A Multi-Agent Environment for Negotiation

Hindriks, Koen V.; Jonker, Catholijn M.; Tykhonov, Dmytro

doi:10.1007/978-0-387-89299-3_10

Cited by 7 publications

(3 citation statements)

References 22 publications

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“…Users can select the agent and accompanying strategy to support them with bidding and stopping advice. PN offers a range of strategies to pick from, including Tit-for-Tat, Hard Headed, Optimal Conceder, and Deniz, based on the literature [26,31,33,41].…”

Section: Strategy Selectionmentioning

confidence: 99%

A Survey of Decision Support Mechanisms for Negotiation

Aydoğan

Jonker

2023

Studies in Computational Intelligence

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This paper introduces a dependency analysis and a categorization of conceptualized and existing economic decision support mechanisms for negotiation. The focus of our survey is on economic decision support mechanisms, although some behavioural support mechanisms were included, to recognize the important work in that area. We categorize support mechanisms from four different aspects: (i) economic versus behavioral decision support, (ii) analytical versus strategical support, (iii) active versus passive support and (iv) implicit versus explicit support. Our survey suggests that active mechanisms would be more effective than passive ones, and that implicit mechanisms can shield the user from mathematical complexities. Furthermore, we provide a list of existing economic support mechanisms.

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Section: Strategy Selectionmentioning

confidence: 99%

A Survey of Decision Support Mechanisms for Negotiation

Aydoğan

Jonker

2023

Studies in Computational Intelligence

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“…Fundamental questions underlying these issues refer to the research into computationally efficient bidding strategies that lead to win‐win outcomes and cannot be exploited by the opponent (see for example, Jonker and Treur [2001]; Ludwig, Kersten, and Huang [2006]); the research in this area is ongoing. Also techniques must be improved to reduce the complexity of the negotiation space while maintaining accuracy in bidding (Hindriks, Jonker, and Tykhonov 2006). Heuristics must be developed for run‐time estimation of the Pareto‐efficient frontier and efficient outcomes, such as Nash, Kalai‐Smorodinski (Raiffa, Richardson, and Metcalfe 2002).…”

Section: The Pocket Negotiatormentioning

confidence: 99%

Negotiating Agents

et al. 2012

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Negotiation is a complex emotional decision-making process aiming to reach an agreement to exchange goods or services. From an agent technological perspective creating negotiating agents that can support humans with their negotiations is an interesting challenge. Already more than a decade, negotiating agents can outperform human beings (in terms of deal optimality) if the negotiation space is well-understood. However, the inherent semantic problem and the emotional issues involved make that negotiation cannot be handled by artificial intelligence alone, and a human-machine collaborative system is required. This article presents research goals, challenges, and an approach to create the next generation of negotiation support agents.

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“…Most research in multiparty automated negotiation has been focused on building efficient mechanisms and protocols to reach agreements among multiple participants, being an objective to optimize some type of social welfare measurement (Hindriks et al, 2009). Examples of such measurements would be the sum or product of utilities,themin utility, etc...…”

Section: Introductionmentioning

confidence: 99%