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Multi-Agent Systems
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Multi-Agent Systems

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 32396

Special Issue Editors

Prof. Dr. Vicent Botti

E-Mail Website
Guest Editor
VRAIN Valencian Research Institute for Artificial Intelligence, Universitat Politècnica de València, 46022 València, Spain
Interests: affective computing; agreement technology; artificial intelligence; computational chemistry; computer science
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Vicente Julian

E-Mail Website
Guest Editor
GTI-IA Research Group, Deputy Director of Research in the Department of Computer Systems and Computation at Universitat Politècnica de València, Valencia, Spain
Interests: multi-agent systems; agreement technologies; Ambient Intelligence; affective computing; intelligent transport systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Research done on intelligent agents and multi-agent systems has matured during the last decade, and many effective applications of this technology are being deployed. Despite the fact that computational approaches for multi-agent systems have mainly emerged in recent decades, scholars have been prolific with the variety of methods proposed to solve this paradigm. Different communities have emerged with multi-agent systems as their main research topic.

Multi-agent systems allow the development of distributed and intelligent applications in complex and dynamic environments. Systems of this kind play a crucial role in life, evidenced by the broad range of applied areas involved in their use, including manufacturing, management sciences, e-commerce, biotechnology, etc.

The interest of researchers in this new discipline lies in diverse reasons. Firstly, computational systems have gradually shifted toward a distributed paradigm where heterogeneous entities with different goals can enter and leave the system dynamically and interact with each other. Secondly, new computational systems should be able to negotiate with one another, typically on behalf of humans, in order to come to mutually acceptable agreements. As a consequence, autonomy, interaction, mobility, and openness are key concepts studied in the area.

The purpose of this Special Issue is to make known some of the advances made in this paradigm and try to show the current state of this technology by analyzing different aspects as well as its possible application to various domains. In this review of the current state, we do not intend to exhaustively explore all the current existing works but rather give an overview of the research in agent technology, showing the high level of activity in this area.

Prof. Dr. Vicent Botti
Prof. Dr. Vicente Julian
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. Applied Sciences is an international peer-reviewed open access semimonthly 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.

Keywords

  • Agent engineering: development techniques, tools, and platforms
  • Agent-based simulation
  • Biologically-inspired approaches and methods
  • Collective intelligence
  • Complex systems
  • Distributed problem solving
  • Human–robot–agent interaction
  • Intelligent control and manufacturing systems
  • Learning and adaptation in MAS
  • Methodologies for agent-based systems
  • Multi-robot systems
  • Negotiation and conflict resolution
  • Normative systems
  • Organizations and institutions
  • Reasoning in agent-based systems
  • Self-organization
  • Single and multi-agent planning and scheduling
  • Socio-technical systems
  • Teamwork, team formation, and teamwork analysis
  • Trust and reputation
  • Agent and multi-agent applications
  • IoT and MAS
  • CPS and MAS
  • Ambient Intelligence and MAS
  • Smart Cities and MAS
  • Industry 4.0 and MAS
  • eHealth and MAS

Published Papers (15 papers)

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Research

22 pages, 1125 KiB  
Article
Boosting Deep Reinforcement Learning Agents with Generative Data Augmentation
by Tasos Papagiannis, Georgios Alexandridis and Andreas Stafylopatis
Appl. Sci. 2024, 14(1), 330; https://doi.org/10.3390/app14010330 - 29 Dec 2023
Viewed by 1222
Abstract
Data augmentation is a promising technique in improving exploration and convergence speed in deep reinforcement learning methodologies. In this work, we propose a data augmentation framework based on generative models for creating completely novel states and increasing diversity. For this purpose, a diffusion [...] Read more.
Data augmentation is a promising technique in improving exploration and convergence speed in deep reinforcement learning methodologies. In this work, we propose a data augmentation framework based on generative models for creating completely novel states and increasing diversity. For this purpose, a diffusion model is used to generate artificial states (learning the distribution of original, collected states), while an additional model is trained to predict the action executed between two consecutive states. These models are combined to create synthetic data for cases of high and low immediate rewards, which are encountered less frequently during the agent’s interaction with the environment. During the training process, the synthetic samples are mixed with the actually observed data in order to speed up agent learning. The proposed methodology is tested on the Atari 2600 framework, producing realistic and diverse synthetic data which improve training in most cases. Specifically, the agent is evaluated on three heterogeneous games, achieving a reward increase of up to 31%, although the results indicate performance variance among the different environments. The augmentation models are independent of the learning process and can be integrated to different algorithms, as well as different environments, with slight adaptations. Full article
(This article belongs to the Special Issue Multi-Agent Systems)
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24 pages, 14560 KiB  
Article
Manufacturer Channel Encroachment and Evolution in E-Platform Supply Chain: An Agent-Based Model
by Rong Ma and Tianjian Yang
Appl. Sci. 2023, 13(5), 3060; https://doi.org/10.3390/app13053060 - 27 Feb 2023
Viewed by 1388
Abstract
Manufacturer channel encroachment is a common phenomenon in the current e-commerce supply chain, which has been well studied. This study develops a multi-agent-based model of the e-platform supply chain to analyse manufacturers’ channel encroachment strategies and supply chain evolution. Through both direct sales [...] Read more.
Manufacturer channel encroachment is a common phenomenon in the current e-commerce supply chain, which has been well studied. This study develops a multi-agent-based model of the e-platform supply chain to analyse manufacturers’ channel encroachment strategies and supply chain evolution. Through both direct sales channels and e-commerce platforms, manufacturers can sell two complementary products of varying quality. Consumers who have preferences compare the pricing information gathered from manufacturers through different channels before selecting the one that generates the best utility. At the end of each period, the manufacturers make a price adjustment using the genetic algorithm. We look at the supply chain evolution process through multi-period simulations and discuss the factors that influence encroachment decisions. We find that manufacturers’ channel encroachment is detrimental to the profitability of the e-commerce platform. Consumers’ channel preferences and quality preferences benefit the e-commerce platform and can discourage manufacturers’ encroachment decisions. In addition, increases in encroachment costs and commission rates can reduce manufacturers’ propensity for channel encroachment. Full article
(This article belongs to the Special Issue Multi-Agent Systems)
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25 pages, 1582 KiB  
Article
Decoupled Monte Carlo Tree Search for Cooperative Multi-Agent Planning
by Okan Asik, Fatma Başak Aydemir and Hüseyin Levent Akın
Appl. Sci. 2023, 13(3), 1936; https://doi.org/10.3390/app13031936 - 02 Feb 2023
Viewed by 1915
Abstract
The number of agents exponentially increases the complexity of a cooperative multi-agent planning problem. Decoupled planning is one of the viable approaches to reduce this complexity. By integrating decoupled planning with Monte Carlo Tree Search, we present a new scalable planning approach. The [...] Read more.
The number of agents exponentially increases the complexity of a cooperative multi-agent planning problem. Decoupled planning is one of the viable approaches to reduce this complexity. By integrating decoupled planning with Monte Carlo Tree Search, we present a new scalable planning approach. The search tree maintains the updates of the individual actions of each agent separately. However, this separation brings coordination and action synchronization problems. When the agent does not know the action of the other agent, it uses the returned reward to deduce the desirability of its action. When a deterministic action selection policy is used in the Monte Carlo Tree Search algorithm, the actions of agents are synchronized. Of all possible action combinations, only some of them are evaluated. We show the effect of action synchronization on different problems and propose stochastic action selection policies. We also propose a combined method as a pruning step in centralized planning to address the coordination problem in decoupled planning. We create a centralized search tree with a subset of joint actions selected by the evaluation of decoupled planning. We empirically show that decoupled planning has a similar performance compared to a central planning algorithm when stochastic action selection is used in repeated matrix games and multi-agent planning problems. We also show that the combined method improves the performance of the decoupled method in different problems. We compare the proposed method to a decoupled method in regard to a warehouse commissioning problem. Our method achieved more than 10% improvement in performance. Full article
(This article belongs to the Special Issue Multi-Agent Systems)
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14 pages, 1343 KiB  
Article
Extensible Hierarchical Multi-Agent Reinforcement-Learning Algorithm in Traffic Signal Control
by Pengqian Zhao, Yuyu Yuan and Ting Guo
Appl. Sci. 2022, 12(24), 12783; https://doi.org/10.3390/app122412783 - 13 Dec 2022
Viewed by 1714
Abstract
Reinforcement-learning (RL) algorithms have made great achievements in many scenarios. However, in large-scale traffic signal control (TSC) scenarios, RL still falls into local optima when controlling multiple signal lights. To solve this problem, we propose a novel goal-based multi-agent hierarchical model (GMHM). Specifically, [...] Read more.
Reinforcement-learning (RL) algorithms have made great achievements in many scenarios. However, in large-scale traffic signal control (TSC) scenarios, RL still falls into local optima when controlling multiple signal lights. To solve this problem, we propose a novel goal-based multi-agent hierarchical model (GMHM). Specifically, we divide the traffic environment into several regions. The region contains a virtual manager and several workers who control the traffic lights. The manager assigns goals to each worker by observing the environment, and the worker makes decisions according to the environment state and the goal. For the worker, we adapted the goal-based multi-agent deep deterministic policy gradient (MADDPG) algorithm combined with hierarchical reinforcement learning. In this way, we simplify tasks and allow agents to cooperate more efficiently. We carried out experiments on both grid traffic scenarios and real-world scenarios in the SUMO simulator. The experimental results show the performance advantages of our algorithm compared with state-of-the-art algorithms. Full article
(This article belongs to the Special Issue Multi-Agent Systems)
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30 pages, 2055 KiB  
Article
CPN4M: Testing Multi-Agent Systems under Organizational Model Moise+ Using Colored Petri Nets
by Eder Mateus Nunes Gonçalves, Ricardo Arend Machado, Bruno Coelho Rodrigues and Diana Adamatti
Appl. Sci. 2022, 12(12), 5857; https://doi.org/10.3390/app12125857 - 09 Jun 2022
Cited by 3 | Viewed by 1747
Abstract
Multi-agent systems (MASs) are distributed and complex software that demand specific software engineering features. Testing is a critical phase when validating software, and it is also difficult to conceive and execute. Designing systems under a multi-agent paradigm is even more difficult because of [...] Read more.
Multi-agent systems (MASs) are distributed and complex software that demand specific software engineering features. Testing is a critical phase when validating software, and it is also difficult to conceive and execute. Designing systems under a multi-agent paradigm is even more difficult because of properties such as autonomy, reactivity, pro-activity, and social skills. Any multi-agent system has at least three main dimensions: the individual and social levels and communication interfaces. Considering an approach for testing a dimension specifically, we deal with the social level as an organizational model in this paper. It imposes some restrictions on agents’ behavior through a set of behavioral constraints. In this sense, an error in the organization can occur when the allocated resources are not enough for executing plans and reaching goals. This work aims to present a whole framework for analyzing and testing MAS social level under organizational model Moise+. This framework uses a Moise+ specifications set as information artifact mapped in a colored Petri net (CPN) model, named CPN4M, as a test case generation mechanism. CPN4M uses two different test adequacy criteria: all-paths and state-transition path. In this paper, we have formalized the transition from Moise+ to CPN, the procedures for test case generation, and executed some tests in a case study. The results indicate that this methodology can increase the correction degree for a social level in a multi-agent system specified by a Moise+ model, indicating the system context that can lead the MAS for failures. Full article
(This article belongs to the Special Issue Multi-Agent Systems)
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25 pages, 9703 KiB  
Article
Emergent Search of UAV Swarm Guided by the Target Probability Map
by Shengyang Liu, Wen Yao, Xiaozhou Zhu, Yuan Zuo and Bin Zhou
Appl. Sci. 2022, 12(10), 5086; https://doi.org/10.3390/app12105086 - 18 May 2022
Cited by 4 | Viewed by 1343
Abstract
In the cooperative searching scenario, most traditional methods are based on the top–down mechanisms. These mechanisms are usually offline and centralized. The characteristics limit the adaptability of unmanned aerial vehicle (UAV) swarm to the complex mission environments, such as those with inaccurate information [...] Read more.
In the cooperative searching scenario, most traditional methods are based on the top–down mechanisms. These mechanisms are usually offline and centralized. The characteristics limit the adaptability of unmanned aerial vehicle (UAV) swarm to the complex mission environments, such as those with inaccurate information of the targets and grids. In order to improve the searching ability of UAV swarm, a novel searching method named emergent search of UAV swarm guided by the target probability map (ESUSTPM) is proposed. ESUSTPM is based on local rules to organize and guide UAV agents to achieve the flocking state, search the mission area and detect the hidden targets concurrently. In ESUSTPM, local rules contain the flocking rules and the guiding rules. The flocking rules are the interactions between the agents, which are designed by a novel constructed function based on two exponential functions in this paper. The new constructed function can better maintain the relatively stable distances between the agents and realize the smooth transition of the positions at the given centers. The local guiding rules based on the target probability information of the nearby grids are firstly designed to realize the multi-function of the swarm, including full area coverage, target detection and reduction in environmental uncertainty (EU). Finally, the simulations verify that ESUSTPM can achieve the full coverage of the mission area while taking into account the target search. The statistical results also indicate that the searching efficiency of the proposed ESUSTPM is higher than the traditional searching algorithms based on the division and allocation of the area or the heuristic algorithms. Full article
(This article belongs to the Special Issue Multi-Agent Systems)
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14 pages, 661 KiB  
Article
FLaMAS: Federated Learning Based on a SPADE MAS
by Jaime Rincon, Vicente Julian and Carlos Carrascosa
Appl. Sci. 2022, 12(7), 3701; https://doi.org/10.3390/app12073701 - 06 Apr 2022
Cited by 8 | Viewed by 1794
Abstract
In recent years federated learning has emerged as a new paradigm for training machine learning models oriented to distributed systems. The main idea is that each node of a distributed system independently trains a model and shares only model parameters, such as weights, [...] Read more.
In recent years federated learning has emerged as a new paradigm for training machine learning models oriented to distributed systems. The main idea is that each node of a distributed system independently trains a model and shares only model parameters, such as weights, and does not share the training data set, which favors aspects such as security and privacy. Subsequently, and in a centralized way, a collective model is built that gathers all the information provided by all of the participating nodes. Several federated learning framework proposals have been developed that seek to optimize any aspect of the learning process. However, a lack of flexibility and dynamism is evident in many cases. In this regard, this study aims to provide flexibility and dynamism to the federated learning process. The methodology used consists of designing a multi-agent system that can form a federated learning framework where the agents act as nodes that can be easily added to the system dynamically. The proposal has been evaluated with different experiments on the SPADE platform; the results obtained demonstrate the benefits of the federated system while facilitating flexibility and scalability. Full article
(This article belongs to the Special Issue Multi-Agent Systems)
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14 pages, 1629 KiB  
Article
Counterfactual-Based Action Evaluation Algorithm in Multi-Agent Reinforcement Learning
by Yuyu Yuan, Pengqian Zhao, Ting Guo and Hongpu Jiang
Appl. Sci. 2022, 12(7), 3439; https://doi.org/10.3390/app12073439 - 28 Mar 2022
Cited by 4 | Viewed by 2030
Abstract
Multi-agent reinforcement learning (MARL) algorithms have made great achievements in various scenarios, but there are still many problems in solving sequential social dilemmas (SSDs). In SSDs, the agent’s actions not only change the instantaneous state of the environment but also affect the latent [...] Read more.
Multi-agent reinforcement learning (MARL) algorithms have made great achievements in various scenarios, but there are still many problems in solving sequential social dilemmas (SSDs). In SSDs, the agent’s actions not only change the instantaneous state of the environment but also affect the latent state which will, in turn, affect all agents. However, most of the current reinforcement learning algorithms focus on analyzing the value of instantaneous environment state while ignoring the study of the latent state, which is very important for establishing cooperation. Therefore, we propose a novel counterfactual reasoning-based multi-agent reinforcement learning algorithm to evaluate the continuous contribution of agent actions on the latent state. We compute that using simulation reasoning and building an action evaluation network. Then through counterfactual reasoning, we can get a single agent’s influence on the environment. Using this continuous contribution as an intrinsic reward enables the agent to consider the collective, thereby promoting cooperation. We conduct experiments in the SSDs environment, and the results show that the collective reward is increased by at least 25% which demonstrates the excellent performance of our proposed algorithm compared to the state-of-the-art algorithms. Full article
(This article belongs to the Special Issue Multi-Agent Systems)
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25 pages, 2538 KiB  
Article
Comparative Agent-Based Simulations on Levels of Multiplicity Using a Network Regression: A Mobile Dating Use-Case
by Joseph A. E. Shaheen, Collin Henley, Liam McKenna, Steven Hoang and Fatma Abdulwahab
Appl. Sci. 2022, 12(4), 1982; https://doi.org/10.3390/app12041982 - 14 Feb 2022
Viewed by 1975
Abstract
We demonstrate the use of agent-based models to simulate the interactions of two mobile dating applications that possess divergent interaction features. We reproduce several expected outcomes when compared to extant literature. We also demonstrate the use of a standard social network analysis technique—the [...] Read more.
We demonstrate the use of agent-based models to simulate the interactions of two mobile dating applications that possess divergent interaction features. We reproduce several expected outcomes when compared to extant literature. We also demonstrate the use of a standard social network analysis technique—the network regression, Multiple Regression Quadratic Assignment Procedure—in conducting a principled and interpretable comparison between the two models with strong results. This combined approach is novel and allows complex system modelers who utilize agent-based models to reduce their reliance on idealized network structures (small world, scale-free, erdos-renyi) when applying underlying network interactions to agent-based models that can often skew results and mislead from a full picture of system-level properties. This work serves as a proof-of-concept in the integration of classical social network analysis methods and contemporary agent-based modeling to compare software designs and to enhance the policy-generation process of online social networks. Full article
(This article belongs to the Special Issue Multi-Agent Systems)
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29 pages, 1828 KiB  
Article
From Affect Theoretical Foundations to Computational Models of Intelligent Affective Agents
by Bexy Alfonso, Joaquin Taverner, Emilio Vivancos and Vicente Botti
Appl. Sci. 2021, 11(22), 10874; https://doi.org/10.3390/app112210874 - 17 Nov 2021
Cited by 1 | Viewed by 1901
Abstract
The links between emotions and rationality have been extensively studied and discussed. Several computational approaches have also been proposed to model these links. However, is it possible to build generic computational approaches and languages so that they can be “adapted” when a specific [...] Read more.
The links between emotions and rationality have been extensively studied and discussed. Several computational approaches have also been proposed to model these links. However, is it possible to build generic computational approaches and languages so that they can be “adapted” when a specific affective phenomenon is being modeled? Would these approaches be sufficiently and properly grounded? In this work, we want to provide the means for the development of these generic approaches and languages by making a horizontal analysis inspired by philosophical and psychological theories of the main affective phenomena that are traditionally studied. Unfortunately, not all the affective theories can be adapted to be used in computational models; therefore, it is necessary to perform an analysis of the most suitable theories. In this analysis, we identify and classify the main processes and concepts which can be used in a generic affective computational model, and we propose a theoretical framework that includes all these processes and concepts that a model of an affective agent with practical reasoning could use. Our generic theoretical framework supports incremental research whereby future proposals can improve previous ones. This framework also supports the evaluation of the coverage of current computational approaches according to the processes that are modeled and according to the integration of practical reasoning and affect-related issues. This framework is being used in the development of the GenIA3 architecture. Full article
(This article belongs to the Special Issue Multi-Agent Systems)
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24 pages, 13003 KiB  
Article
Promoting the Emergence of Behavior Norms in a Principal–Agent Problem—An Agent-Based Modeling Approach Using Reinforcement Learning
by Saeed Harati, Liliana Perez and Roberto Molowny-Horas
Appl. Sci. 2021, 11(18), 8368; https://doi.org/10.3390/app11188368 - 09 Sep 2021
Cited by 3 | Viewed by 2197
Abstract
One of the complexities of social systems is the emergence of behavior norms that are costly for individuals. Study of such complexities is of interest in diverse fields ranging from marketing to sustainability. In this study we built a conceptual Agent-Based Model to [...] Read more.
One of the complexities of social systems is the emergence of behavior norms that are costly for individuals. Study of such complexities is of interest in diverse fields ranging from marketing to sustainability. In this study we built a conceptual Agent-Based Model to simulate interactions between a group of agents and a governing agent, where the governing agent encourages other agents to perform, in exchange for recognition, an action that is beneficial for the governing agent but costly for the individual agents. We equipped the governing agent with six Temporal Difference Reinforcement Learning algorithms to find sequences of decisions that successfully encourage the group of agents to perform the desired action. Our results show that if the individual agents’ perceived cost of the action is low, then the desired action can become a trend in the society without the use of learning algorithms by the governing agent. If the perceived cost to individual agents is high, then the desired output may become rare in the space of all possible outcomes but can be found by appropriate algorithms. We found that Double Learning algorithms perform better than other algorithms we used. Through comparison with a baseline, we showed that our algorithms made a substantial difference in the rewards that can be obtained in the simulations. Full article
(This article belongs to the Special Issue Multi-Agent Systems)
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22 pages, 3093 KiB  
Article
Agent-Based Simulation to Measure the Effectiveness of Citizen Sensing Applications—The Case of Missing Children
by Ariadni Michalitsi-Psarrou, Iason Lazaros Papageorgiou, Christos Ntanos and John Psarras
Appl. Sci. 2021, 11(14), 6530; https://doi.org/10.3390/app11146530 - 15 Jul 2021
Cited by 2 | Viewed by 2305
Abstract
Citizen sensing applications need to have a number of users defined that ensures their effectiveness. This is not a straightforward task because neither the relationship between the size of the userbase or its effectiveness is easily quantified, nor is it clear which threshold [...] Read more.
Citizen sensing applications need to have a number of users defined that ensures their effectiveness. This is not a straightforward task because neither the relationship between the size of the userbase or its effectiveness is easily quantified, nor is it clear which threshold for the number of users would make the application ‘effective’. This paper presents an approach for estimating the number of users needed for location-based crowdsourcing applications to work successfully, depending on the use case, the circumstances, and the criteria of success. It circumvents various issues, ethical or practical, in performing real-world controlled experiments and tackles this challenge by developing an agent-based modelling and simulation framework. This framework is tested on a specific scenario, that of missing children and the search for them. The search is performed with the contribution of citizens being made aware of the disappearance through a mobile application. The result produces an easily reconfigurable testbed for the effectiveness of citizen sensing mobile applications, allowing the study of the marginal utility of new users of the application. The resulting framework aims to be the digital twin of a real urban scenario, and it has been designed to be easily adapted and support decisions on the feasibility, evaluation, and targeting of the deployment of spatial crowdsourcing applications. Full article
(This article belongs to the Special Issue Multi-Agent Systems)
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26 pages, 534 KiB  
Article
Can Social Agents Efficiently Perform in Automated Negotiation?
by Victor Sanchez-Anguix, Okan Tunalı, Reyhan Aydoğan and Vicente Julian
Appl. Sci. 2021, 11(13), 6022; https://doi.org/10.3390/app11136022 - 29 Jun 2021
Cited by 7 | Viewed by 2402
Abstract
In the last few years, we witnessed a growing body of literature about automated negotiation. Mainly, negotiating agents are either purely self-driven by maximizing their utility function or by assuming a cooperative stance by all parties involved in the negotiation. We argue that, [...] Read more.
In the last few years, we witnessed a growing body of literature about automated negotiation. Mainly, negotiating agents are either purely self-driven by maximizing their utility function or by assuming a cooperative stance by all parties involved in the negotiation. We argue that, while optimizing one’s utility function is essential, agents in a society should not ignore the opponent’s utility in the final agreement to improve the agent’s long-term perspectives in the system. This article aims to show whether it is possible to design a social agent (i.e., one that aims to optimize both sides’ utility functions) while performing efficiently in an agent society. Accordingly, we propose a social agent supported by a portfolio of strategies, a novel tit-for-tat concession mechanism, and a frequency-based opponent modeling mechanism capable of adapting its behavior according to the opponent’s behavior and the state of the negotiation. The results show that the proposed social agent not only maximizes social metrics such as the distance to the Nash bargaining point or the Kalai point but also is shown to be a pure and mixed equilibrium strategy in some realistic agent societies. Full article
(This article belongs to the Special Issue Multi-Agent Systems)
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19 pages, 2038 KiB  
Article
Exploring the Impact of Driver Adherence to Speed Limits and the Interdependence of Roadside Collisions in an Urban Environment: An Agent-Based Modelling Approach
by Sedar Olmez, Liam Douglas-Mann, Ed Manley, Keiran Suchak, Alison Heppenstall, Dan Birks and Annabel Whipp
Appl. Sci. 2021, 11(12), 5336; https://doi.org/10.3390/app11125336 - 08 Jun 2021
Cited by 4 | Viewed by 3151
Abstract
Roadside collisions are a significant problem faced by all countries. Urbanisation has led to an increase in traffic congestion and roadside vehicle collisions. According to the UK Government’s Department for Transport, most vehicle collisions occur on urban roads, with empirical evidence showing drivers [...] Read more.
Roadside collisions are a significant problem faced by all countries. Urbanisation has led to an increase in traffic congestion and roadside vehicle collisions. According to the UK Government’s Department for Transport, most vehicle collisions occur on urban roads, with empirical evidence showing drivers are more likely to break local and fixed speed limits in urban environments. Analysis conducted by the Department for Transport found that the UK’s accident prevention measure’s cost is estimated to be £33bn per year. Therefore, there is a strong motivation to investigate the causes of roadside collisions in urban environments to better prepare traffic management, support local council policies, and ultimately reduce collision rates. This study utilises agent-based modelling as a tool to plan, experiment and investigate the relationship between speeding and vehicle density with collisions. The study found that higher traffic density results in more vehicles travelling at a slower speed, regardless of the degree to which drivers comply with speed restrictions. Secondly, collisions increase linearly as speed compliance is reduced for all densities. Collisions are lowest when all vehicles comply with speed limits for all densities. Lastly, higher global traffic densities result in higher local traffic densities near-collision sites across all adherence levels, increasing the likelihood of congestion around these sites. This work, when extended to real-world applications using empirical data, can support effective road safety policies. Full article
(This article belongs to the Special Issue Multi-Agent Systems)
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18 pages, 790 KiB  
Article
Observer-Based Consensus Control for Heterogeneous Multi-Agent Systems with Output Saturations
by Young-Hun Lim and Gwang-Seok Lee
Appl. Sci. 2021, 11(10), 4345; https://doi.org/10.3390/app11104345 - 11 May 2021
Cited by 3 | Viewed by 2031
Abstract
This paper studies the consensus problem for heterogeneous multi-agent systems with output saturations. We consider the agents to have different dynamics and assume that the agents are neutrally stable and that the communication graph is undirected. The goal of this paper is to [...] Read more.
This paper studies the consensus problem for heterogeneous multi-agent systems with output saturations. We consider the agents to have different dynamics and assume that the agents are neutrally stable and that the communication graph is undirected. The goal of this paper is to achieve the consensus for leaderless and leader-following cases. To solve this problem, we propose the observer-based distributed consensus algorithms, which consists of three parts: the nonlinear observer, the reference generator, and the regulator. Then, we analyze the consensus based on the Lasalle’s Invariance Principle and the input-to-state stability. Finally, we provide numerical examples to demonstrate the validity of the proposed algorithms. Full article
(This article belongs to the Special Issue Multi-Agent Systems)
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