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Multi-Agent Systems 2019

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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 January 2020) | Viewed by 36951

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Special Issue Editors

Prof. Dr. Andrea Omicini

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Department of Computer Science and Engineering (DISI) Alma Mater Studiorum – Università di Bologna, 47521 Cesena, Italy
Interests: distributed and pervasive systems; agents and multiagent systems; software engineering; intelligent systems; multi-paradigm programming languages; simulation; self-organization
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Dr. Stefano Mariani

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Guest Editor

Department of Sciences and Methods for Engineering, Università degli Studi di Modena e Reggio Emilia, 41121 Modena, Italy
Interests: coordination; socio-technical systems; Internet of Things; self-organization; multi-agent systems; computational argumentation; AI in healthcare
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Research work done on intelligent agents and multi-agent systems has matured during the past decade, and many effective applications of this technology are currently being deployed. Although computational approaches for multi-agent systems have mainly emerged in the past few 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.

There are many reasons for the interest of researchers in this new discipline. Firstly, computational systems have gradually shifted towards 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 analysing different aspects, as well as its possible application to various domains. This review of the current state-of-the-art is not intended to make an exhaustive exploration of all the current existing works, but rather to try to give an overview of the research in agent technology, showing the high level of activity of this area.

Prof. Dr. Andrea Omicini
Dr. Stefano Mariani
Guest Editors

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Keywords

agent and multi-agent applications
agent engineering: development techniques, tools, and platforms
agent-based simulation: development techniques, tools, and platforms
biologically-inspired approaches and methods
agent-based collective intelligence
multi-agent complex systems
agent-based distributed problem solving
human–robot/agent interaction
agent-based intelligent control and manufacturing systems
learning and adaptation in multi-agent systems
methodologies for agent-based systems
multi-robot systems
negotiation and conflict resolution in multi-agent systems
norms for multi-agent systems
institutions for multi-agent systems
reasoning in agent-based systems
self-organization in multi-agent systems
single and multi-agent planning and scheduling
agent-based socio-technical systems
teamwork, team formation, teamwork analysis
trust and reputation in multi-agent systems

Published Papers (12 papers)

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Editorial

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6 pages, 1839 KiB

Open AccessEditorial

Special Issue “Multi-Agent Systems”: Editorial

by Stefano Mariani and Andrea Omicini

Appl. Sci. 2020, 10(15), 5329; https://doi.org/10.3390/app10155329 - 01 Aug 2020

Cited by 3 | Viewed by 1903

Abstract

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. Full article

(This article belongs to the Special Issue Multi-Agent Systems 2019)

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Research

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32 pages, 5055 KiB

Open AccessArticle

An Agent-Based Model Driven Decision Support System for Reactive Aggregate Production Scheduling in the Green Coffee Supply Chain

by María del Rosario Pérez-Salazar, Alberto Alfonso Aguilar-Lasserre, Miguel Gastón Cedillo-Campos, Rubén Posada-Gómez, Marco Julio del Moral-Argumedo and José Carlos Hernández-González

Appl. Sci. 2019, 9(22), 4903; https://doi.org/10.3390/app9224903 - 15 Nov 2019

Cited by 6 | Viewed by 4503

Abstract

The aim of this paper is to contribute to the thread of research regarding the need for logistic systems for planning and scheduling/rescheduling within the agro-industry. To this end, an agent-based model driven decision support system for the agri-food supply chain is presented. Inputs in this research are taken from a case example of a Mexican green coffee supply chain. In this context, the decision support agent serves the purposes of deriving useful knowledge to accomplish (i) the decision regarding the estimation of Cherry coffee yield obtained at the coffee plantation, and the Parchment coffee sample verification decision, using fuzzy logic involving an inference engine with IF-THEN type rules; (ii) the production plan establishment decision, using a decision-making rule approach based upon the coupling of IF-THEN fuzzy inference rules and equation-based representation by means of mixed integer programming with the aim to maximize customer service level; and (iii) the production plan update decision using mathematical equations once the customer service level falls below the expected level. Three scenarios of demand patterns were considered to conduct the experiments: increasing, unimodal and decreasing. We found that the input inventory and output inventory vary similar over time for the unimodal demand pattern, not the case for both the increasing and decreasing demand patterns. For the decreasing demand pattern, ten tardy orders for the initial production schedule, an 88% service level, and nineteen tardy orders from the estimated production results, a 77% service level. This value falls below the expected level. Consequently, the updated aggregate production schedule resulted in ten tardy orders and an 88% service level. Full article

(This article belongs to the Special Issue Multi-Agent Systems 2019)

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22 pages, 3494 KiB

Open AccessArticle

Situated Psychological Agents: A Methodology for Educational Games

by Michela Ponticorvo, Elena Dell’Aquila, Davide Marocco and Orazio Miglino

Appl. Sci. 2019, 9(22), 4887; https://doi.org/10.3390/app9224887 - 14 Nov 2019

Cited by 16 | Viewed by 2700

Abstract

In recent years, the ever-increasing need for valid and effective training to acquire competences in multiform contexts has led to a wide diffusion of educational games (EG). In spite of their diffusion, there is still a need to reflect on the design process that should embed the games’ pedagogical potential and the instructional process in the entertainment scope. Moreover, as building EG, especially in digital environments, is an enterprise that involves specialists with different expertise, it can be useful to have a shared methodology that is easily understandable and usable by many users. In this paper, we propose to use situated psychological agents (SPA) as a methodology to design and build effective EG and show how to represent games in terms of SPA and their interactions by diagrams and describe different examples of how this approach has been applied. Full article

(This article belongs to the Special Issue Multi-Agent Systems 2019)

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14 pages, 1058 KiB

Open AccessArticle

Woc-Bots: An Agent-Based Approach to Decision-Making

by Sean Grimes and David E. Breen

Appl. Sci. 2019, 9(21), 4653; https://doi.org/10.3390/app9214653 - 01 Nov 2019

Cited by 3 | Viewed by 2139

Abstract

We present a flexible, robust approach to predictive decision-making using simple, modular agents (WoC-Bots) that interact with each other socially and share information about the features they are trained on. Our agents form a knowledge-diverse crowd, allowing us to use Wisdom of the Crowd (WoC) theories to aggregate their opinions and come to a collective conclusion. Compared to traditional multi-layer perceptron (MLP) networks, WoC-Bots can be trained more quickly, more easily incorporate new features, and make it easier to determine why the network gives the prediction that it does. We compare our predictive accuracy with MLP networks to show that WoC-Bots can attain similar results when predicting the box office success of Hollywood movies, while requiring significantly less training time. Full article

(This article belongs to the Special Issue Multi-Agent Systems 2019)

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17 pages, 748 KiB

Open AccessArticle

ARPS: A Framework for Development, Simulation, Evaluation, and Deployment of Multi-Agent Systems

by Thiago Coelho Prado and Michael Bauer

Appl. Sci. 2019, 9(21), 4483; https://doi.org/10.3390/app9214483 - 23 Oct 2019

Cited by 1 | Viewed by 3413

Abstract

Multi-Agent Systems (MASs) are often used to optimize the use of the resources available in an environment. A flaw during the modelling phase or an unanticipated scenario during their execution, however, can make the agents behave not as planned. As a consequence, the resources can be poorly utilized and operate sub-optimized, but it can also bring the resources into an unexpected state. Such problems can be mitigated if there is a controlled environment to test the agents’ behaviour before deployment. To this end, a simulated environment provides not only a way to test the agents’ behaviour under different common scenarios but test them as well in adverse and rare state conditions. With this in mind, we have developed ARPS, an open-source framework that can be used to design computational agents, evaluate them in a simulated environment modelled after a real one, and then deploy and manage them seamlessly in the actual environment when the results of their evaluation are satisfactory. Full article

(This article belongs to the Special Issue Multi-Agent Systems 2019)

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25 pages, 2209 KiB

Open AccessArticle

Scale-Free Features in Collective Robot Foraging

by Ilja Rausch, Yara Khaluf and Pieter Simoens

Appl. Sci. 2019, 9(13), 2667; https://doi.org/10.3390/app9132667 - 30 Jun 2019

Cited by 8 | Viewed by 3126

Abstract

In many complex systems observed in nature, properties such as scalability, adaptivity, or rapid information exchange are often accompanied by the presence of features that are scale-free, i.e., that have no characteristic scale. Following this observation, we investigate the existence of scale-free features in artificial collective systems using simulated robot swarms. We implement a large-scale swarm performing the complex task of collective foraging, and demonstrate that several space and time features of the simulated swarm—such as number of communication links or time spent in resting state—spontaneously approach the scale-free property with moderate to strong statistical plausibility. Furthermore, we report strong correlations between the latter observation and swarm performance in terms of the number of retrieved items. Full article

(This article belongs to the Special Issue Multi-Agent Systems 2019)

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38 pages, 1097 KiB

Open AccessArticle

Towards Agent Organizations Interoperability: A Model Driven Engineering Approach

by Luciano R. Coutinho, Anarosa A. F. Brandão, Olivier Boissier and Jaime S. Sichman

Appl. Sci. 2019, 9(12), 2420; https://doi.org/10.3390/app9122420 - 13 Jun 2019

Cited by 5 | Viewed by 3141

Abstract

In the research and development of multiagent systems (MAS), one of the central issues is how to conciliate the autonomy of the agents with a desirable and stable behavior of the MAS as a whole. Agent organizations have been proposed as a suitable metaphor for engineering social order in MAS. However, this emphasis has led to several proposals of organizational models for MAS design, thus creating an organizational interoperability problem: How to ensure that agents, possibly designed to work with different organizational models, could interact and collectively solve problems? In this paper, we have adopted techniques from Model Driven Engineering to handle this problem. In particular, we propose an abstract and integrated view of the main concepts that have been used to specify agent organizations, based on several organizational models present in the literature. We apply this integrated view to design MAORI, a model-based architecture for organizational interoperability. We present a MAORI application example that has shown that our approach is computationally feasible, enabling agents endowed with heterogeneous organizational models to cooperatively solve a problem. Full article

(This article belongs to the Special Issue Multi-Agent Systems 2019)

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24 pages, 671 KiB

Open AccessArticle

Improving Computational Efficiency in Crowded Task Allocation Games with Coupled Constraints

by Ming Chong Lim and Han-Lim Choi

Appl. Sci. 2019, 9(10), 2117; https://doi.org/10.3390/app9102117 - 24 May 2019

Cited by 6 | Viewed by 2290

Abstract

Multi-agent task allocation is a well-studied field with many proven algorithms. In real-world applications, many tasks have complicated coupled relationships that affect the feasibility of some algorithms. In this paper, we leverage on the properties of potential games and introduce a scheduling algorithm to provide feasible solutions in allocation scenarios with complicated spatial and temporal dependence. Additionally, we propose the use of random sampling in a Distributed Stochastic Algorithm to enhance speed of convergence. We demonstrate the feasibility of such an approach in a simulated disaster relief operation and show that feasibly good results can be obtained when the confirmation and sample size requirements are properly selected. Full article

(This article belongs to the Special Issue Multi-Agent Systems 2019)

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21 pages, 411 KiB

Open AccessArticle

Merging Observed and Self-Reported Behaviour in Agent-Based Simulation: A Case Study on Photovoltaic Adoption

by Andrea Borghesi and Michela Milano

Appl. Sci. 2019, 9(10), 2098; https://doi.org/10.3390/app9102098 - 22 May 2019

Cited by 6 | Viewed by 2231

Abstract

Designing and evaluating energy policies is a difficult challenge because the energy sector is a complex system that cannot be adequately understood without using models merging economic, social and individual perspectives. Appropriate models allow policy makers to assess the impact of policy measures, satisfy strategic objectives and develop sustainable policies. Often the implementation of a policy cannot be directly enforced by governments, but falls back to many stakeholders, such as private citizens and enterprises. We propose to integrate two basic cornerstones to devise realistic models: the self-reported behaviour, derived from surveys, and the observed behaviour, from historical data. The self-reported behaviour enables the identification of drivers and barriers pushing or limiting people in their decision making process, while the observed behaviour is used to tune these drivers/barriers in a model. We test our methodology on a case-study: the adoption of photovoltaic panels among private citizens in the Emilia–Romagna region, Italy. We propose an agent-based model devised using self-reported data and then empirically tuned using historical data. The results reveal that our model can predict with great accuracy the photovoltaic (PV) adoption rate and thus support the energy policy-making process. Full article

(This article belongs to the Special Issue Multi-Agent Systems 2019)

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25 pages, 6555 KiB

Open AccessArticle

Spatiotemporal Modeling of the Smart City Residents’ Activity with Multi-Agent Systems

by Robert Olszewski, Piotr Pałka, Agnieszka Turek, Bogna Kietlińska, Tadeusz Płatkowski and Marek Borkowski

Appl. Sci. 2019, 9(10), 2059; https://doi.org/10.3390/app9102059 - 19 May 2019

Cited by 13 | Viewed by 3648

Abstract

The article proposes the concept of modeling that uses multi-agent systems of mutual interactions between city residents as well as interactions between residents and spatial objects. Adopting this perspective means treating residents, as well as buildings or other spatial objects, as distinct agents that exchange multifaceted packages of information in a dynamic and non-linear way. The exchanged information may be reinforced or diminished during the process, which may result in changing the social activity of the residents. Utilizing Latour’s actor–network theory, the authors developed a model for studying the relationship between demographic and social factors, and the diversified spatial arrangement and the structure of a city. This concept was used to model the level of residents’ trust spatiotemporally and, indirectly, to study the level of social (geo)participation in a smart city. The devised system, whose test implementation as an agent-based system was done in the GAMA: agent-based, spatially explicit, modeling and simulation platform, was tested on both model and real data. The results obtained for the model city and the capital of Poland, Warsaw, indicate the significant and interdisciplinary analytical and scientific potential of the authorial methodology in the domain of geospatial science, geospatial data models with multi-agent systems, spatial planning, and applied social sciences. Full article

(This article belongs to the Special Issue Multi-Agent Systems 2019)

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16 pages, 4722 KiB

Open AccessArticle

Consensus Algorithms Based Multi-Robot Formation Control under Noise and Time Delay Conditions

by Heng Wei, Qiang Lv, Nanxun Duo, GuoSheng Wang and Bing Liang

Appl. Sci. 2019, 9(5), 1004; https://doi.org/10.3390/app9051004 - 11 Mar 2019

Cited by 18 | Viewed by 3353

Abstract

In recent years, the formation control of multi-mobile robots has been widely investigated by researchers. With increasing numbers of robots in the formation, distributed formation control has become the development trend of multi-mobile robot formation control, and the consensus problem is the most basic problem in the distributed multi-mobile robot control algorithm. Therefore, it is very important to analyze the consensus of multi-mobile robot systems. There are already mature and sophisticated strategies solving the consensus problem in ideal environments. However, in practical applications, uncertain factors like communication noise, communication delay and measurement errors will still lead to many problems in multi-robot formation control. In this paper, the consensus problem of second-order multi-robot systems with multiple time delays and noises is analyzed. The characteristic equation of the system is transformed into a quadratic polynomial of pure imaginary eigenvalues using the frequency domain analysis method, and then the critical stability state of the maximum time delay under noisy conditions is obtained. When all robot delays are less than the maximum time delay, the system can be stabilized and achieve consensus. Compared with the traditional Lyapunov method, this algorithm has lower conservativeness, and it is easier to extend the results to higher-order multi-robot systems. Finally, the results are verified by numerical simulation using MATLAB/Simulink. At the same time, a multi-mobile robot platform is built, and the proposed algorithm is applied to an actual multi-robot system. The experimental results show that the proposed algorithm is finally able to achieve the consensus of the second-order multi-robot system under delay and noise interference. Full article

(This article belongs to the Special Issue Multi-Agent Systems 2019)

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Other

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19 pages, 1393 KiB

Open AccessProject Report

A Multi-Agent Based Intelligent Training System for Unmanned Surface Vehicles

by Wei Han, Bing Zhang, Qianyi Wang, Jun Luo, Weizhi Ran and Yang Xu

Appl. Sci. 2019, 9(6), 1089; https://doi.org/10.3390/app9061089 - 15 Mar 2019

Cited by 13 | Viewed by 3144

Abstract

The modeling and design of multi-agent systems is imperative for applications in the evolving intelligence of unmanned systems. In this paper, we propose a multi-agent system design that is used to build a system for training a team of unmanned surface vehicles (USVs) where no historical data concerning the behavior is available. In this approach, agents are built as the physical controller of each USV and their cooperative decisions used for the USVs’ group coordination. To make our multi-agent system intelligently coordinate USVs, we built a multi-agent-based learning system. First, an agent-based data collection platform is deployed to gather competition data from agents’ observation for on-line learning tasks. Second, we design a genetic-based fuzzy rule training algorithm that is capable of optimizing agents’ coordination decisions in an accumulated manner. The simulation results of this study demonstrate that our proposed training approach is feasible and able to converge to a stable action selection policy towards efficient multi-USVs’ cooperative decision making. Full article

(This article belongs to the Special Issue Multi-Agent Systems 2019)

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