Special Issue “Multi-Agent Systems”: Editorial

Mariani, Stefano; Omicini, Andrea

doi:10.3390/app9050954

Cited by 3 publications

(3 citation statements)

References 31 publications

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“…Before delving into the individual contributions gathered, a few general statistics and observations are useful to have an overview of the content and outreach of this special issue: These numbers are in line with previous edition of the special issue [2], except for a lower acceptance rate, which reflects the more selective review process meant to increase the quality of the special issue and its potential impact on research and practice.…”

Section: Overviewmentioning

confidence: 88%

Special Issue “Multi-Agent Systems”: Editorial

Mariani

Omicini

2020

Applied Sciences

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Multi-agent systems (MAS) are built around the central notions of agents, interaction, and environment. Agents are autonomous computational entities able to pro-actively pursue goals, and re-actively adapt to environment change. In doing so, they leverage on their social and situated capabilities: interacting with peers, and perceiving/acting on the environment. The relevance of MAS is steadily growing as they are extensively and increasingly used to model, simulate, and build heterogeneous systems across many different application scenarios and business domains, ranging from logistics to social sciences, from robotics to supply chain, and more. The reason behind such a widespread and diverse adoption lies in MAS great expressive power in modeling and actually supporting operational execution of a variety of systems demanding decentralized computations, reasoning skills, and adaptiveness to change, which are a perfect fit for MAS central notions introduced above. This special issue gathers 11 contributions sampling the many diverse advancements that are currently ongoing in the MAS field.

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Section: Overviewmentioning

confidence: 88%

Special Issue “Multi-Agent Systems”: Editorial

Mariani

Omicini

2020

Applied Sciences

Self Cite

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“…To analyze the behaviors of rickshaws that suggest tourist behaviors, we performed agent-based simulations. Agent-based models fundamentally predict probabilistic processes with no memory effects [28][29][30][31][32]; random selections with specific probabilities determine the agent's next destination. This indicates that numerical results derived by agent-based simulations provide random motions without any biases that might arise from such preset knowledge as recommendations by external information.…”

Section: Predictions By Agent-based Model and Discussionmentioning

confidence: 99%

“…We analyze the data of their traces on a cloud server with the graphical approximation of rickshaw routes using networking or graph representation and simulate rickshaw motions in cyberspace with similar conditions to a given area using an agent-based model. Agent-based models are widely used to simulate movable objects in hypothetical situations [28][29][30][31][32]. For instance, multi-agent simulations can predict epidemic dissemination in a given social space from a local area to an international region, where an agent corresponds to one person [28].…”

Section: Introductionmentioning

confidence: 99%

Mobilities in Network Topology and Simulation Reproducibility of Sightseeing Vehicle Detected by Low-Power Wide-Area Positioning System

et al. 2020

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Vehicle mobilities for passengers in a city’s downtown area or in the countryside are significant points to characterize their functions and outputs. We focus on commercial sightseeing vehicles in a Japanese city where many tourists enjoy sightseeing. Such mobilities and their visualizations make tourist activities smoother and richer. We design and install a low-power, wide-area positioning system on a rickshaw, which is a human-pulled, two- or three-wheeled cart, and monitor its mobility in Hikone City. All the spatial locations, which are recorded in a time sequence on a cloud server, are currently available as open data on the internet. We analyze such sequential data using graph topology, which reflects the information of corresponding geographical maps, and reproduce it in cyberspace using an agent-based model with similar probabilities to the accumulated rickshaw records from one spatial node to another. Although the numerical results of the agent traced in a simulated city are partially consistent with the rickshaw’s record, we identify some significant differences. We conclude that the rickshaw’s mobility observed at the actual sightseeing sites is partially in the random motion; some cases are strongly biased by memory routes. Such non-randomness in the rickshaw’s mobility indicates the existence of specific features in tourism sources that are identified for each sightseeing activity and affected by local sightseeing resources.

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