Multiagent Systems on Virtual Games: A Systematic Mapping Study

Barambones, Jose; Cano-Benito, Juan; Sanchez-Rivero, Ismael; Imbert, Ricardo; Richoux, Florian

doi:10.1109/tg.2022.3214154

Cited by 4 publications

(2 citation statements)

References 72 publications

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“…In this process, more than 30 papers with detailed environmental specifications and experiments on PCG or multi-agent training were reviewed. In addition, previous work on metastudies was also reviewed for PCG [27] taxonomies and multi-agent-based gameplaying [28], [29]. For summarizing previous works, we have included papers from only public research platforms because we are proposing an open-source game research framework.…”

Section: Related Workmentioning

confidence: 99%

RaidEnv: Exploring New Challenges in Automated Content Balancing for Boss Raid Games

Jeon,

Baek,

Bae

et al. 2024

IEEE Trans. Games

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Section: Related Workmentioning

confidence: 99%

RaidEnv: Exploring New Challenges in Automated Content Balancing for Boss Raid Games

Jeon,

Baek,

Bae

et al. 2024

IEEE Trans. Games

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“…2003; Malaschuk and Dyumin 2020; Drew 2021), e‐commerce (e.g., Pěchouček and Mařík 2008; Müller and Fischer 2014), to interactive games (e.g., Riedl, Saretto, and Young 2003; Guyot and Honiden 2006; Barambones et al. 2022), and financial fraud detection and protection (e.g., antimoney laundering Gao et al. 2006; Kumar et al.…”

Section: Introductionmentioning

confidence: 99%

Decision making in open agent systems

Eck,

Soh,

Doshi

2023

AI Magazine

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In many real‐world applications of AI, the set of actors and tasks are not constant, but instead change over time. Robots tasked with suppressing wildfires eventually run out of limited suppressant resources and need to temporarily disengage from the collaborative work in order to recharge, or they might become damaged and leave the environment permanently. In a large business organization, objectives and goals change with the market, requiring workers to adapt to perform different sets of tasks across time. We call these multiagent systems (MAS) open agent systems (OASYS), and the openness of the sets of agents and tasks necessitates new capabilities and modeling for decision making compared to planning and learning in closed environments. In this article, we discuss three notions of openness: agent openness, task openness, and type openness. We also review the past and current research on addressing the novel challenges brought about by openness in OASYS. We share lessons learned from these efforts and suggest directions for promising future work in this area. We also encourage the community to engage and participate in this area of MAS research to address critical real‐world problems in the application of AI to enhance our daily lives.

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