Analysis of task allocation based on social utility and incompatible individual preference

Iijima, Naoki; Hayano, Masashi; Sugiyama, Ayumi; Sugawara, Toshiharu

doi:10.1109/taai.2016.7880161

Cited by 4 publications

(2 citation statements)

References 16 publications

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“…Saito k et al allocated resources based on multiple bid declaration with preference [37]. Iijima, Naoki, et al analyzed task allocation based on social utility and incompatible individual preference [17]. However, fuzzy and qualitative approaches are rarely adopted compared to cardinal preference structure and ordinal preferences in MARA [9,22].…”

Section: Introductionmentioning

confidence: 99%

Preference-based resource reservation method for resource allocation in full distributed systems

Wang¹,

Zgaya-Biau²,

Mathieu³

et al. 2020

MGS

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Multiagent Resource Allocation (MARA) is a field to find out solutions that distribute a set of resources among agents. Heretofore, lots of utility-and reputationbased approaches have been proposed. In this paper, we develop a preference-based resource reservation approach for resource allocation in a system that consists of entirely selfless agents. Preference, defined as the satisfaction of required resources, has a crucial impact on resource allocation. The importance degree is also adopted to represent the extent to which a particular attribute is needed. In the system, agents communicate with semi-local friends to exchange information as well as reserving resources. Finally, a confirmation or cancelation message is sent to obtain or release the reserved resources. Numerical examples are given to show the rationality and effectiveness of the proposed method.

show abstract

Section: Introductionmentioning

confidence: 99%

Preference-based resource reservation method for resource allocation in full distributed systems

Wang¹,

Zgaya-Biau²,

Mathieu³

et al. 2020

MGS

View full text Add to dashboard Cite

show abstract

“…There is some research on multi-UAV task allocation problems under uncertainty. A lot of task allocation problem models [ 5 , 9 ] and task assignment solving algorithms [ 10 , 11 , 12 , 13 , 14 , 15 ] have been developed to meet the respective needs of various situations. Some intelligent methods have been proposed for multi-UAV task allocation problem under uncertain situation [ 7 , 8 , 16 , 17 , 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Sampling-Based Algorithms Using Active Learning and Manifold Learning for Multiple Unmanned Aerial Vehicle Task Allocation under Uncertainty

Wang

Li³

et al. 2018

Sensors

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This paper presents a sampling-based approximation for multiple unmanned aerial vehicle (UAV) task allocation under uncertainty. Our goal is to reduce the amount of calculations and improve the accuracy of the algorithm. For this purpose, Gaussian process regression models are constructed from an uncertainty parameter and task reward sample set, and this training set is iteratively refined by active learning and manifold learning. Firstly, a manifold learning method is used to screen samples, and a sparse graph is constructed to represent the distribution of all samples through a small number of samples. Then, multi-points sampling is introduced into the active learning method to obtain the training set from the sparse graph quickly and efficiently. This proposed hybrid sampling strategy could select a limited number of representative samples to construct the training set. Simulation analyses demonstrate that our sampling-based algorithm can effectively get a high-precision evaluation model of the impact of uncertain parameters on task reward.

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Adaptive Task Allocation Based on Social Utility and Individual Preference in Distributed Environments

Iijima

Sugiyama

Hayano

et al. 2017

Procedia Computer Science

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Analysis of task allocation based on social utility and incompatible individual preference

Cited by 4 publications

References 16 publications

Preference-based resource reservation method for resource allocation in full distributed systems

Preference-based resource reservation method for resource allocation in full distributed systems

An Efficient Sampling-Based Algorithms Using Active Learning and Manifold Learning for Multiple Unmanned Aerial Vehicle Task Allocation under Uncertainty

Adaptive Task Allocation Based on Social Utility and Individual Preference in Distributed Environments

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