Coalition formation based on marginal contributions and the Markov process

Liao, Stephen Shaoyi; Zhang, Jia-Dong; Lau, Raymond Y. K.; Wu, Tianying

doi:10.1016/j.dss.2013.09.019

Cited by 10 publications

(3 citation statements)

References 31 publications

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“…Second, it yields conflict resolutions among agents because each agent thinks of its own satisfaction degree when joining any coalition. Third, it considers stability of the coalition as well as monitoring the coalition formation process explicitly (Arnold & Schwalbe, 2002;Chalkiadakis & Boutilier, 2012;Liao et al, 2014a;Liu et al, 2011). Amongst exact approaches, we find the category of other exact approaches more popular, accounting for 23.6% of the total problem-solving approaches for CSG problems.…”

Section: Macro Analysismentioning

confidence: 99%

Overlapping coalition formation in game theory: A state-of-the-art review

Mahdiraji

Razghandi

Hatami-Marbini

2021

Expert Systems with Applications

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Section: Macro Analysismentioning

confidence: 99%

Overlapping coalition formation in game theory: A state-of-the-art review

Mahdiraji

Razghandi

Hatami-Marbini

2021

Expert Systems with Applications

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“…Bayesian Reinforcement Learning (RL) allows the agents to learn the capabilities of other agents through interactions and transform repetitive coalition formation problems into sequential decision problems [21]. This approach was validated in the soccer team formation problem [22], Modeling Dynamic Robot Formation for the Area Coverage Problem Using Weighted Voting Games and Q-Learning, and Extended to Formation-Based Navigation Problems [23,24]. The formation structure is pruned using Shapley values and marginal contributions, and the transition of robots from one formation to another is represented by the Markov process, which searches for the optimal structure of the formation space using a Markov probability distribution.…”

Section: Introductionmentioning

confidence: 99%

Multi-UAV Collaborative Search and Attack Mission Decision-Making in Unknown Environments

Liang,

Li,

2023

Sensors

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To address the challenge of coordinated combat involving multiple UAVs in reconnaissance and search attacks, we propose the Multi-UAV Distributed Self-Organizing Cooperative Intelligence Surveillance and Combat (CISCS) strategy. This strategy employs distributed control to overcome issues associated with centralized control and communication difficulties. Additionally, it introduces a time-constrained formation controller to address the problem of unstable multi-UAV formations and lengthy formation times. Furthermore, a multi-task allocation algorithm is designed to tackle the issue of allocating multiple tasks to individual UAVs, enabling autonomous decision-making at the local level. The distributed self-organized multi-UAV cooperative reconnaissance and combat strategy consists of three main components. Firstly, a multi-UAV finite time formation controller allows for the rapid formation of a mission-specific formation in a finite period. Secondly, a multi-task goal assignment module generates a task sequence for each UAV, utilizing an improved distributed Ant Colony Optimization (ACO) algorithm based on Q-Learning. This module also incorporates a colony disorientation strategy to expand the search range and a search transition strategy to prevent premature convergence of the algorithm. Lastly, a UAV obstacle avoidance module considers internal collisions and provides real-time obstacle avoidance paths for multiple UAVs. In the first part, we propose a formation algorithm in finite time to enable the quick formation of multiple UAVs in a three-dimensional space. In the second part, an improved distributed ACO algorithm based on Q-Learning is introduced for task allocation and generation of task sequences. This module includes a colony disorientation strategy to expand the search range and a search transition strategy to avoid premature convergence. In the third part, a multi-task target assignment module is presented to generate task sequences for each UAV, considering internal collisions. This module provides real-time obstacle avoidance paths for multiple UAVs, preventing premature convergence of the algorithm. Finally, we verify the practicality and reliability of the strategy through simulations.

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“…Distributed intelligent control based on multi-agent system (MAS) is booming (Bristow et al, 2014;Liao et al, 2014;Loia & Vaccaro, 2014;Service & Adams, 2011a;Ye et al, 2013), which is another leap in the development of control science. The coordination and cooperation between agents is the key issue of the control.…”

Section: Introductionmentioning

confidence: 99%

Application of an Encoding Revision Algorithm in Overlapping Coalition Formation

Gui

Zhao

et al. 2021

International Journal of Cognitive Informatics and Natural Intelligence

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Overlapping coalition formation is a very active research field in multi-agent systems (MAS). In overlapping coalition, each agent can participate in different coalitions corresponding to multiple tasks at the same time. As each agent has limited resources, resource conflicts will occur. In order to resolve resource conflicts, we develop an improved encoding revision algorithm in this paper which can revise an invalid two-dimensional binary encoding into a valid one by checking the encoding for each row. To verify the effectiveness of the algorithm, differential evolution was used as the experimental platform and compared with Zhang et al. The experimental results show that the algorithm in this paper is superior to Zhang et al. in both solution quality and encoding revision time.

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Coalition formation based on marginal contributions and the Markov process

Cited by 10 publications

References 31 publications

Overlapping coalition formation in game theory: A state-of-the-art review

Overlapping coalition formation in game theory: A state-of-the-art review

Multi-UAV Collaborative Search and Attack Mission Decision-Making in Unknown Environments

Application of an Encoding Revision Algorithm in Overlapping Coalition Formation

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