Editorial

3 pages, 173 KiB

Open AccessEditorial

by Vicente Julian and Vicente Botti

Appl. Sci. 2023, 13(2), 1021; https://doi.org/10.3390/app13021021 - 12 Jan 2023

Cited by 1 | Viewed by 1676

Multi-agent systems (MAS) are a class of systems in which multiple agents interact with each other and their environment to achieve a common or individual goal [...] Full article

(This article belongs to the Special Issue Multi-Agent Systems 2020)

Research

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14 pages, 817 KiB

Open AccessArticle

Stability Analysis of Multi-Agent Tracking Systems with Quasi-Cyclic Switching Topologies

by Dongyu Fan, Haikuo Shen and Lijing Dong

Appl. Sci. 2020, 10(24), 8889; https://doi.org/10.3390/app10248889 - 12 Dec 2020

Cited by 4 | Viewed by 1703

Abstract

In this paper, the stability problem of a class of multi-agent tracking systems with quasi-cyclic switching topologies is investigated. The existing results of systems with switching topologies are usually achieved based on the assumption that the piecewise constant communication topologies are connected and [...] Read more.

In this paper, the stability problem of a class of multi-agent tracking systems with quasi-cyclic switching topologies is investigated. The existing results of systems with switching topologies are usually achieved based on the assumption that the piecewise constant communication topologies are connected and the switchings are cyclic. The communication topologies are possible to be unconnected and it is difficult to guarantee the topologies switch circularly. The piecewise unconnected topology makes the interactive multi-agent tracking system to be an unstable subsystem over this time interval. In order to relax the assumption constraint, a quasi-cyclic method is proposed, which allows the topologies of multi-agent systems to switch in a less conservative way. Moreover, the stability of the tracking system with the existence of unstable subsystems is analyzed based on switched control theory. It is obtained that the convergence rate is affected by the maximum dwell time of unstable subsystems. Finally, a numerical example is provided to demonstrate the effectiveness of the theoretical results. Full article

(This article belongs to the Special Issue Multi-Agent Systems 2020)

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16 pages, 381 KiB

Open AccessArticle

Observer-Based Distributed Fault Detection for Heterogeneous Multi-Agent Systems

by Wenhao Jia and Jinzhi Wang

Appl. Sci. 2020, 10(21), 7466; https://doi.org/10.3390/app10217466 - 23 Oct 2020

Cited by 7 | Viewed by 1376

Abstract

This paper solves the distributed fault detection (FD) problem for heterogeneous multi-agent systems (MAS). For a heterogeneous MAS, we adopt a distributed control law to realise cooperative output regulation (COR) when no fault occurs in the MAS, and propose a state-feedback-based FD scheme, [...] Read more.

This paper solves the distributed fault detection (FD) problem for heterogeneous multi-agent systems (MAS). For a heterogeneous MAS, we adopt a distributed control law to realise cooperative output regulation (COR) when no fault occurs in the MAS, and propose a state-feedback-based FD scheme, where the adopted distributed control law and proposed FD scheme all utilise state information. Furthermore, we consider the condition that state information is unmeasurable, the output-feedback-based distributed FD scheme is proposed, and the adopted distributed control law also utilises measurement output. Finally, two numerical examples are utilised to verify that the proposed distributed FD schemes could locate and remove the faulty agent in time. Full article

(This article belongs to the Special Issue Multi-Agent Systems 2020)

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22 pages, 3436 KiB

Open AccessArticle

Framework for Incorporating Artificial Somatic Markers in the Decision-Making of Autonomous Agents

by Daniel Cabrera, Claudio Cubillos, Enrique Urra and Rafael Mellado

Appl. Sci. 2020, 10(20), 7361; https://doi.org/10.3390/app10207361 - 21 Oct 2020

Cited by 6 | Viewed by 3120

Abstract

The somatic marker hypothesis proposes that when a person faces a decision scenario, many thoughts arise and different “physical consequences” are fleetingly observable. It is generally accepted that affective dimension influences cognitive capacities. Several proposals for including affectivity within artificial systems have been [...] Read more.

The somatic marker hypothesis proposes that when a person faces a decision scenario, many thoughts arise and different “physical consequences” are fleetingly observable. It is generally accepted that affective dimension influences cognitive capacities. Several proposals for including affectivity within artificial systems have been presented. However, to the best of our knowledge, a proposal that considers the incorporation of artificial somatic markers in a disaggregated and specialized way for the different phases that make up a decision-making process has not been observed yet. Thus, this research work proposes a framework that considers the incorporation of artificial somatic markers in different phases of the decision-making of autonomous agents: recognition of decision point; determination of the courses of action; analysis of decision options; decision selection and performing; memory management. Additionally, a unified decision-making process and a general architecture for autonomous agents are presented. This proposal offers a qualitative perspective following an approach of grounded theory, which is suggested when existing theories or models cannot fully explain or understand a phenomenon or circumstance under study. This research work represents a novel contribution to the body of knowledge in guiding the incorporation of this biological concept in artificial terms within autonomous agents. Full article

(This article belongs to the Special Issue Multi-Agent Systems 2020)

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29 pages, 289 KiB

Open AccessFeature PaperArticle

A Review on MAS-Based Sentiment and Stress Analysis User-Guiding and Risk-Prevention Systems in Social Network Analysis

by Guillem Aguado, Vicente Julián, Ana García-Fornes and Agustín Espinosa

Appl. Sci. 2020, 10(19), 6746; https://doi.org/10.3390/app10196746 - 26 Sep 2020

Cited by 1 | Viewed by 2196

Abstract

In the current world we live immersed in online applications, being one of the most present of them Social Network Sites (SNSs), and different issues arise from this interaction. Therefore, there is a need for research that addresses the potential issues born from [...] Read more.

In the current world we live immersed in online applications, being one of the most present of them Social Network Sites (SNSs), and different issues arise from this interaction. Therefore, there is a need for research that addresses the potential issues born from the increasing user interaction when navigating. For this reason, in this survey we explore works in the line of prevention of risks that can arise from social interaction in online environments, focusing on works using Multi-Agent System (MAS) technologies. For being able to assess what techniques are available for prevention, works in the detection of sentiment polarity and stress levels of users in SNSs will be reviewed. We review with special attention works using MAS technologies for user recommendation and guiding. Through the analysis of previous approaches on detection of the user state and risk prevention in SNSs we elaborate potential future lines of work that might lead to future applications where users can navigate and interact between each other in a more safe way. Full article

(This article belongs to the Special Issue Multi-Agent Systems 2020)

18 pages, 899 KiB

Open AccessArticle

Robustness Analysis for Multi-Agent Consensus Systems with Application to DC Motor Synchronization

by Daniel Olivares, Gerardo Romero, Jose A. Guerrero and Rogelio Lozano

Appl. Sci. 2020, 10(18), 6521; https://doi.org/10.3390/app10186521 - 18 Sep 2020

Cited by 2 | Viewed by 1928

Abstract

DC motor speed synchronization is a critical problem in industrial and robotic applications. To tackle this problem, we propose to use a multi-agent consensus-based control scheme that guarantees the convergence of the DC motor speeds to either fixed or time-varying reference. A detailed [...] Read more.

DC motor speed synchronization is a critical problem in industrial and robotic applications. To tackle this problem, we propose to use a multi-agent consensus-based control scheme that guarantees the convergence of the DC motor speeds to either fixed or time-varying reference. A detailed robustness analysis considering parametric uncertainty and time delay in the multi-agent system is performed to guarantee the consensus on the speed of DC motors in actual practice. The results obtained concerning the robustness analysis allowed us to implement experimental tests on a three-motor system using a wireless communication system to achieve satisfactory performance. Full article

(This article belongs to the Special Issue Multi-Agent Systems 2020)

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16 pages, 4686 KiB

Open AccessArticle

Cooperative Multi-Agent Interaction and Evaluation Framework Considering Competitive Networks with Dynamic Topology Changes

by Jinbae Kim and Hyunsoo Lee

Appl. Sci. 2020, 10(17), 5828; https://doi.org/10.3390/app10175828 - 23 Aug 2020

Cited by 4 | Viewed by 2261

Abstract

In recent years, the problem of reinforcement learning has become increasingly complex, and the computational demands with respect to such processes have increased. Accordingly, various methods for effective learning have been proposed. With the help of humans, the learning object can learn more [...] Read more.

In recent years, the problem of reinforcement learning has become increasingly complex, and the computational demands with respect to such processes have increased. Accordingly, various methods for effective learning have been proposed. With the help of humans, the learning object can learn more accurately and quickly to maximize the reward. However, the rewards calculated by the system and via human intervention (that make up the learning environment) differ and must be used accordingly. In this paper, we propose a framework for learning the problems of competitive network topologies, wherein the environment dynamically changes agent, by computing the rewards via the system and via human evaluation. The proposed method is adaptively updated with the rewards calculated via human evaluation, making it more stable and reducing the penalty incurred while learning. It also ensures learning accuracy, including rewards generated from complex network topology consisting of multiple agents. The proposed framework contributes to fast training process using multi-agent cooperation. By implementing these methods as software programs, this study performs numerical analysis to demonstrate the effectiveness of the adaptive evaluation framework applied to the competitive network problem depicting the dynamic environmental topology changes proposed herein. As per the numerical experiments, the greater is the human intervention, the better is the learning performance with the proposed framework. Full article

(This article belongs to the Special Issue Multi-Agent Systems 2020)

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26 pages, 798 KiB

Open AccessArticle

Designing Multi-Agent System Organisations for Flexible Runtime Behaviour

by Kathleen Keogh and Liz Sonenberg

Appl. Sci. 2020, 10(15), 5335; https://doi.org/10.3390/app10155335 - 02 Aug 2020

Cited by 4 | Viewed by 3042

Abstract

We address the challenge of multi-agent system (MAS) design for organisations of agents acting in dynamic and uncertain environments where runtime flexibility is required to enable improvisation through sharing knowledge and adapting behaviour. We identify behavioural features that correspond to runtime improvisation by [...] Read more.

We address the challenge of multi-agent system (MAS) design for organisations of agents acting in dynamic and uncertain environments where runtime flexibility is required to enable improvisation through sharing knowledge and adapting behaviour. We identify behavioural features that correspond to runtime improvisation by agents in a MAS organisation and from this analysis describe the OJAzzIC meta-model and an associated design method. We present results from simulation scenarios, varying both problem complexity and the level of organisational support provided in the design, to show that increasing design time guidance in the organisation specification can enable runtime flexibility afforded to agents and improve performance. Hence the results demonstrate the usefulness of the constructs captured in the OJAzzIC meta-model. Full article

(This article belongs to the Special Issue Multi-Agent Systems 2020)

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27 pages, 2457 KiB

Open AccessArticle

A Multiple Criteria Decision Analysis Framework for Dispersed Group Decision-Making Contexts

by João Carneiro, Diogo Martinho, Patrícia Alves, Luís Conceição, Goreti Marreiros and Paulo Novais

Appl. Sci. 2020, 10(13), 4614; https://doi.org/10.3390/app10134614 - 03 Jul 2020

Cited by 10 | Viewed by 2540

Abstract

To support Group Decision-Making processes when participants are dispersed is a complex task. The biggest challenges are related to communication limitations that impede decision-makers to take advantage of the benefits associated with face-to-face Group Decision-Making processes. Several approaches that intend to aid dispersed [...] Read more.

To support Group Decision-Making processes when participants are dispersed is a complex task. The biggest challenges are related to communication limitations that impede decision-makers to take advantage of the benefits associated with face-to-face Group Decision-Making processes. Several approaches that intend to aid dispersed groups attaining decisions have been applied to Group Decision Support Systems. However, strategies to support decision-makers in reasoning, understanding the reasons behind the different recommendations, and promoting the decision quality are very limited. In this work, we propose a Multiple Criteria Decision Analysis Framework that intends to overcome those limitations through a set of functionalities that can be used to support decision-makers attaining more informed, consistent, and satisfactory decisions. These functionalities are exposed through a microservice, which is part of a Consensus-Based Group Decision Support System and is used by autonomous software agents to support decision-makers according to their specific needs/interests. We concluded that the proposed framework greatly facilitates the definition of important procedures, allowing decision-makers to take advantage of deciding as a group and to understand the reasons behind the different recommendations and proposals. Full article

(This article belongs to the Special Issue Multi-Agent Systems 2020)

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18 pages, 4048 KiB

Open AccessFeature PaperArticle

An Intelligent Approach to Allocating Resources within an Agent-Based Cloud Computing Platform

by Fernando De la Prieta, Sara Rodríguez-González, Pablo Chamoso, Yves Demazeau and Juan Manuel Corchado

Appl. Sci. 2020, 10(12), 4361; https://doi.org/10.3390/app10124361 - 25 Jun 2020

Cited by 7 | Viewed by 2265

Abstract

The cloud computing paradigm has the ability to adapt to new technologies and provide consistent cloud services. These features have led to the widespread use of the paradigm, making it necessary for the underlying computer infrastructure to cope with the increased demand and [...] Read more.

The cloud computing paradigm has the ability to adapt to new technologies and provide consistent cloud services. These features have led to the widespread use of the paradigm, making it necessary for the underlying computer infrastructure to cope with the increased demand and the high number of end users. Platforms often use classical mathematical models for this purpose, helping assign computational resources to the services provided to the final user. Although this kind of model is valid and widespread, it can be refined through intelligent techniques. Therefore, this research presents a novel system consisting of a multi-agent system, which integrates a case-based reasoning system. The resulting system dynamically allocates resources within a cloud computing platform. This approach, which is distributed and scalable, can learn from previous experiences and produce better results in each resource allocation. A model of the system has been implemented and tested on a real cloud platform with successful results. Full article

(This article belongs to the Special Issue Multi-Agent Systems 2020)

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26 pages, 3267 KiB

Open AccessArticle

Central Heating Cost Optimization for Smart-Homes with Fuzzy Logic and a Multi-Agent Architecture

by Diego M. Jiménez-Bravo, Álvaro Lozano Murciego, Daniel H. de la Iglesia, Juan F. De Paz and Gabriel Villarrubia González

Appl. Sci. 2020, 10(12), 4057; https://doi.org/10.3390/app10124057 - 12 Jun 2020

Cited by 6 | Viewed by 2225

Abstract

Recent years have defined the need to reduce gas emissions to fight climate change, and society’s move to green energies is important to make responsible use of non-renewable energies. Therefore, it is now important to use technologies to optimize the use of actual [...] Read more.

Recent years have defined the need to reduce gas emissions to fight climate change, and society’s move to green energies is important to make responsible use of non-renewable energies. Therefore, it is now important to use technologies to optimize the use of actual energy sources. In this aspect, the Internet of Things (IoT) technology has had a great impact on society. Hence, this research work aims to use IoT technology and multi-agent systems to optimize the use of central heating installation in buildings. It is intended to improve the user’s comfort, reduce the consumption of energy and reduce the financial costs. Therefore, a multi-agent system is proposed to collect data from sensors located in a smart-home and obtain the best action to perform in a central heating system. The decisions will be taken by an intelligent agent based on fuzzy logic. This technology will allow for generating the control action with a fuzzy controller. The results obtained show that the proposal improves the actual system in terms of users’ comfort and financial and energy savings. Full article

(This article belongs to the Special Issue Multi-Agent Systems 2020)

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15 pages, 2929 KiB

Open AccessArticle

Realistic Multi-Agent Formation Using Discretionary Group Behavior (DGB)

by Nahid Salehi and Mankyu Sung

Appl. Sci. 2020, 10(10), 3518; https://doi.org/10.3390/app10103518 - 19 May 2020

Cited by 3 | Viewed by 1987

Abstract

Simulating groups and their behaviors have been one of the important topics recently. This paper proposes a novel velocity-based method to simulate the realistic behavior of groups moving in a specific formation in a virtual environment including other groups and obstacles. The proposed [...] Read more.

Simulating groups and their behaviors have been one of the important topics recently. This paper proposes a novel velocity-based method to simulate the realistic behavior of groups moving in a specific formation in a virtual environment including other groups and obstacles. The proposed algorithm, we called “DGB—Discretionary Group Behavior”, takes advantage of ORCA (Optimal Reciprocal Collision Avoidance) half-planes for both grouping and collision avoidance strategy. By considering new half-planes for each agent, we can have more reasonable and intelligent behavior in front of challenging obstacles and other agents. Unlike recent similar works, independent members in a group do not have predefined connections to each other even though they can keep the group’s formation while moving and trying to follow their best neighbors discretionarily in critical situations. Through experiments, we found that the proposed algorithm can yield more human-like group behavior in a crowd of agents. Full article

(This article belongs to the Special Issue Multi-Agent Systems 2020)

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18 pages, 902 KiB

Open AccessArticle

Recommending Learning Objects with Arguments and Explanations

by Stella Heras, Javier Palanca, Paula Rodriguez, Néstor Duque-Méndez and Vicente Julian

Appl. Sci. 2020, 10(10), 3341; https://doi.org/10.3390/app10103341 - 12 May 2020

Cited by 8 | Viewed by 1949

Abstract

The massive presence of online learning resources leads many students to have more information than they can consume efficiently. Therefore, students do not always find adaptive learning material for their needs and preferences. In this paper, we present a Conversational Educational Recommender System [...] Read more.

The massive presence of online learning resources leads many students to have more information than they can consume efficiently. Therefore, students do not always find adaptive learning material for their needs and preferences. In this paper, we present a Conversational Educational Recommender System (C-ERS), which helps students in the process of finding the more appropriated learning resources considering their learning objectives and profile. The recommendation process is based on an argumentation-based approach that selects the learning objects that allow a greater number of arguments to be generated to justify their suitability. Our system includes a simple and intuitive communication interface with the user that provides an explanation to any recommendation. This allows the user to interact with the system and accept or reject the recommendations, providing reasons for such behavior. In this way, the user is able to inspect the system’s operation and understand the recommendations, while the system is able to elicit the actual preferences of the user. The system has been tested online with a real group of undergraduate students in the Universidad Nacional de Colombia, showing promising results. Full article

(This article belongs to the Special Issue Multi-Agent Systems 2020)

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19 pages, 2893 KiB

Open AccessArticle

Fair Task Allocation When Cost of Task Is Multidimensional

by Fengjie Sun, Xianchang Wang and Rui Zhang

Appl. Sci. 2020, 10(8), 2798; https://doi.org/10.3390/app10082798 - 17 Apr 2020

Cited by 5 | Viewed by 1959

Abstract

We consider the problem of fairly allocating indivisible tasks, focusing on a recently introduced notion of fairness called Minmax share guarantee. Minmax share (MMS) is a term of fairness guarantees that is defined to be the minimum cost that an agent can ensure [...] Read more.

We consider the problem of fairly allocating indivisible tasks, focusing on a recently introduced notion of fairness called Minmax share guarantee. Minmax share (MMS) is a term of fairness guarantees that is defined to be the minimum cost that an agent can ensure for herself, if she were to partition the tasks into n bundles, and then receive the maximum cost bundle of tasks. However, the cost of tasks considered in previous work is single dimensional, and multidimensional situations have not been researched. In this work, we proposed an allocation algorithm that allocates tasks with multidimensional cost to agents under ordinal model. We prove the approximation ratio of MMS of the algorithm proposed can be guaranteed under

2 + \frac{m \cdot α_{i} \cdot (1 + n) - n}{n^{2}}

, in addition the time complexity of the algorithm is

O (m log m)

. This proposed method is implemented and tested on datasets generated based on a real environment, and the experimental result shows that our algorithm has better performance than existing task allocation algorithms when cost of tasks is multidimensional. Full article

(This article belongs to the Special Issue Multi-Agent Systems 2020)

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17 pages, 612 KiB

Open AccessArticle

An Agent-Ensemble for Thresholded Multi-Target Classification

by Nathan H. Parrish, Ashley J. Llorens and Alex E. Driskell

Appl. Sci. 2020, 10(4), 1376; https://doi.org/10.3390/app10041376 - 18 Feb 2020

Cited by 1 | Viewed by 1737

Abstract

We propose an ensemble approach for multi-target binary classification, where the target class breaks down into a disparate set of pre-defined target-types. The system goal is to maximize the probability of alerting on targets from any type while excluding background clutter. The agent-classifiers [...] Read more.

We propose an ensemble approach for multi-target binary classification, where the target class breaks down into a disparate set of pre-defined target-types. The system goal is to maximize the probability of alerting on targets from any type while excluding background clutter. The agent-classifiers that make up the ensemble are binary classifiers trained to classify between one of the target-types vs. clutter. The agent ensemble approach offers several benefits for multi-target classification including straightforward in-situ tuning of the ensemble to drift in the target population and the ability to give an indication to a human operator of which target-type causes an alert. We propose a combination strategy that sums weighted likelihood ratios of the individual agent-classifiers, where the likelihood ratio is between the target-type for the agent vs. clutter. We show that this combination strategy is optimal under a conditionally non-discriminative assumption. We compare this combiner to the common strategy of selecting the maximum of the normalized agent-scores as the combiner score. We show experimentally that the proposed combiner gives excellent performance on the multi-target binary classification problems of pin-less verification of human faces and vehicle classification using acoustic signatures. Full article

(This article belongs to the Special Issue Multi-Agent Systems 2020)

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13 pages, 321 KiB

Open AccessArticle

Self-Organization When Pedestrians Move in Opposite Directions. Multi-Lane Circular Track Model

by Guillermo H. Goldsztein

Appl. Sci. 2020, 10(2), 563; https://doi.org/10.3390/app10020563 - 13 Jan 2020

Cited by 5 | Viewed by 1914

Abstract

When pedestrians walk along a corridor in both directions, a frequently observed phenomenon is the segregation of the whole group into lanes of individuals moving in the same direction. While this formation of lanes facilitates the flow and benefits the whole group, it [...] Read more.

When pedestrians walk along a corridor in both directions, a frequently observed phenomenon is the segregation of the whole group into lanes of individuals moving in the same direction. While this formation of lanes facilitates the flow and benefits the whole group, it is believed that results from the actions of the individuals acting on their behalf, without considering others. This phenomenon is an example of self-organization and has attracted the attention of a number of researchers in diverse fields. We introduce and analyze a simple model. We assume that individuals move around a multi-lane circular track. All of them move at the same speed. Half of them in one direction and the rest in the opposite direction. Each time two individuals collide, one of them moves to a neighboring lane. The individual changing lanes is selected randomly. We prove that the system self-organizes. Eventually, each lane is occupied with individuals moving in only one direction. Our analysis supports the belief that global self-organization is possible even if each member of the group acts without considering the rest. Full article

(This article belongs to the Special Issue Multi-Agent Systems 2020)

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33 pages, 7103 KiB

Open AccessArticle

A Multiagent System Prototype of a Tacit Knowledge Management Model to Reduce Labor Incident Resolution Times

by Lilyam Paolino, David Lizcano, Genoveva López and Jaime Lloret

Appl. Sci. 2019, 9(24), 5448; https://doi.org/10.3390/app9245448 - 12 Dec 2019

Cited by 4 | Viewed by 2660

Abstract

The transformation of the tacit knowledge of a company’s human resources into permanent organizational capital in spite of possible staff turnover is of business interest. This research focuses on the management of tacit knowledge to resolve labor incidents and reduce resolution times. We [...] Read more.

The transformation of the tacit knowledge of a company’s human resources into permanent organizational capital in spite of possible staff turnover is of business interest. This research focuses on the management of tacit knowledge to resolve labor incidents and reduce resolution times. We present the GESTAC model, a name derived from the first syllables of the Spanish words “gestión” (management) and “tácito” (tacit), which identifies, locates and rates people in the business domain capable of resolving a labor incident logged by a user employed by the company. In order to achieve its objective, the GESTAC model follows the tacit knowledge management paradigm, according to which tacit knowledge that could eventually resolve the logged incidents is identified, captured and stored in a permanent database, and then evaluated and disseminated to the people who have need of the knowledge. This could lead to the knowledge source being automatically rerated, and the entire process restarted. The aim is to contribute to the state of the art, showing that by applying tacit knowledge management to a specific domain the GESTAC model is able to reduce incident resolution times with respect to traditional systems. The model was prototyped (GESTAC_APP) using the multiagent systems paradigm. Full article

(This article belongs to the Special Issue Multi-Agent Systems 2020)

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18 pages, 832 KiB

Open AccessFeature PaperArticle

An Abstract Framework for Non-Cooperative Multi-Agent Planning

by Jaume Jordán, Javier Bajo, Vicent Botti and Vicente Julian

Appl. Sci. 2019, 9(23), 5180; https://doi.org/10.3390/app9235180 - 29 Nov 2019

Cited by 3 | Viewed by 2745

Abstract

In non-cooperative multi-agent planning environments, it is essential to have a system that enables the agents’ strategic behavior. It is also important to consider all planning phases, i.e., goal allocation, strategic planning, and plan execution, in order to solve a complete problem. Currently, [...] Read more.

In non-cooperative multi-agent planning environments, it is essential to have a system that enables the agents’ strategic behavior. It is also important to consider all planning phases, i.e., goal allocation, strategic planning, and plan execution, in order to solve a complete problem. Currently, we have no evidence of the existence of any framework that brings together all these phases for non-cooperative multi-agent planning environments. In this work, an exhaustive study is made to identify existing approaches for the different phases as well as frameworks and different applicable techniques in each phase. Thus, an abstract framework that covers all the necessary phases to solve these types of problems is proposed. In addition, we provide a concrete instantiation of the abstract framework using different techniques to promote all the advantages that the framework can offer. A case study is also carried out to show an illustrative example of how to solve a non-cooperative multi-agent planning problem with the presented framework. This work aims to establish a base on which to implement all the necessary phases using the appropriate technologies in each of them and to solve complex problems in different domains of application for non-cooperative multi-agent planning settings. Full article

(This article belongs to the Special Issue Multi-Agent Systems 2020)

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27 pages, 4886 KiB

Open AccessArticle

Improving Agent Quality in Dynamic Smart Cities by Implementing an Agent Quality Management Framework

by Najwa Abu Bakar, Ali Selamat and Ondrej Krejcar

Appl. Sci. 2019, 9(23), 5111; https://doi.org/10.3390/app9235111 - 26 Nov 2019

Cited by 4 | Viewed by 2534

Abstract

It is critical for quality requirements, such as trust, privacy, and confidentiality, to be fulfilled during the execution of smart city applications. In this study, smart city applications were modeled as agent systems composed of many agents, each with its own privacy and [...] Read more.

It is critical for quality requirements, such as trust, privacy, and confidentiality, to be fulfilled during the execution of smart city applications. In this study, smart city applications were modeled as agent systems composed of many agents, each with its own privacy and confidentiality properties. Violations of those properties may occur during execution due to the dynamic of agent behavior, decision-making capabilities, and social activities. In this research, a framework called Agent Quality Management was proposed and implemented to manage agent quality in agent systems. This paper demonstrates the effectiveness of the approach by applying it toward a smart city application called a crowdsourced navigation system to verify and assess agent data confidentiality. The AnyLogic Agent-Based Modeling tool was used to model and conduct the experiments. The experiments showed that the framework helped to improve the detection of agent quality violations in a dynamic smart city application. The results can be further analyzed using advanced data analytic approach to reduce future violations and improve data confidentiality in a smart city environment. Full article

(This article belongs to the Special Issue Multi-Agent Systems 2020)

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19 pages, 1478 KiB

Open AccessArticle

Coordinated Formation Design of Multi-Robot Systems via an Adaptive-Gain Super-Twisting Sliding Mode Method

by Dianwei Qian, Guigang Zhang, Jiarong Chen, Jian Wang and Zhimin Wu

Appl. Sci. 2019, 9(20), 4315; https://doi.org/10.3390/app9204315 - 14 Oct 2019

Cited by 6 | Viewed by 2008

Abstract

This paper presents a super-twisting-based sliding mode control method for the formation problem of multi-robot systems. The multiple robots contain plenty of uncertainties and disturbances. Such a control method has two adaptive gains that can contribute to the robustness and improve the response [...] Read more.

This paper presents a super-twisting-based sliding mode control method for the formation problem of multi-robot systems. The multiple robots contain plenty of uncertainties and disturbances. Such a control method has two adaptive gains that can contribute to the robustness and improve the response of the formation maneuvers despite these uncertainties and disturbances. Based on the leader-follower frame, this control method was investigated. The closed-loop formation stability is theoretically guaranteed in the sense of Lyapunov. From the aspect of practice, the control method was carried out by a multi-robot system to achieve some desired formation patterns. Some numerical results were demonstrated to verify the feasibility of the control method. Some comparisons were also illustrated to support the superiority and effectiveness of the presented sliding mode control method. Full article

(This article belongs to the Special Issue Multi-Agent Systems 2020)

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13 pages, 448 KiB

Open AccessArticle

Flocking of Multi-Agent System with Nonlinear Dynamics via Distributed Event-Triggered Control

by Yanhua Shen, Zhengmin Kong and Li Ding

Appl. Sci. 2019, 9(7), 1336; https://doi.org/10.3390/app9071336 - 29 Mar 2019

Cited by 19 | Viewed by 2529

Abstract

In this paper, a distributed event-triggered control strategy is proposed to investigate a flocking problem in a multi-agent system with Lipschitz nonlinear dynamics, where triggering conditions are proposed to determine the instants to update the controller. A distributed event-triggered control law with bounded [...] Read more.

In this paper, a distributed event-triggered control strategy is proposed to investigate a flocking problem in a multi-agent system with Lipschitz nonlinear dynamics, where triggering conditions are proposed to determine the instants to update the controller. A distributed event-triggered control law with bounded action function is proposed for free flocking. It is proved that the designed event-triggered controller ensures a group of agents reach stable flocking motion while preserving connectivity of the communication network. Lastly, simulations are provided to verify the effectiveness of the theoretical results. Full article

(This article belongs to the Special Issue Multi-Agent Systems 2020)

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18 pages, 5636 KiB

Open AccessArticle

Coin Recognition Approach in Social Environments Using Virtual Organizations of Agents

by André Sales Mendes, Gabriel Villarrubia González, Juan Francisco De Paz, Alberto López Barriuso and Álvaro Lozano Murciego

Appl. Sci. 2019, 9(6), 1252; https://doi.org/10.3390/app9061252 - 25 Mar 2019

Cited by 1 | Viewed by 4249

Abstract

Social systems have gained relevance during the last decade, trying to provide intelligent environments where humans and machines collaborate to resolve a social problem. The main objective of this paper is to obtain an intelligent system specifically designed to help dependent and/or visually [...] Read more.

Social systems have gained relevance during the last decade, trying to provide intelligent environments where humans and machines collaborate to resolve a social problem. The main objective of this paper is to obtain an intelligent system specifically designed to help dependent and/or visually disabled people to count money more easily by using a mobile phone camera. The proposed system incorporates an image recognition system for classifying coins by using homography to transform images previously for classification tasks. The main difficulty in the appliance of these techniques relies on the fact that camera position and height are unknown. This process allows changing the perspective of the images in order to calculate different meaningful variables such as diameter and colour employed later to perform classification and counting tasks. The system uses the information of the variables as inputs for classification algorithms that allow us to identify the amount and type of coins. The system has been tested with euro coins. This paper presents the results obtained. Full article

(This article belongs to the Special Issue Multi-Agent Systems 2020)

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10 pages, 3035 KiB

Open AccessLetter

Design of Warship Simulation Using Variable-Chromosome Genetic Algorithm

by Kang-moon Park, Suk-hoon Shin, Donghoon Shin and Sung-do Chi

Appl. Sci. 2019, 9(19), 4131; https://doi.org/10.3390/app9194131 - 02 Oct 2019

Cited by 7 | Viewed by 2559

Abstract

A genetic algorithm (GA) is a global search algorithm based on biological genetics. GAs are generally used for industrial applications, artificial neural networks, web applications, the defense industry, and so on. However, it is difficult to apply GAs to more complex situations because [...] Read more.

A genetic algorithm (GA) is a global search algorithm based on biological genetics. GAs are generally used for industrial applications, artificial neural networks, web applications, the defense industry, and so on. However, it is difficult to apply GAs to more complex situations because of the fixed number of chromosomes. In this research, in order to overcome this limitation, we propose a variable-chromosome GA with a chromosome attachment feature. Verification of the algorithm is carried out through anti-submarine high value unit (HVU) escort mission simulations. Ultimately, it is confirmed that the GA using the variable chromosome is more effective in dealing with highly complex missions, whereby the number of chromosomes gradually increases. Full article

(This article belongs to the Special Issue Multi-Agent Systems 2020)

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