Self-organizing Multi-agent Systems Research Articles

Robot swarms as embodied extensions of humans

In order to harness the potential of robot swarms, humans must be able to control decentralized and self-organizing multi-agent systems. The field of human-swarm interaction investigates how humans with their centralized information and control demands can be paired with the decentralized architectures of robot swarms. Recently, we have proposed an architecture which aims at balancing the trade-offs between centralized and decentralized control and communication in human-swarm interaction while joining the relative capabilities of human cognition and swarm intelligence. In this article, we expand on this architecture by discussing a list of design requirements that must be met in order to make robot swarms embodied extensions of humans that act and feel like natural body parts. We also conceptually show how some of the design requirements could be implemented by simple rules when we take the world’s structure into account.

Somas-Iot; an Anfis Enabled Middleware to Utilize Self-Organizing Multi-Agent Systems to Manage Internet of Things

Somas-Iot; an Anfis Enabled Middleware to Utilize Self-Organizing Multi-Agent Systems to Manage Internet of Things

A lobster-inspired multi-robot control strategy for monitoring non-stationary concentration fields

We propose a new lobster-inspired chemotaxis decentralized control strategy for monitoring a non-stationary concentration field using a team of nonholonomic mobile robots. The task of the team is to locate and trace the movement of the point (or points) with the highest field value (i.e. source), provided that the robots are not aware of the dynamics of the field and can only periodically sample the field at their locations. As an example of the concentration field we consider a population of biological species modeled by a self-organizing multi-agent system with agents acting as individuals of the population in accordance with some flocking rules. The proposed strategy combines the lobsters’ plume localization behavior and flocking mechanisms to efficiently solve the problem even with a small group of robots. Simulations and experimental works on physical unicycle robots are performed to validate the efectiveness of the approach for the cases of non-stationary fields.

A Self-Organizing Multi-Agent System for Distributed Voltage Regulation

This paper presents a distributed voltage regulation method based on multi-agent system control and network self-organization for a large distribution network. The network autonomously organizes itself into small subnetworks through the epsilon decomposition of the sensitivity matrix, and agents group themselves into these subnetworks with the communication links being autonomously determined. Each subnetwork controls its voltage by locating the closest local distributed generation and optimizing their outputs. This simplifies and reduces the size of the optimization problem and the interaction requirements. This approach also facilitates adaptive grouping of the network by self-reorganizing to maintain a stable state in response to time-varying network requirements and changes. The effectiveness of the proposed approach is validated through simulations on a model of a real heavily-meshed secondary distribution network. Simulation results and comparisons with other methods demonstrate the ability of the subnetworks to autonomously and independently regulate the voltage and to adapt to unpredictable network conditions over time, thereby enabling autonomous and flexible distribution networks.

Tendencies in Multi-Agent Systems: A Systematic Literature Review

The application of Artificial Intelligence mechanisms allows the development of systems capable to solve very complex engineering problems. Multi-agent systems (MAS) are one paradigm that allows an alternative way to design distributed control systems. While research in this area grew exponentially before 2009, there is a need to understand the status quo of the field from 2009 to June 2017. An extension of the results of a SLR related to Multi-Agent Systems, its applications and research gaps, following Kitchenham and Wholin guidelines are presented in this paper. From the analysis of 279 papers (out of 3522 candidates), our findings suggest that: a) there were 20 gaps related to agent-oriented methodologies; coordination, cooperation and negotiation; modelling, developing, testing and debugging; b) 24 gaps related to specific domains (recycling, dynamic evacuation, hazard management, health-care, industry, logistics and manufacturing, machine learning, ambient assisted living); and 14 gaps related to specific areas within MAS (A-Teams, dynamic MAS and mobile agents, ABMS, evolutionary MAS, and self-organizing MAS). These gaps specify lines of research where the MAS community must work to achieve the unification of the agent-oriented paradigm; as well as strengthen ties with the industry.

Oscillatory evolution of collective behavior in evolutionary games played with reinforcement learning

Large-scale cooperation underpins the evolution of ecosystems and the human society, and the collective behaviors by self-organization of multi-agent systems are the key for understanding. As artificial intelligence (AI) prevails in almost all branches of science, it would be of great interest to see what new insights of collective behaviors could be obtained from a multi-agent AI system. Here, we introduce a typical reinforcement learning (RL) algorithm—Q-learning into evolutionary game dynamics, where agents pursue optimal action on the basis of the introspectiveness rather than the outward manner such as the birth–death or imitation processes in the traditional evolutionary game (EG). We investigate the cooperation prevalence numerically for a general $$2\times 2$$ game setting. We find that the cooperation prevalence in the multi-agent AI is unexpectedly of equal level as in the traditional EG in most cases. However, in the snowdrift games with RL, we reveal that explosive cooperation appears in the form of periodic oscillation, and we study the impact of the payoff structure on its emergence. Finally, we show that the periodic oscillation can also be observed in some other EGs with the RL algorithm, such as the rock–paper–scissors game. Our results offer a reference point to understand the emergence of cooperation and oscillatory behaviors in nature and society from AI’s perspective.

A Metadata-Driven Approach for Testing Self-Organizing Multiagent Systems

Multiagent Systems (MASs) have multiple different characteristics, such as autonomy, and asynchronous and social features, which make these systems difficult to understand. Thus, there is a lack of procedures guaranteeing that multiagent systems once implemented would behave as desired. Determining the reliability of such systems is further complicated by the fact that current agent-based approaches may also involve non-deterministic characteristics, such as learning, self-adaptation and self-organization (SASO). Nonetheless, there is a gap in the literature regarding the testing of systems with these features. This paper presents an approach based on metadata and the publish-subscribe paradigm to develop test applications that address the process of failure diagnosis in a self-organizing MAS. The novelty of the proposed approach involves its ability to test self-organizing MAS systems in the context of local and global behavior. To illustrate the use of this approach, we developed a self-organizing MAS system based on the Internet of Things (IoT), which simulates a set of smart street lights, and we performed functional ad-hoc tests. The street lights need to interact with each other in order to achieve the global goals of reducing energy consumption and maintaining the maximum value of visual comfort in illuminated areas. To achieve these global behaviors, the street lights develop local behaviors automatically through a self-organizing process based on machine-learning algorithms.

A self-organising multi-agent system for decentralised forensic investigations

As network-based threats continue to evolve more rapidly, detecting and responding to intrusion attempts in real-time requires an increasingly automated and intelligent response. This paper provides an agent-based framework for the analysis of cyber events within networks of varying sizes to detect complex multi-stage attacks. Agents are used as intelligent systems to explore domain specific and situational information showing the benefit of adaptive technologies that proactively analyse security events in real time. We introduce several algorithms to encapsulate and manage the traditional detection technologies and provide agent-based performance introspection as a mechanism to identify poorly performing systems. Our evaluation shows that the algorithms can reduce the amount of processing needed to analyse a security event by over 50% and improve the detection rate by up to 20% by introducing corrective systems to reduce false alarm rates in error-prone environments.

Role-Based Modeling for Designing Agent Behavior in Self-Organizing Multi-Agent Systems

Self-organization has been proposed to be implemented in complex systems which require the automation capabilities to govern itself and to adapt upon changes. Self-organizing systems can be modeled as multi-agent systems (MAS) since they share common characteristics in that they consist of multiple autonomous systems. However, most existing MAS engineering methodologies do not fully support self-organizing systems design since they require predefined goals and agent behaviors, which is not the case in self-organizing systems. Another feature that is currently not supported for designing self-organizing MAS is the separation between the design of agent behaviors and behavior adaptation, i.e. how agents adapt their behaviors to respond upon changes. To tackle these issues, this paper proposes a role modeling method, in which agent behaviors are represented as roles, to design how agents perform behavior adaptation at runtime by switching between roles. The applicability of the proposed role modeling method is evaluated in a case study of a self-organizing smart transportation system.

Electronic Social Capital for Self-Organising Multi-Agent Systems

It is a recurring requirement in open systems, such as networks, distributed systems, and socio-technical systems, that a group of agents must coordinate their behaviour for the common good. In those systems—where agents are heterogeneous—unexpected behaviour can occur due to errors or malice. Agents whose practices free-ride the system can be accepted to a certain level; however, not only do they put the stability of the system at risk, but they also compromise the agents that behave according to the system’s rules. In social systems, it has been observed that social capital is an attribute of individuals that enhances their ability to solve collective action problems. Sociologists have studied collective action through human societies and observed that social capital plays an important role in maintaining communities though time as well as in simplifying the decision-making in them. In this work, we explore the use of Electronic Social Capital for optimising self-organised collective action. We developed a context-independent Electronic Social Capital framework to test this hypothesis. The framework comprises a set of handlers that capture events from the system and update three different forms of social capital: trustworthiness, networks, and institutions. Later, a set of metrics are generated by the forms of social capital and used for decision-making. The framework was tested in different scenarios such as two-player games, n -player games, and public goods games. The experimental results show that social capital optimises the outcomes (in terms of long-term satisfaction and utility), reduces the complexity of decision-making, and scales with the size of the population. This work proposes an alternative solution using Electronic Social Capital to represent and reason with qualitative, instead of traditional quantitative, values. This solution could be embedded into socio-technical systems to incentivise collective action without commodifying the resources or actions in the system.

Self-organizing multi-agent systems for the control of complex systems

Because of the law of requisite variety, designing a controller for complex systems implies designing a complex system. In software engineering, usual top-down approaches become inadequate to design such systems. The Adaptive Multi-Agent Systems (AMAS) approach relies on the cooperative self-organization of autonomous micro-level agents to tackle macro-level complexity. This bottom-up approach provides adaptive, scalable, and robust systems. This paper presents a complex system controller that has been designed following this approach, and shows results obtained with the automatic tuning of a real internal combustion engine.

Self-Organization in Multi-Agent Systems Based on Examples of Modeling Economic Relationships between Agents

The goal of the research was to observe and analyze self-organization patterns in Multi-Agent Systems (MAS) by modeling basic economic relationships between agents. The paper describes the worked-out MAS including the example of a production cycle and used economic rules. A special focus is put on behavior rules and decision systems of an individual agent as product advertising, price & purchase negotiations, deals in the context of limited resources and time constraints to make a decision. The MAS was implemented in a dedicated C#-based environment and a number of simulations were carried out. The paper reports some of recorded self-organization patterns and their dynamics over time in terms of their spatial arrangement or mutual relations (e.g.: bargain price, market shares, etc.), and provides their analysis and discussion.

Combining PosoMAS Method Content with Scrum: Agile Software Engineering for Open Self-Organising Systems

In this paper we discuss how to combine the method content from PosoMAS, the Process for open, self-organising Multi-Agent Systems, with the agile iterative-incremental life cycle of Scrum. The result is an agile software engineering methodology tailored to open self-organising systems. We show how the methodology has been applied in a development project and discuss the lessons learned. Finally, we compare the Scrum version of PosoMAS to other agile agent-oriented software engineering methodologies and address the selection of a suitable process.

Towards smart factory for industry 4.0: a self-organized multi-agent system with big data based feedback and coordination

The proliferation of cyber-physical systems introduces the fourth stage of industrialization, commonly known as Industry 4.0. The vertical integration of various components inside a factory to implement a flexible and reconfigurable manufacturing system, i.e., smart factory, is one of the key features of Industry 4.0. In this paper, we present a smart factory framework that incorporates industrial network, cloud, and supervisory control terminals with smart shop-floor objects such as machines, conveyers, and products. Then, we provide a classification of the smart objects into various types of agents and define a coordinator in the cloud. The autonomous decision and distributed cooperation between agents lead to high flexibility. Moreover, this kind of self-organized system leverages the feedback and coordination by the central coordinator in order to achieve high efficiency. Thus, the smart factory is characterized by a self-organized multi-agent system assisted with big data based feedback and coordination. Based on this model, we propose an intelligent negotiation mechanism for agents to cooperate with each other. Furthermore, the study illustrates that complementary strategies can be designed to prevent deadlocks by improving the agents’ decision making and the coordinator's behavior. The simulation results assess the effectiveness of the proposed negotiation mechanism and deadlock prevention strategies.

A Survey of Self-Organization Mechanisms in Multiagent Systems

This paper surveys the literature over the last decades in the field of self-organizing multiagent systems. Self-organization has been extensively studied and applied in multiagent systems and other fields, e.g., sensor networks and grid systems. Self-organization mechanisms in other fields have been thoroughly surveyed. However, there has not been a survey of self-organization mechanisms developed for use in multiagent systems. In this paper, we provide a survey of existing literature on self-organization mechanisms in multiagent systems. We also highlight the future work on key research issues in multiagent systems. This paper can serve as a guide and a starting point for anyone who will conduct research on self-organization in multiagent systems. Also, this paper complements existing survey studies on self-organization in multiagent systems.

A stepwise refinement-based development of self-organising multi-agent systems: application to the foraging ants

This paper proposes a formal modelling for self-organising multi-agent systems SOMAS based on stepwise refinements, with the Event-B language and the temporal logic of actions TLA. This modelling allows to develop this kind of systems in a more structured manner. In addition, it enables to reason, in a rigorous way, about the correctness of the derived models both at the local level and the global level. Our work is illustrated by the foraging ants case study.

A stepwise refinement-based development of self-organising multi-agent systems: application to the foraging ants

A stepwise refinement-based development of self-organising multi-agent systems: application to the foraging ants

A hybrid approach for Structural Monitoring with self-organizing multi-agent systems and inverse numerical methods in material-embedded sensor networks

One of the major challenges in Structural Monitoring of mechanical structures is the derivation of meaningful information from sensor data. This work investigates a hybrid data processing approach for material-integrated structural health and load monitoring systems by using self-organizing mobile multi-agent systems (MAS), and inverse numerical methods providing the spatial resolved load information from a set of sensors embedded in the technical structure with low-resource agent processing platforms scalable to microchip level, enabling material-integrated real-time sensor systems. The MAS is deployed in a heterogeneous environment and offers event-based sensor preprocessing, distribution, and pre-computation. Inverse numerical approaches usually require a large amount of computational power and storage resources, not suitable for resource constrained sensor node implementations. Instead, the computation is partitioned into spatial off-line (outside the network) and on-line parts, with on-line sensor processing performed by the agent system. A unified multi-domain simulation framework is used to profile and validate the proposed approach.

Self-Organising Electronic Institutions and Flexible Manufacturing Systems

This paper addresses the issue of organised adaptation in flexible manufacturing systems using multi-agent systems and electronic institutions. We analyse two representative case studies, the first a reconfigurable manufacturing system for intelligent ‘plug and produce’, and the second an evolvable assembly system that changes over time as context and requirements change. We observe that the management of adaptation is typically the responsibility of intelligent agents operating in a multi-agent system, but that dealing with openness, selfdetermination or fulfilment and violation of contracts requires conventional, mutually-agreed and mutable rules. We therefore propose to use the computational framework of self-organising electronic institutions as one way to represent solutions to these problems. However, issues of scale, complexity and uncertainty also need to be addressed, and we discuss how the framework can be developed to address these research challenges. We conclude with some comments on the potential transformative impact of self-organising multi-agent systems on reconfigurable manufacturing and evolvable assembly systems.

A SELF-ORGANIZED MULTI AGENT DECISION MAKING SYSTEM BASED ON FUZZY PROBABILITIES: THE CASE OF APHASIA DIAGNOSIS

Aphasia diagnosis is a challenging medical diagnostic task due to the linguistic uncertainty and vagueness, large number of measurements with imprecision, inconsistencies in the denition of Aphasic syndromes, natural diversity and subjectivity in test objects as well as in options of experts who diagnose the disease. In this paper we present a new self-organized multi agent system that diagnoses dierent types of Aphasia based on fuzzy probabilities. In the proposed multi agent system, the characteristic of self organization is employed as both a decision making feature selection paradigm as well as a mechanism to estimate the probability mass functions of Aphasia factors. The estimated probability mass functions are involved in fuzzy probability calcu- lation of dierent types of Aphasia. The performance and robustness of the proposed method is compared with several earlier approaches. While the pro- posed method requires more of the available test parameters, the comparison clearly shows the superiority of the proposed method in terms of accuracy as well as robustness.