which consist of accepting or rejecting the opponent's offer.…”

Section: Proposed Acceptance Strategymentioning

confidence: 99%

See 1 more Smart Citation

Deep reinforcement learning for acceptance strategy in bilateral negotiations

Razeghi¹,

Yavuz²,

Aydoğan³

2020

Turk J Elec Eng & Comp Sci

Self Cite

View full text Add to dashboard Cite

This paper introduces an acceptance strategy based on reinforcement learning for automated bilateral negotiation, where negotiating agents bargain on multiple issues in a variety of negotiation scenarios. Several acceptance strategies based on predefined rules have been introduced in the automated negotiation literature. Those rules mostly rely on some heuristics, which take time and/or utility into account. For some negotiation settings, an acceptance strategy solely based on a negotiation deadline might perform well; however, it might fail in another setting. Instead of following predefined acceptance rules, this paper presents an acceptance strategy that aims to learn whether to accept its opponent's offer or make a counter offer by reinforcement signals received after performing an action. In an experimental setup, it is shown that the performance of the proposed approach improves over time.

show abstract

Section: Proposed Acceptance Strategymentioning

confidence: 99%

“…This interaction is governed by a certain protocol determining the rules of encounter. The alternative offers protocol [8] is 1 of the most widely used protocols in bilateral negotiation. According to this protocol, an agent initiates the negotiation with an offer and its opponent can accept or reject this offer.…”

Section: Introductionmentioning

confidence: 99%

Deep reinforcement learning for acceptance strategy in bilateral negotiations

Razeghi¹,

Yavuz²,

Aydoğan³

2020

Turk J Elec Eng & Comp Sci

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, in recent years, multilateral negotiations have also attracted attention. From ANAC 2015, a multilateral negotiation protocol [19] was used instead of bilateral negotiation protocol [20] because a multi-lateral negotiation assumes more realistic scenarios. After that, many negotiation strategies have been proposed ( [21]- [24]).…”

Section: Related Workmentioning

confidence: 99%

Weighting Estimation Methods for Opponents' Utility Functions Using Boosting in Multi-Time Negotiations

Matsune

Fujita

2018

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

Recently, multi-issue closed negotiations have attracted attention in multi-agent systems. In particular, multi-time and multilateral negotiation strategies are important topics in multi-issue closed negotiations. In multi-issue closed negotiations, an automated negotiating agent needs to have strategies for estimating an opponent's utility function by learning the opponent's behaviors since the opponent's utility information is not open to others. However, it is difficult to estimate an opponent's utility function for the following reasons: (1) Training datasets for estimating opponents' utility functions cannot be obtained. (2) It is difficult to apply the learned model to different negotiation domains and opponents. In this paper, we propose a novel method of estimating the opponents' utility functions using boosting based on the least-squares method and nonlinear programming. Our proposed method weights each utility function estimated by several existing utility function estimation methods and outputs improved utility function by summing each weighted function. The existing methods using boosting are based on the frequency-based method, which counts the number of values offered, considering the time elapsed when they offered. Our experimental results demonstrate that the accuracy of estimating opponents' utility functions is significantly improved under various conditions compared with the existing utility function estimation methods without boosting.

show abstract

“…Each party complies to Stacked Alternating Offers Protocol (SAOP) [2], which is a turn-taking fashion protocol where each agent i is expected to provide an action a i in its turn during a negotiation session. Type of an action is denoted as type(a) which can take type(a) = {I bid , I accept , I end }; that is, the agent can make a counter offer, accept the offer in the negotiation table or walk away from the negotiation respectively.…”

Section: Problem Settingmentioning

confidence: 99%

“…Instead of following a one-shot strategy determination, an agent may combine multiple strategies of expert agents on-the-fly to achieve higher performance similarly to "mixture of experts", combining multiple learners approach in machine learning [1]. Accordingly, this paper studies how to combine multiple experts in multilateral negotiation settings in which more than two agents aim to reach a joint consensus by following Stacked Alternating Offer Protocol (SAOP) [2]. In the light of aforementioned motivations, this paper introduces two techniques to merge the strength of the experts: incremental portfolio strategy and crossover voting strategy.…”

mentioning

confidence: 99%

Collective Voice of Experts in Multilateral Negotiation

Güneş

Arditi

Aydoğan

2017

PRIMA 2017: Principles and Practice of Multi-Agent Systems

Self Cite

View full text Add to dashboard Cite

Inspired from the ideas such as "algorithm portfolio", "mixture of experts", and "genetic algorithm", this paper presents two novel negotiation strategies, which combine multiple negotiation experts to decide what to bid and what to accept during the negotiation. In the first approach namely incremental portfolio, a bid is constructed by asking each negotiation agent's opinion in the portfolio and picking one of the suggestions stochastically considering the expertise levels of the agents. In the second approach namely crossover strategy, each expert agent makes a bid suggestion and a majority voting is used on each issue value to decide the bid content. The proposed approaches have been evaluated empirically and our experimental results showed that the crossover strategy outperformed the top five finalists of the ANAC 2016 Negotiation Competition in terms of the obtained average individual utility.

show abstract

Alternating Offers Protocols for Multilateral Negotiation

Cited by 68 publications

References 14 publications

Deep reinforcement learning for acceptance strategy in bilateral negotiations

Deep reinforcement learning for acceptance strategy in bilateral negotiations

Weighting Estimation Methods for Opponents' Utility Functions Using Boosting in Multi-Time Negotiations

Collective Voice of Experts in Multilateral Negotiation

Contact Info

Product

Resources

About