Cooperative Decision Making for Diagnosis of Complex System based on Game Theory: Survey and an Alternative Scheme

Author(s):  
Pan-jing Li ◽  
Xian-sheng Qin ◽  
Kondo Adjallah ◽  
Benoit Eynard ◽  
Jay Lee
2021 ◽  
Author(s):  
Simon Rothfuß ◽  
Maximilian Wörner ◽  
Jairo Inga ◽  
Andrea Kiesel ◽  
Sören Hohmann

<div>The experiment reported in this paper provides a first experimental evaluation of human-machine cooperation on decision level: It explicitly focuses on the interaction of human and machine in cooperative decision making situations for which a suitable experimental design is introduced. Furthermore, it challenges conventional leader-follower approaches by comparing them to newly proposed automation designs based on cooperative decision making models. These models originate from negotiation theory and game theory and allow for an investigation of cooperative decision making between equal partners. This equality is motivated by similar approaches on the action level of human-machine cooperation. <br></div><div>The experiment’s results indicate an added value of the proposed automation designs in terms of objective cooperative performance as well as human trust in and satisfaction with the cooperation. Hence, the experiment yields the same insight on decision level as already observed on action level: it may be beneficial to design machines as equal cooperation partners and in accordance to models of emancipated human-machine cooperation.</div>


2021 ◽  
Author(s):  
Simon Rothfuß ◽  
Maximilian Wörner ◽  
Jairo Inga ◽  
Andrea Kiesel ◽  
Sören Hohmann

<div>The experiment reported in this paper provides a first experimental evaluation of human-machine cooperation on decision level: It explicitly focuses on the interaction of human and machine in cooperative decision making situations for which a suitable experimental design is introduced. Furthermore, it challenges conventional leader-follower approaches by comparing them to newly proposed automation designs based on cooperative decision making models. These models originate from negotiation theory and game theory and allow for an investigation of cooperative decision making between equal partners. This equality is motivated by similar approaches on the action level of human-machine cooperation. <br></div><div>The experiment’s results indicate an added value of the proposed automation designs in terms of objective cooperative performance as well as human trust in and satisfaction with the cooperation. Hence, the experiment yields the same insight on decision level as already observed on action level: it may be beneficial to design machines as equal cooperation partners and in accordance to models of emancipated human-machine cooperation.</div>


Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1523
Author(s):  
Nikita Smirnov ◽  
Yuzhou Liu ◽  
Aso Validi ◽  
Walter Morales-Alvarez ◽  
Cristina Olaverri-Monreal

Autonomous vehicles are expected to display human-like behavior, at least to the extent that their decisions can be intuitively understood by other road users. If this is not the case, the coexistence of manual and autonomous vehicles in a mixed environment might affect road user interactions negatively and might jeopardize road safety. To this end, it is highly important to design algorithms that are capable of analyzing human decision-making processes and of reproducing them. In this context, lane-change maneuvers have been studied extensively. However, not all potential scenarios have been considered, since most works have focused on highway rather than urban scenarios. We contribute to the field of research by investigating a particular urban traffic scenario in which an autonomous vehicle needs to determine the level of cooperation of the vehicles in the adjacent lane in order to proceed with a lane change. To this end, we present a game theory-based decision-making model for lane changing in congested urban intersections. The model takes as input driving-related parameters related to vehicles in the intersection before they come to a complete stop. We validated the model by relying on the Co-AutoSim simulator. We compared the prediction model outcomes with actual participant decisions, i.e., whether they allowed the autonomous vehicle to drive in front of them. The results are promising, with the prediction accuracy being 100% in all of the cases in which the participants allowed the lane change and 83.3% in the other cases. The false predictions were due to delays in resuming driving after the traffic light turned green.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shion Maeda ◽  
Nicolas Chauvet ◽  
Hayato Saigo ◽  
Hirokazu Hori ◽  
Guillaume Bachelier ◽  
...  

AbstractCollective decision making is important for maximizing total benefits while preserving equality among individuals in the competitive multi-armed bandit (CMAB) problem, wherein multiple players try to gain higher rewards from multiple slot machines. The CMAB problem represents an essential aspect of applications such as resource management in social infrastructure. In a previous study, we theoretically and experimentally demonstrated that entangled photons can physically resolve the difficulty of the CMAB problem. This decision-making strategy completely avoids decision conflicts while ensuring equality. However, decision conflicts can sometimes be beneficial if they yield greater rewards than non-conflicting decisions, indicating that greedy actions may provide positive effects depending on the given environment. In this study, we demonstrate a mixed strategy of entangled- and correlated-photon-based decision-making so that total rewards can be enhanced when compared to the entangled-photon-only decision strategy. We show that an optimal mixture of entangled- and correlated-photon-based strategies exists depending on the dynamics of the reward environment as well as the difficulty of the given problem. This study paves the way for utilizing both quantum and classical aspects of photons in a mixed manner for decision making and provides yet another example of the supremacy of mixed strategies known in game theory, especially in evolutionary game theory.


2014 ◽  
Vol 986-987 ◽  
pp. 400-403 ◽  
Author(s):  
Kuan Lu ◽  
Wen Shan Gao ◽  
Jing Li ◽  
Wan Lei Xue ◽  
Wen Xue Sun

First, behavior models of the station operator and consumer are established considering all the main factors. Second, an optimized EV charging pricing method using Game Theory is introduced based on benefit-cost analysis. The calculation of government subsidies is also proposed in order to reach an equilibrium charging price. Finally, actual data of Hebei is used and results are shown to give supports to local government and charging station operator’s decision making.


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