Collective Voice of Experts in Multilateral Negotiation

Güneş, Taha D.; Arditi, Emir; Aydoğan, Reyhan

doi:10.1007/978-3-319-69131-2_27

Cited by 7 publications

(4 citation statements)

References 18 publications

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“…Lastly, combining multiple learners may improve the performance of the predictions as seen in [35,36]. As future work, we would like to introduce multiple learners for the underlying problem and combine them to obtain higher precision and recall.…”

Section: Discussionmentioning

confidence: 99%

Using Convolutional Neural Networks to Automate Aircraft Maintenance Visual Inspection

Doğru

Bouarfa²,

Arizar³

et al. 2020

Aerospace

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Convolutional Neural Networks combined with autonomous drones are increasingly seen as enablers of partially automating the aircraft maintenance visual inspection process. Such an innovative concept can have a significant impact on aircraft operations. Though supporting aircraft maintenance engineers detect and classify a wide range of defects, the time spent on inspection can significantly be reduced. Examples of defects that can be automatically detected include aircraft dents, paint defects, cracks and holes, and lightning strike damage. Additionally, this concept could also increase the accuracy of damage detection and reduce the number of aircraft inspection incidents related to human factors like fatigue and time pressure. In our previous work, we have applied a recent Convolutional Neural Network architecture known by MASK R-CNN to detect aircraft dents. MASK-RCNN was chosen because it enables the detection of multiple objects in an image while simultaneously generating a segmentation mask for each instance. The previously obtained F1 and F2 scores were 62.67% and 59.35%, respectively. This paper extends the previous work by applying different techniques to improve and evaluate prediction performance experimentally. The approach uses include (1) Balancing the original dataset by adding images without dents; (2) Increasing data homogeneity by focusing on wing images only; (3) Exploring the potential of three augmentation techniques in improving model performance namely flipping, rotating, and blurring; and (4) using a pre-classifier in combination with MASK R-CNN. The results show that a hybrid approach combining MASK R-CNN and augmentation techniques leads to an improved performance with an F1 score of (67.50%) and F2 score of (66.37%).

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Section: Discussionmentioning

confidence: 99%

Using Convolutional Neural Networks to Automate Aircraft Maintenance Visual Inspection

Doğru

Bouarfa²,

Arizar³

et al. 2020

Aerospace

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“…Proposing another agent that is supported by a variety of negotiation strategies [53], the authors presented a multi-party negotiation agent for which the decisions (e.g., bidding, acceptance, etc.) are built by aggregating the decisions of multiple winning agents from previous ANAC competitions.…”

Section: Related Workmentioning

confidence: 99%

“…are built by aggregating the decisions of multiple winning agents from previous ANAC competitions. While our agent is also supported by several strategies, the behavior of the agent proposed in [53] does not adapt according to the negotiation context, as the aggregation rules remain the same. In addition, the objective of our agent is that of being social, while the work presented in Güneş et al [53] solely strives for utility maximization.…”

Section: Related Workmentioning

confidence: 99%

Can Social Agents Efficiently Perform in Automated Negotiation?

Sánchez-Anguix

Tunalı²,

Aydoğan

et al. 2021

Applied Sciences

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In the last few years, we witnessed a growing body of literature about automated negotiation. Mainly, negotiating agents are either purely self-driven by maximizing their utility function or by assuming a cooperative stance by all parties involved in the negotiation. We argue that, while optimizing one’s utility function is essential, agents in a society should not ignore the opponent’s utility in the final agreement to improve the agent’s long-term perspectives in the system. This article aims to show whether it is possible to design a social agent (i.e., one that aims to optimize both sides’ utility functions) while performing efficiently in an agent society. Accordingly, we propose a social agent supported by a portfolio of strategies, a novel tit-for-tat concession mechanism, and a frequency-based opponent modeling mechanism capable of adapting its behavior according to the opponent’s behavior and the state of the negotiation. The results show that the proposed social agent not only maximizes social metrics such as the distance to the Nash bargaining point or the Kalai point but also is shown to be a pure and mixed equilibrium strategy in some realistic agent societies.

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“…-CaduceusDC16 by Taha Gunes and Emir Arditi: CaduceusDC16 extends the Caduceus agent [20], which inspired from the ideas such as "algorithm portfolio", "mixture of experts", and "genetic algorithm". Simply, this agent asks the expert agents' opinion on whether to make a counter offer or to accept the given offer as well as what to bid.…”

Section: Individual Utility Categorymentioning

confidence: 99%

ANAC 2017: Repeated Multilateral Negotiation League

Aydoğan

Fujita

Baarslag

et al. 2020

Advances in Automated Negotiations

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The Automated Negotiating Agents Competition (ANAC) is annually organized competition to facilitate the research on automated negotiation. This paper presents the ANAC 2017 Repeated Multilateral Negotiation League. As human negotiators do, agents are supposed to learn from their previous negotiations and improve their negotiation skills over time. Especially, when they negotiate with the same opponent on the same domain, they can adopt their negotiation strategy according to their past experiences. They can adjust their acceptance threshold or bidding strategy accordingly. In ANAC 2017, participants aimed to develop such a negotiating agent. Accordingly, this paper describes the competition settings and results with a brief description of the winner negotiation strategies.

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Collective Voice of Experts in Multilateral Negotiation

Cited by 7 publications

References 18 publications

Using Convolutional Neural Networks to Automate Aircraft Maintenance Visual Inspection

Using Convolutional Neural Networks to Automate Aircraft Maintenance Visual Inspection

Can Social Agents Efficiently Perform in Automated Negotiation?

ANAC 2017: Repeated Multilateral Negotiation League

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