Memory Mechanism Enhances Cooperation in Mobile Multi-agent System

Wang, Tao; Li, Lianyang; Zhang, Songlei; Hong, Peng; Yu, Lei; Chen, Zhigang

doi:10.1109/ihmsc.2016.275

Cited by 3 publications

(4 citation statements)

References 23 publications

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“…Of course, memory here also refers to the fact that the agent can know the choices made by other agents in the past few rounds of the game (whether or not the game was played with itself) and decide its own strategy based on these (Lotfi & Rodrigues, 2022; Wang, et al, 2016, August; Heller & Mohlin, 2018) [15,23,38] .…”

Section: Memorymentioning

confidence: 99%

“…Among the 6 studies that used Memory, there were 5 (Tao, et [10,34,38,39] applied to Network and 1 (Heller & Mohlin, 2018) [15,23] applied to IPD.…”

Section: Memorymentioning

confidence: 99%

“…Memory can increase cooperation frequency substantially (Tao, et al, 2022, February; Wang, et al, 2016, August) [34,38] , but requires the network to be as dense as possible (Fernández-Domingos, et al, 2017) [10] , because if game partners are randomly matched and difficult to match again with the same agent, cooperation cannot be induced (Heller & Mohlin, 2018) [15] .…”

Section: Memorymentioning

confidence: 99%

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Cooperation: A Systematic Review of how to Enable Agent to Circumvent the Prisoner’s Dilemma

Pan,

Yoshikawa,

Yamamura

2023

SHS Web of Conf.

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It is widely accepted that rational individuals are unable to create cooperation in a prisoner’s dilemma. However, in everyday life, cooperation, for example, during a fishing moratorium, can be observed frequently. Additionally, the appearance of cooperation in the prisoner’s dilemma can be seen in numerous simulation studies. This paper reviews 31 simulation studies published between January 2017 and January 2023 in which agents can be observed in the results to improve cooperation in a prisoner’s dilemma. The proposed methodologies were sorted into seven categories, including Bounded Rationality, Memory, Adaptive Strategy, Mood Model, Intrinsic Reward, Network Dynamics, and Altruistic Attribute. Based on their impacts, the effectiveness of these seven approaches was classified into three categories: generating cooperation, maintaining cooperation, and spreading cooperation. This review is expected to be helpful for scholars conducting future research on multi-agent cooperation and irrational agent modeling.

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Section: Memorymentioning

confidence: 99%

“…Among the 6 studies that used Memory, there were 5 (Tao, et [10,34,38,39] applied to Network and 1 (Heller & Mohlin, 2018) [15,23] applied to IPD.…”

Section: Memorymentioning

confidence: 99%

Section: Memorymentioning

confidence: 99%

See 1 more Smart Citation

Cooperation: A Systematic Review of how to Enable Agent to Circumvent the Prisoner’s Dilemma

Pan,

Yoshikawa,

Yamamura

2023

SHS Web of Conf.

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“…So, it is appropriate to use an economic model to describe the interaction between BDCs, SOs and users in CPS systems [21], [45], [46], so that the decision-makers can adopt the optimized pricing strategy to control the network interaction and achieve maximum benefit. The advantage of using the price competition model is that: before investing in a complex network, it is possible to predict when the network will reach the equilibrium point, and the optimized values of parameters of the SOs, BDCs and users under balanced conditions, such as the payment of the data samples made by SOs, the price of the services, the number of samples to be collected and the QoS indicators of services.…”

mentioning

confidence: 99%

A Game-Based Economic Model for Price Decision Making in Cyber-Physical-Social Systems

et al. 2019

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Cyber-physical-social (CPS) systems integrate Big Data Collectors (BDCs), Service Organizers (SOs) and users to build a unified data-centric computing framework. In CPS systems, BDCs leverage a vast variety of sensing devices to collect cyber-physical-social data, and report these data to SOs to orchestrate various services provided to users, thus offering a great potential for solving complex network tasks that are far beyond the capabilities of existing networks. However, due to the lack of an economic model to describe such complex data interactions, their applications are limited. So, a game-based economic model is proposed in this paper to make smart price decisions in CPS systems. Specifically, it has the following innovations: (a) The economic model gives a dynamic game income matrix which can accurately describe the revenue changes of BDCs in the game, so as to help BDCs select appropriate game parameters and strategies, and make BDCs competitive in the game. (b) The economic model can help SOs to make optimized data purchase price and service selling price based on data collection cost and competitor price analysis, so that SOs can have a better Quality of Service (QoS) and users attraction, and maximize the profit. Experimental results demonstrate that the proposed model can help BDCs and SOs find the most suitable game strategy and price adjustment principle, which has great significance in applications.INDEX TERMS Cyber-physical-social system, price decision, game theory, economic model. I. INTRODUCTIONModern networks are becoming increasingly heterogeneous, complex and dynamic, represented by Cyberphysical-social (CPS) systems [1]-[3], Cloud Computing Systems (CCS) [4]-[6] and Wireless Sensor Networks (WSN) [7]-[12]. Cyber-physical-social (CPS) system connects the cyber space, the physical space and the social space, and becomes a promising human-in-the-loop computing system, which is widely used in urban management, public safety, smart transportation, business intelligence and other fields [2], [13]-[17]. In CPS systems, the number of devices The associate editor coordinating the review of this article and approving it for publication was Shuiguang Deng. is huge, taking smartphones as an example, in 2011, the number of mobile smartphones has surpassed PCs [18], [19]. By 2016, the number of mobile users worldwide has reached 7 billion [20]-[23]. These devices for gathering data are called Big Data Collectors (BDCs) [24]-[26], which can sense and gather data from the surrounding environment and report to Service Organizers (SOs) [24], [27]-[29]. Then SOs clean, filter and mine the raw data and orchestrate these data to various services provided to users. The classic example of such applications is Vtrack [30] and Waze [31]. VTrack is a system that provides omnipresent traffic information by collecting vehicle operating information, WeatherLah gives fine-grained situation on the ground and NoiseTube makes noise maps [30]-[33]. Ubiquitous sensing

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