Efficient Methods for Multi-agent Multi-issue Negotiation: Allocating Resources

Wu, Mengxiao; Weerdt, Mathijs de; Poutré, Han La

doi:10.1007/978-3-642-11161-7_7

Cited by 14 publications

(14 citation statements)

References 15 publications

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“…Automated negotiation has proven to be valuable to support the decision-making process in scenarios where it is necessary to find an agreement quickly and with conflicting interests involved [ 12 ]. Potential applications of automated negotiation range from e-commerce [ 13 ] to task distribution problem solving, resource sharing or cooperative design [ 14 , 15 , 16 , 17 ]. One of the most important advantages of automated negotiation is that it takes into account the conflict of interests from the beginning.…”

Section: Introductionmentioning

confidence: 99%

Automated Negotiation for Resource Assignment in Wireless Surveillance Sensor Networks

Hoz

Giménez-Guzmán

Marsa-Maestre

et al. 2015

Sensors

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Due to the low cost of CMOS IP-based cameras, wireless surveillance sensor networks have emerged as a new application of sensor networks able to monitor public or private areas or even country borders. Since these networks are bandwidth intensive and the radioelectric spectrum is limited, especially in unlicensed bands, it is mandatory to assign frequency channels in a smart manner. In this work, we propose the application of automated negotiation techniques for frequency assignment. Results show that these techniques are very suitable for the problem, being able to obtain the best solutions among the techniques with which we have compared them.

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Section: Introductionmentioning

confidence: 99%

Automated Negotiation for Resource Assignment in Wireless Surveillance Sensor Networks

Hoz

Giménez-Guzmán

Marsa-Maestre

et al. 2015

Sensors

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“…It has been studied extensively in e-commerce settings (Myerson and Satterthwaite, 1983) (Athey and Segal,2007) (Lau et al, 2008) but can also be seen more generally as a paradigm for solving coordination and cooperation problems in complex multi-agent systems, e.g. for task allocation, resource sharing, or cooperative planning and design (Smith, 1980) (Hemaissia et al, 2007) (Wu et al, 2009) (Sim and Shi, 2009). The protocol used during the negotiation determines the rules of the negotiation.…”

Section: Introductionmentioning

confidence: 99%

From problems to protocols: Towards a negotiation handbook

Marsa-Maestre

Klein

Jonker

et al. 2014

Decision Support Systems

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Automated negotiation protocols represent a potentially powerful tool for problem solving in decision support systems involving participants with conflicting interests. However, the effectiveness of negotiation approaches depends greatly on the negotiation problem under consideration. Since there is no one negotiation protocol that clearly outperforms all others in all scenarios, we need to be able to decide which protocol is most suited for each particular problem. The goal of our work is to meet this challenge by defining a "negotiation handbook", that is, a collection of design rules which allow us, given a particular negotiation problem, to choose the most appropriate protocol to address it. This paper describes our progress towards this goal, including a tool for generating a wide range of negotiation scenarios, a set of high-level metrics for characterizing how negotiation scenarios differ, a testbed environment for evaluating protocol performance with different scenarios, and a community repository which allows us to systematically record and analyze protocol performance data.

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“…For 1 st Degree/Linear Polynomial, the mathematical equation is; (2) While for 2 nd Degree/Quadratic Polynomial; (3) Where ܷ is the predicted payoff from the model, ‫ݔ‬ are the independent variables, ߚ is polynomial coefficients, and ‫ܥ‬ is the model's constant. Indifference curves are produced through implementations of polynomial regressions (Somefun et al, 2004;Wu, Weerdt and Poutre, 2009). Another method to generate indifference curves is the Cobb-Douglas Function (Byrns and Stone, 1982;Train, 2003;Pavelescu, 2014), using the following formula: (4) Where ܷ is the predicted utility from the model, ‫ݔ‬ are the independent variables, ߚ is polynomial coefficients, and ‫ܥ‬ is the model's constant.…”

Section: Figure 6 Indifference Curves For Preference Elicitationmentioning

confidence: 99%

“…For this purpose, we implement Orthogonal Strategy (Wu, Weerdt and Poutre, 2009;Ma et al, 2010;Kamps, 2013). Orthogonal Strategy is a negotiation model which operates on stakeholders' indifference curves (Somefun et al, 2004).…”

Section: Orthogonal Strategymentioning

confidence: 99%

“…Implementations of the orthogonal strategy in computer mediated negotiations mostly only using one indifference curve (Gerding, 2004;Wu, Weerdt and Poutre, 2009). In this paper, an improvement to Orthogonal Strategy is introduced, which is the presence of multiple indifference curves for each stakeholder to represent his or her different payoffs towards different bundles of negotiation issues.…”

Section: Introductionmentioning

confidence: 99%

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Orthogonal strategy based computer-mediated negotiation: Principles and example

Susetyo

Timmermans

Vries

2018

Environment and Planning B: Urban Analytics and City Science

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Previous efforts to improve stakeholders’ involvement in planning and decision-making processes mostly put planners and decision makers as the ones who decide which solution is the best for the decision problems. In bottom-up planning and decision-making processes that supposedly involve stakeholders as much as possible, the most common practice is that when stakeholders have different preferences about the decision issues, supra decision makers such as planners and experts gather stakeholders’ preferences, and then, using their expertise and experience, decide what is the best choice for stakeholders. We approach the involvement of stakeholders in planning and decision-making not by relying on planners’ expertise but from a negotiation perspective. Previous works related to stakeholders’ negotiation mostly require stakeholders to engage in a face-to-face negotiation that seldom involves a computer system to improve the process. In this paper, we develop a negotiation system to support multi-issue and multi-stakeholder decision-making problems. In our approach, stakeholders do not directly interact with each other. Their proposals are submitted to a system that produces counter-proposals to reduce the differences among stakeholders’ proposals. Therefore, stakeholders do not exchange their preferences directly, but rather preference elicitations are mediated by the system. This approach is called computer-mediated negotiation. The system itself is based on the principle of an orthogonal strategy. Our computer-mediated negotiation protocol consists of two main phases. The first phase is the preference elicitation phase, which measures stakeholders’ utility functions. The second phase is the e-negotiation phase, in which stakeholders make their proposals and the computer system provides suggestions to improve them. To simulate real-world negotiations where stakeholders make proposals and counter-proposals in a series of negotiation rounds, we implemented the indifference curve approach to enable stakeholders to make incremental changes of their proposals during negotiation. The results from our experiment suggest that our method can produce an optimum solution for a multi-issue and multi-stakeholder decision problem by moving stakeholders’ proposals closer to one another.

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Efficient Methods for Multi-agent Multi-issue Negotiation: Allocating Resources

Cited by 14 publications

References 15 publications

Automated Negotiation for Resource Assignment in Wireless Surveillance Sensor Networks

Automated Negotiation for Resource Assignment in Wireless Surveillance Sensor Networks

From problems to protocols: Towards a negotiation handbook

Orthogonal strategy based computer-mediated negotiation: Principles and example

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