Advancements in Practical Applications of Agents, Multi-Agent Systems and Simulating Cognitive Mimetics

A special issue of Systems (ISSN 2079-8954). This special issue belongs to the section "Complex Systems and Cybernetics".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 4889

Special Issue Editors


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Department of Computer Science, Lille University, Cité Scientifique, 59650 Villeneuve-d’Ascq, France
Interests: artificial intelligence; multi-agent systems; individual based simulation; agent based computational economics; game theory
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Guest Editor
Departamento de Informática, Universidade do Minho, Campus of Gualtar, 4710 -057 Braga, Portugal
Interests: artificial intelligence; human–computer interaction; behavior analysis; sentiment analysis; and human action recognition
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BISITE Research Group, University of Salamanca, Edificio Multiusos I+D+I, 37007 Salamanca, Spain
Interests: artificial intelligence; multi-agent systems; cloud computing and distributed systems; technology-enhanced learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Research in cognitive mimetics combined with artificial intelligence techniques is fundamental to advancing the understanding of the human mind and the development of more advanced technologies. This can have a positive impact on people's lives by improving the efficiency of human–computer interactions and the ability of technology to solve complex problems.

The combination of different AI techniques makes it possible to create more complex and realistic systems that mimic human behaviour. Understanding these processes can have a significant impact in areas such as education, psychology, and technology. For example, multi-agent systems make it possible to simulate the interaction of several individuals and thus understand how social dynamics work. Machine learning allows models to be adjusted based on real data, improving the accuracy of imitations. Fuzzy logic allows modelling the uncertainty and subjectivity that characterise human behaviour. Expert systems offer an alternative to data-driven models to mimic human decision making in specific situations. Finally, the internet of things allows the integration of digital and physical systems to create more realistic environments for research.

In conclusion, research in cognitive mimetics combined with AI techniques is essential to improve the understanding of the human mind and human behaviour and to develop more advanced technologies. The combination of different techniques allows the creation of more complex and realistic models that simulate human behaviour, which can have a significant impact in areas such as education, psychology, and technology.

For this Special Issue, we invite researchers to submit original, high- quality studies on the cognitive mimetics systems domain, and we urge them to address its main subdisciplines, including:

  • Cognitive mimetics;
  • Simulating cognitive mimetics systems;
  • Simulation, modelling and analysis techniques;
  • Reasoning in complex systems;
  • Fuzzy logic systems;
  • Mathematical modelling;
  • Agent-based simulation;
  • Multi-agent systems (MAS);
  • Virtual agent organisations (VAO);
  • IoT and MAS;
  • CPS and MAS;
  • Ambient intelligence.

Prof. Dr. Philippe Mathieu
Dr. Dalila Durães
Dr. Alfonso González-Briones
Dr. Fernando De la Prieta Pintado
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Systems is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (4 papers)

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Research

27 pages, 3595 KiB  
Article
NEMAS: Norm Entrepreneurship in Multi-Agent Systems
by Amritha Menon Anavankot, Stephen Cranefield and Bastin Tony Roy Savarimuthu
Systems 2024, 12(6), 187; https://doi.org/10.3390/systems12060187 - 25 May 2024
Viewed by 676
Abstract
We propose a framework that integrates norm entrepreneurship from human society into the dynamics of an agent society. Most work in agent coordination in a distributed environment studies norms that are provided to agents as part of their specification or distributed from centralised [...] Read more.
We propose a framework that integrates norm entrepreneurship from human society into the dynamics of an agent society. Most work in agent coordination in a distributed environment studies norms that are provided to agents as part of their specification or distributed from centralised agents. Exploring an alternate perspective, we focus on peer-to-peer interaction by providing the agents with the freedom to initiate norm creation in demanding situations like a potential interference. This paper explores the concept of norm entrepreneurship through proactive establishment and emergence of norms by agents. A common approach in prior work focuses on coordination problems that are reduced to simple game theory models involving the simultaneous performance of a single action by each agent. Instead, we define the concept of a local coordination plan (LCP), which is a sequence of actions from each agent to cope with an interference in their normal course of action. We identify LCPs across various scenarios and abstract these plans using coordination state machines (CSMs). A coordination state machine contains a separate state machine for each agent where the states encapsulate the potentially constrained and suboptimal movement options agents have at a given time. We also explore how multiple LCPs lead to a coordination state machine of the same format and how a coordination state machine can abstract across multiple scenarios. Full article
27 pages, 1308 KiB  
Article
Integration of Efficient Techniques Based on Endpoints in Solution Method for Lifelong Multiagent Pickup and Delivery Problem
by Toshihiro Matsui
Systems 2024, 12(4), 112; https://doi.org/10.3390/systems12040112 - 27 Mar 2024
Viewed by 919
Abstract
We investigate the integration of several additional efficient techniques that improve a solution method for the lifelong multiagent pickup-and-delivery (MAPD) problem to reduce the redundancy in the concurrent task execution and space usage of a warehouse map. The lifelong MAPD problem is an [...] Read more.
We investigate the integration of several additional efficient techniques that improve a solution method for the lifelong multiagent pickup-and-delivery (MAPD) problem to reduce the redundancy in the concurrent task execution and space usage of a warehouse map. The lifelong MAPD problem is an extended class of iterative multiagent pathfinding problems where a set of shortest collision-free travel paths of multiple agents is iteratively planned. This problem models a system in automated warehouses with robot-carrier agents that are allocated to pickup-and-delivery tasks generated on demand. In the task allocation to agents, several solution methods for lifelong MAPD problems consider the endpoints of the agents’ travel paths to avoid the deadlock situations among the paths due to the conflict of the endpoints. Since redundancies are found in the problem settings themselves and the concurrency of allocated tasks, several additional techniques have been proposed to reduce them in solution methods. However, there should be opportunities to investigate the integration of additional techniques with improvements for more practical solution methods. As analysis and an improved understanding of the additional solution techniques based on endpoints, we incrementally integrate the techniques and experimentally investigate their contributions to the quality of task allocation and the paths of the agents. Our result reveals significant complementary effects of the additionally integrated techniques and trade-offs among them in several different problem settings. Full article
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24 pages, 1423 KiB  
Article
Impact of Static Urban Traffic Flow-Based Traffic Weighted Multi-Maps Routing Strategies on Pollutant Emissions
by Alvaro Paricio-Garcia and Miguel A. Lopez-Carmona
Systems 2024, 12(3), 89; https://doi.org/10.3390/systems12030089 - 12 Mar 2024
Viewed by 1073
Abstract
Addressing urban traffic congestion is a pressing issue requiring efficient solutions that need to be analyzed regarding travel time and pollutant emissions. The traffic weighted multi-maps (TWM) method has been proposed as an efficient mechanism for congestion mitigation that enables differential traffic routing [...] Read more.
Addressing urban traffic congestion is a pressing issue requiring efficient solutions that need to be analyzed regarding travel time and pollutant emissions. The traffic weighted multi-maps (TWM) method has been proposed as an efficient mechanism for congestion mitigation that enables differential traffic routing and path diversity by strategically distributing different network views (maps) to the drivers. Previous works have focused on TWM generation by creating optimal edge weights, but the complexity exponentially increases with the network size and traffic group diversity. This work describes how congestion and emissions can be addressed using TWM maps based on the k-shortest paths for the traffic flows (instead of individuals) that are optimally assigned and distributed to the components of the traffic flow. The map allocation strategies optimal TWM (OTV), optimal TWM per path flow with linear constraints (LCTV), and its variant unconstrained optimal TWM per path flow (UCTV) are described. They use maps generated from the k-shortest paths of the traffic flows (kSP-TWM). The heuristic solution obtained is compared with the theoretical static traffic assignment estimation baseline with different configurations, regarding congestion reduction, total travel time enhancement, and pollutant emissions. Experiments are developed using a synthetic traffic grid network scenario with a mesoscopic simulation. They show that the solution provided is adequate for its proximity to the theoretical equilibrium solutions and can generate minimum emissions patterns. The presented solution opens new possibilities for further congestion and pollutant management studies and seamless integration with existing traffic management frameworks. Full article
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16 pages, 1243 KiB  
Article
Towards an Affective Intelligent Agent Model for Extrinsic Emotion Regulation
by Aaron Pico, Joaquin Taverner, Emilio Vivancos, Vicente Botti and Ana García-Fornes
Systems 2024, 12(3), 77; https://doi.org/10.3390/systems12030077 - 28 Feb 2024
Cited by 1 | Viewed by 1455
Abstract
Emotion regulation is the human ability to modulate one’s or other emotions to maintain emotional well-being. Despite its importance, only a few computational models have been proposed for facilitating emotion regulation. None of them prepare a plan of all the actions necessary for [...] Read more.
Emotion regulation is the human ability to modulate one’s or other emotions to maintain emotional well-being. Despite its importance, only a few computational models have been proposed for facilitating emotion regulation. None of them prepare a plan of all the actions necessary for emotion regulation customized to the needs of a specific individual. To address this gap, we propose a computational model for an intelligent agent which, grounded in a multidimensional emotion representation, facilitates emotion regulation in individuals. This computational model is based on J. Gross’s theoretical framework of emotion regulation. An intelligent agent selects the most appropriate regulation strategy to maintain an individual’s emotional equilibrium considering the individual’s personality traits. A dynamic planner prepares a plan of emotion regulation actions which is dynamically adapted according to the emotional changes observed in the individual after applying the previous emotion regulation actions. This refinement of the initial regulatory action plan allows the proposed emotion regulation agent to adapt the plan to the specific characteristics of the individual, facilitating the individual to improve their emotion regulation capabilities and improve their emotional health. Full article
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