An opponent-adaptive strategy to increase utility and fairness in agents’ negotiation

Mirzayi, Sahar; Taghiyareh, Fattaneh; Nassiri-Mofakham, Faria

doi:10.1007/s10489-021-02638-2

Cited by 10 publications

(3 citation statements)

References 45 publications

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“…Significant effort has been put into developing adaptive strategies that learn opponent models and strategies (Bagga, Paoletti, and Stathis 2022;Baarslag et al 2016;Mirzayi, Taghiyareh, and Nassiri-Mofakham 2022;Sengupta, Mohammad, and Nakadai 2021). We do not include adaptive strategies in the mix of agents we test in our simulated marketplace as it is unlikely that the same opponent will be seen again in a large, open market environment.…”

Section: Related Workmentioning

confidence: 99%

“…Based on the shape of the curve representing a change in aspiration level, we identify two additional agent types when compared to a linear decrease with time: the Boulware and Conceder agents, with aspiration levels that decrease slower and faster than linear, respectively. We also include an adaptive, tit-for-tat strategy that responds to the negotiation partner's lowering of aspiration levels (Mirzayi, Taghiyareh, and Nassiri-Mofakham 2022).…”

Section: Introductionmentioning

confidence: 99%

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Relative Performance of Bilateral Multiattribute Negotiation Strategies in Open Markets

Brue,

Shymanski,

Karaoglu

et al. 2023

FLAIRS

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The long-running Automated Negotiating Agents Competition (ANAC) is comprised of various agent-agent and human-agent negotiation leagues. One such competition is the Automated Negotiation League (ANL) which involves repeated, bilateral negotiation over multiple issues. Researchers have investigated a tournament setting for this scenario involving a small, fixed number of agents. We are interested in automated agents participating in large and open marketplaces containing many instances of well-known agent types of varying sophistication. We experiment with four representative negotiation behaviors as agent types: Hardliner, Boulware, Conceder, and Tit-for-Tat. We simulate open markets with varying negotiation domain sizes, agent type distributions, and negotiation time available to evaluate the relative performances of different negotiation strategies. We analyze and report relative performances of the strategies on relevant performance metrics. We also extend this analysis using a head-to-head matrix.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Relative Performance of Bilateral Multiattribute Negotiation Strategies in Open Markets

Brue,

Shymanski,

Karaoglu

et al. 2023

FLAIRS

View full text Add to dashboard Cite

show abstract

“…Such methods can be divided into time-based strategies and behavior-based strategies (Faratin et al, 1998 ). Mirzayi et al ( 2021 ) proposed an opponent-adaptive concession method that creates a concession neighborhood around the target utility of each round, and the radius of the neighborhood growth rate is determined by the negotiation time. Mansour ( 2020 ) presented an imitation offer ration tactic that considers both the current concession behavior of the agent and that offered by its opponent.…”

Section: Introductionmentioning

confidence: 99%

An opponent model for agent-based shared decision-making via a genetic algorithm

Lin,

Wei,

Liu

et al. 2023

Front. Psychol.

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IntroductionShared decision-making (SDM) has received a great deal of attention as an effective way to achieve patient-centered medical care. SDM aims to bring doctors and patients together to develop treatment plans through negotiation. However, time pressure and subjective factors such as medical illiteracy and inadequate communication skills prevent doctors and patients from accurately expressing and obtaining their opponent's preferences. This problem leads to SDM being in an incomplete information environment, which significantly reduces the efficiency of the negotiation and even leads to failure.MethodsIn this study, we integrated a negotiation strategy that predicts opponent preference using a genetic algorithm with an SDM auto-negotiation model constructed based on fuzzy constraints, thereby enhancing the effectiveness of SDM by addressing the problems posed by incomplete information environments and rapidly generating treatment plans with high mutual satisfaction.ResultsA variety of negotiation scenarios are simulated in experiments and the proposed model is compared with other excellent negotiation models. The results indicated that the proposed model better adapts to multivariate scenarios and maintains higher mutual satisfaction.DiscussionThe agent negotiation framework supports SDM participants in accessing treatment plans that fit individual preferences, thereby increasing treatment satisfaction. Adding GA opponent preference prediction to the SDM negotiation framework can effectively improve negotiation performance in incomplete information environments.

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Taxonomy of Styles, Strategies, and Tactics in E-Negotiations

Meyer

Schoop

2023

Lecture Notes in Business Information Processing

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An opponent-adaptive strategy to increase utility and fairness in agents’ negotiation

Cited by 10 publications

References 45 publications

Relative Performance of Bilateral Multiattribute Negotiation Strategies in Open Markets

Relative Performance of Bilateral Multiattribute Negotiation Strategies in Open Markets

An opponent model for agent-based shared decision-making via a genetic algorithm

Taxonomy of Styles, Strategies, and Tactics in E-Negotiations

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