Achieving Mobile Agent Systems interoperability through software layering

Interoperability is a key issue for a wider adoption of mobile agent systems (MASs) in heterogeneous and open distributed environments where agents, in order to fulfill their tasks, must interact with non-homogeneous agents and traverse different agent platforms to access remote resources. To date, while several approaches have been proposed to deal with different aspects of MAS interoperability, they all lack the necessary flexibility to provide an adequate degree of interoperability among the currently available MASs. In this paper, we propose an application-level approach grounded in the software layering concept, which enables execution, migration and communication interoperability between Java-based mobile agent systems, thus overcoming major setbacks affecting the other approaches currently proposed for supporting MAS interoperability. In particular, we define a Java-based framework, named JIMAF, which relies on an event-driven, proxy-based mobile agent model and supports interoperable mobile agents which can be easily coded and adapted to existing MASs without any modifications of the MAS infrastructures. Results from the performance evaluation of MAS interoperability was carried by using JIMAF atop Aglets, Ajanta, Grasshopper, and Voyager, demonstrating that the high-level JIMAF approach offers high efficacy while maintaining overhead at acceptable levels for target computing environments.     

Robust design of multi-agent system interactions: A testing approach based on pattern matching

The definition of protocols between agents is not enough for guaranteeing the absence of undesirable communication in organizations and the presence of desirable ones in large multi-agent systems (MASs). This is a consequence of the complex system nature of MASs, which cause sophisticated behaviors to arise out of a multiplicity of relatively simple interactions among the independent agents composing them. With this motivation, this paper presents an approach for testing communication in MAS architectures. In this approach, designers are not only recommended to specify the desired communication protocols, but also the undesired patterns and organization structures in the agents’ communications, allowing designers to define robust communication structures. For this purpose, this work presents (1) a language to define such patterns; (2) a set of already defined desired and undesired patterns which usually appear in general MASs; (3) a tool that allows developers to automatically detect these patterns in logs of MAS executions; and (4) a guideline that takes developers through the testing of the communications in MASs. The current approach is experienced with a case study, and the results show that the application of the current approach and the suppression of detected undesired patterns improve the effectiveness and efficiency of the corresponding MAS.     

Evaluating fault tolerance approaches in multi-agent systems

A multi-agent system (MAS) is a distributed system that consists of multiple agents working together to solve mutual problems. Even though MASs are well suited for the development of complex distributed systems, the number of real-world usages is still small. One of the main reasons for this is that MASs are very fragile. In a typical, large-scale MAS, the rate of failure grows with the number of hosts, the number of deployed agents, and the duration of the agent’s task execution. For this reason, numerous approaches have been introduced to deal with aspects of failure handling. However, the absence of centralized control and a large number of individual intelligent components makes it difficult to detect and treat errors. The risk of uncontrollable fault propagation is high and can seriously impact on system performance. There are two important factors that limit the usage of MASs: (1) existing fault tolerance (FT) approaches are not generic, as they focus on and improve specific issues of FT; and (2) despite the plethora of available FT approaches and theories, there is a remarkable lack of general metrics, tools, benchmarks, and experimental methods for formal validation and comparison of existing or newly developed FT approaches. As FT approaches in MASs become a well-established field, the need for generalized, standardized evaluation of FT approaches emerges as imperative. In this paper, we first present a detailed overview of existing FT solutions, approaches, and techniques in agent platform hosted MASs. From that overview, we derive the commonalities in existing research. Next, we present the main contribution of our paper: an evaluation methodology, with a set of metrics, for comparing FT approaches in MASs. We adopt an engineering perspective on the problem, defining a methodology and metrics that are both implementation- and domain-independent. The metrics are formalized with an acyclic directed graph. By using our methodology, evaluators can select an appropriate FT approach for targeted MAS application, thus improving MAS usability, stability, and development speed. In order to show the viability of our approach, a case study that compares two FT approaches for a targeted MAS is presented. The case study results show that our methodology can be used for selecting an appropriate FT approach for the targeted MAS.     

A collection of method fragments automated with model transformations in agent-oriented modeling

The Foundation for Intelligent Physical Agents (FIPA) Methodology Technical Committee recommends the method engineering approach for Multi-agent System (MAS) development. This approach consists in defining method fragments for agent-oriented software engineering, and then developers combine these fragments to define customized development processes. In this line of research, the contribution of this paper is a collection of novel method fragments that are assisted with model transformations, in order to allow engineers to better develop MASs in an automated way. Some of these fragments also include explicit advices for incorporating artificial intelligence in the development. In the experiments, two processes have been composed with these method fragments for two different agent-oriented methodologies (i.e. Ingenias and Adelfe). This paper shows how these processes are respectively used to develop two known MASs (i.e. Delphi MAS and Timetable MAS). Furthermore, the presented approach is evaluated with 24 developers that were randomly divided into two groups. Each developer designed a MAS satisfying the same common requirements. One group applied the current approach, while the other group did not. The results show that this approach reduces the 63.3% of the designing time in average forMASs.     

A systematic approach to evaluating domain-specific modeling language environments for multi-agent systems

Multi-agent systems (MASs) include multiple interacting agents within an environment to provide a solution for complex systems that cannot be easily solved with individual agents or monolithic systems. However, the development of MASs is not trivial due to the various agent properties such as autonomy, responsiveness, and proactiveness, and the need for realization of the many different agent interactions. To support the development of MASs various domain-specific modeling languages (DSMLs) have been introduced that provide a declarative approach for modeling and supporting the generation of agent-based systems. To be effective, the proposed DSMLs need to meet the various stakeholder concerns and the related quality criteria for the corresponding MASs. Unfortunately, very often the evaluation of the DSML is completely missing or has been carried out in idiosyncratic approach. If the DSMLs are not well defined, then implicitly this will have an impact on the quality of the MASs. In this paper, we present an evaluation framework and systematic approach for assessing existing or newly defined DSMLs for MASs. The evaluation is specific for MAS DSMLs and targets both the language and the corresponding tools. To illustrate the evaluation approach, we first present SEA_ML, which is a model-driven MAS DSML for supporting the modeling and generation of agent-based systems. The evaluation of SEA_ML is based on a multi-case study research approach and provides both qualitative evaluation and quantitative analysis. We report on the lessons learned considering the adoption of the evaluation approach as well as the SEA_ML for supporting the generation of agent-based systems.     

Agents in object‐oriented software engineering

Software engineers of multi‐agent systems (MASs) are faced with different concerns such as autonomy, adaptation, interaction, collaboration, learning, and mobility, which are essentially different from classical concerns addressed in object‐oriented software engineering. MAS developers, however, have relied mostly on object‐oriented design techniques and programming languages, such as Java. This often leads to a poor separation of MAS concerns and in turn to the production of MASs that are difficult to maintain and reuse. This paper discusses software engineering approaches for MASs, and presents a new method for integrating agents into object‐oriented software engineering from an early stage of design. The proposed approach encourages the separate handling of MAS concerns, and provides a disciplined scheme for their composition. Our proposal explores the benefits of aspect‐oriented software development for the incorporation of agents into object‐oriented systems. We also illustrate our aspect‐oriented approach through the Portalware multi‐agent system, a Web‐based environment for the development of e‐commerce portals. Copyright © 2004 John Wiley & Sons, Ltd.     

The RETSINA MAS Infrastructure

RETSINA is an implemented Multi-Agent System infrastructure that has been developed for several years and applied in many domains ranging from financial portfolio management to logistic planning. In this paper, we distill from our experience in developing MASs to clearly define a generic MAS infrastructure as the domain independent and reusable substratum that supports the agents' social interactions. In addition, we show that the MAS infrastructure imposes requirements on an individual agent if the agent is to be a member of a MAS and take advantage of various components of the MAS infrastructure. Although agents are expected to enter a MAS and seamlessly and effortlessly interact with the agents in the MAS infrastructure, the current state of the art demands agents to be programmed with the knowledge of what infrastructure they will utilize, and what are various fall-back and recovery mechanisms that the infrastructure provides. By providing an abstract MAS infrastructure model and a concrete implemented instance of the model, RETSINA, we contribute towards the development of principles and practice to make the MAS infrastructure invisible and ubiquitous to the interacting agents.     

A recursive genetic framework for evolutionary decision-making in problems with high dynamism

Communication and coordination are the main cores for reaching a constructive agreement among multi-agent systems (MASs). Dividing the overall performance of MAS to individual agents may lead to group learning as opposed to individual learning, which is one of the weak points of MASs. This paper proposes a recursive genetic framework for solving problems with high dynamism. In this framework, a combination of genetic algorithm and multi-agent capabilities is utilised to accelerate team learning and accurate credit assignment. The argumentation feature is used to accomplish agent learning and the negotiation features of MASs are used to achieve a credit assignment. The proposed framework is quite general and its recursive hierarchical structure could be extended. We have dedicated one special controlling module for increasing convergence time. Due to the complexity of blackjack, we have applied it as a possible test bed to evaluate the system’s performance. The learning rate of agents is measured as well as their credit assignment. The analysis of the obtained results led us to believe that our robust framework with the proposed negotiation operator is a promising methodology to solve similar problems in other areas with high dynamism.     

Beast methodology: An agile testing methodology for multi-agent systems based on behaviour driven development

This paper presents a testing methodology to apply Behaviour Driven Development (BDD) techniques while developing Multi-Agent Systems (MASs), termed BEhavioural Agent Simple Testing (BEAST) Methodology. This methodology is supported by the open source framework (BEAST Tool) which automatically generates test cases skeletons from BDD scenarios specifications. The developed framework allows the testing of MASs based on JADE or JADEX platforms. In addition, this framework offers a set of configurable Mock Agents with the aim of being able to execute tests while the MAS is under development. The BEAST Methodology presents transparent traceability from user requirements to test cases. Thus, the stakeholders can be aware of the project status. The methodology and the associated tool have been validated in the development of a MAS for fault diagnosis in FTTH (Fiber To The Home) networks. The results have been measured in quantifiable way obtaining a reduction of the tests implementation time.     

Predicting the Expected Behavior of Agents that Learn About Agents: The CLRI Framework

We describe a framework and equations used to model and predict the behavior of multi-agent systems (MASs) with learning agents. A difference equation is used for calculating the progression of an agent's error in its decision function, thereby telling us how the agent is expected to fare in the MAS. The equation relies on parameters which capture the agent's learning abilities, such as its change rate, learning rate and retention rate, as well as relevant aspects of the MAS such as the impact that agents have on each other. We validate the framework with experimental results using reinforcement learning agents in a market system, as well as with other experimental results gathered from the AI literature. Finally, we use PAC-theory to show how to calculate bounds on the values of the learning parameters.     

Quadratic stabilisability of multi-agent systems under switching topologies

This paper addresses the stabilisability of multi-agent systems (MASs) under switching topologies. Necessary and/or sufficient conditions are presented in terms of graph topology. These conditions explicitly reveal how the intrinsic dynamics of the agents, the communication topology and the external control input affect stabilisability jointly. With the appropriate selection of some agents to which the external inputs are applied and the suitable design of neighbour-interaction rules via a switching topology, an MAS is proved to be stabilisable even if so is not for each of uncertain subsystem. In addition, a method is proposed to constructively design a switching rule for MASs with norm-bounded time-varying uncertainties. The switching rules designed via this method do not rely on uncertainties, and the switched MAS is quadratically stabilisable via decentralised external self-feedback for all uncertainties. With respect to applications of the stabilisability results, the formation control and the cooperative tracking control are addressed. Numerical simulations are presented to demonstrate the effectiveness of the proposed results.     

Debugging complex software systems by means of pathfinder networks

This paper introduces a new methodology based on the use of Pathfinder networks (PFNETs) for the debugging of multi-agent systems (MASs). This methodology is specifically designed to develop a forensic analysis (i.e. a debugging process performed on previously recorded data of the MAS run) of MASs showing complex tissues of relationships between agents (i.e. a high complexity in their social level). Like previous works in the field of forensic analysis of MASs, our approach is performed by considering displays of the system activity which aim to be understandable by human beings. These displays allow us to understand the social behavior of the system, discover emergent behaviors, and debug possible undesirable behaviors. However, it is well known that the visualization of information in a humanly comprehensible way becomes a complex task when large amounts of information have to be represented, as is the case of the social behavior of large-scale MASs. Our methodology tackles this problem through the use of PFNETs, which are considered to reduce the data complexity in order to obtain simple representations that show only the most important global interactions in the system. In addition, the proposed methodology is customizable thanks to the use of two thresholds allowing the user to define the desired specificity level in the display. The proposal is illustrated with a detailed case study considering a complex customer-seller MAS.     

Towards the integration of the agent-oriented modeling diversity with a powertype-based language

Multi-agent Systems (MASs) are one of the main fields of distributed systems. MASs are based on autonomous entities that cooperate obtaining emergent behaviors, and can be useful for integrating open systems. However, the great diversity of agent-oriented modeling languages (AOMLs) hinders the understanding and interchange of MAS models. Most MAS concepts are shared among the AOMLs. However, these concepts have certain peculiarities in each AOML, such as the expected behavior and concrete syntax. This paper presents a metamodeling solution for the integration of the AOML diversity that uses the powertype pattern. In this pattern, the clabjects represent concept subtypes and are instantiated in models. MAS designers can change the clabject properties to indicate the peculiarities of each concept subtype, depending on the particular needs. Each designer can understand models of other experts by consulting the peculiarities of concepts in models. This solution is the Inter-methodology AOML, which is supported with a graphical modeling tool. This tool is created with a model-driven development approach. This work presents this AOML as a first step of a potential standardization process in the modeling of MASs. In addition, the Ingenias Development Kit tool, an existing agent-oriented software engineering tool, is adapted to export models to the presented AOML. The proposed Inter-methodology AOML is quantitatively compared with other AOMLs in nine different problem domains, and this comparison shows that the proposed Inter-methodology AOML can determine a higher proportion of concepts in these domains than other AOMLs. The presented AOML is also evaluated and validated with its mapping to FAML.     

An Evolutionary Dynamical Analysis of Multi-Agent Learning in Iterated Games

In this paper, we investigate Reinforcement learning (RL) in multi-agent systems (MAS) from an evolutionary dynamical perspective. Typical for a MAS is that the environment is not stationary and the Markov property is not valid. This requires agents to be adaptive. RL is a natural approach to model the learning of individual agents. These Learning algorithms are however known to be sensitive to the correct choice of parameter settings for single agent systems. This issue is more prevalent in the MAS case due to the changing interactions amongst the agents. It is largely an open question for a developer of MAS of how to design the individual agents such that, through learning, the agents as a collective arrive at good solutions. We will show that modeling RL in MAS, by taking an evolutionary game theoretic point of view, is a new and potentially successful way to guide learning agents to the most suitable solution for their task at hand. We show how evolutionary dynamics (ED) from Evolutionary Game Theory can help the developer of a MAS in good choices of parameter settings of the used RL algorithms. The ED essentially predict the equilibriums outcomes of the MAS where the agents use individual RL algorithms. More specifically, we show how the ED predict the learning trajectories of Q-Learners for iterated games. Moreover, we apply our results to (an extension of) the COllective INtelligence framework (COIN). COIN is a proved engineering approach for learning of cooperative tasks in MASs. The utilities of the agents are re-engineered to contribute to the global utility. We show how the improved results for MAS RL in COIN, and a developed extension, are predicted by the ED. Author funded by a doctoral grant of the institute for advancement of scientific technological research in Flanders (IWT).     

A Framework for Coordinated Control of Multiagent Systems and Its Applications

In this paper, a framework is proposed for the distributed control and coordination of multiagent systems (MASs). In the proposed framework, the control of MASs is regarded as achieving decentralized control and coordination of agents. Each agent is modeled as a coordinated hybrid agent, which is composed of an intelligent coordination layer and a hybrid control layer. The intelligent coordination layer takes the coordination input, plant input, and workspace input. In the proposed framework, we describe the coordination mechanism in a domain-independent way, i.e., as simple abstract primitives in a coordination rule base for certain dependence relationships between the activities of different agents. The intelligent coordination layer deals with the planning, coordination, decision making, and computation of the agent. The hybrid control layer of the proposed framework takes the output of the intelligent coordination layer and generates discrete and continuous control signals to control the overall process. To verify the feasibility of the proposed framework, experiments for both heterogeneous and homogeneous MASs are implemented. The proposed framework is applied to a multicrane system, a multiple robot system, and a MAS consisting of an overhead crane, a mobile robot, and a robot manipulator. It is demonstrated that the proposed framework can model the three MASs. The agents in these systems are able to cooperate and coordinate to achieve a global goal. In addition, the stability of systems modeled using the proposed framework is also analyzed.     

Convergence analysis of max-consensus algorithm in probabilistic communication networks with Bernoulli dropouts

In the presence of probabilistic communication networks between agents, the convergence analysis of max-consensus algorithm (MCA) is addressed in this paper. It is considered that at each iteration of MCA, all agents share their measurements with adjacent agents via local communication networks which is applicable in many multi-agent systems (MASs). It is assumed that the communication networks have Bernoulli dropouts, i.e. the information exchanged between agents may be lost with Bernoulli distribution. In the proposed method, the information topology of MAS is modelled as a dynamic graph with the Bernoulli adjacency matrix. It is proved that in the presence of Bernoulli dropouts and under non-restrictive assumptions concerning the MAS features and communication topology, the MCA converges with a probability one in the finite time. Furthermore, the upper bounds are provided by means of deterministic and probabilistic expressions for the expectation and dispersion of convergence time, respectively. It is shown that the proposed upper bounds are asymptotic, i.e. there are specific conditions of MAS in which the convergence time of MCA tends to the proposed upper bounds. The convergence accuracy of MCA is discussed in terms of probabilistic equations. The validity of the proposed theorems is illustrated by means of simulation results.     

A framework for the design and automated implementation of communication aspects in multi-agent systems

This paper presents REFCON, a framework for the automated development of Agent Communication Contexts (ACCs) in multi-agent systems (MASs). ACCs are intended to capture the interaction requirements of a MAS.A formal specification framework is first presented, aimed at modelling an ACC as a set of rules for filtering and filling messages, based on their contents, and the names and roles of the exchanging agents. A XML-based specification language is then introduced, which encodes the specification formalism for the sake of its computer processing. Finally, an object-oriented software architecture capable of supporting ACC-based MAS development is presented.REFCON key characteristic is that it allows a seamless integration of ACC support (even) into an existing MAS, at run-time, independently of the agent platform used for the implementation. This is made possible by a layered software architecture based on computational reflection, a technology that allows transparent evolution and adaptation of existing systems. The REFCON framework is also dynamic, in the two-fold sense that it is capable of both adding new rules and handling multiple contexts, which it can easily switch among, at run-time. The ACC-based design of an example MAS for document sharing is briefly discussed, as a demonstration of the principles put forward.     

A values‐driven self‐organization mechanism for automating multiagent coordination

In distributed and open environments, MASs (multiagent systems) generally have no mechanisms for prior coordination and self‐organization has been believed to be the necessary selection to achieve the coordination of agents. This paper first presents a values‐driven model for self‐organization in which the expected emergent properties of a system are specified as the social values while the social values are realized via implicitly inducing members to regulate their individual values and adjust their behaviors to fit the expectations of the system. Based on the values‐driven self‐organization, this paper proposes an automated coordination mechanism for decentralized MASs. In this mechanism, by indirectly changing the difficulties in acquiring resources (which may be delegated to some special agents since MASs generally do not have substantial bodies), MASs can lead agents to regulate their values to be consistent with the social values of MASs so that the coordination of MASs can spontaneously emerge from the local behaviors of agents. Finally, this paper implements a simulation traffic system using the coordination mechanism based on values‐driven self‐organization to validate the emergence of coordination among multiple agents.     

Agent-oriented modeling and development of a system for crisis management

The management of crisis situations has been a challenging problem from different points of views, such as communication efficiency and avoiding casualties. This paper presents a novel approach that includes an interaction organization pattern for Multi-agent Systems (MASs) in crisis management, abstracted from several existing case studies in which the agents follow a sequence of interactions and the organization must optimize the use of human resources. The pattern considers an emergent organization of peers that adopt different roles according to the circumstances. The key features of the organization are its robustness, scalability (in terms of both agents and roles), flexibility to deal with a changing environment, and the efficient use of resources. In order to validate the organization, the paper presents its modeling and development with the Ingenias methodology, conforming the corresponding MAS. This development follows a model-driven approach, which allows a smooth transition from the specification to the code, and a low-cost testing of the system with different settings. Another key aspect is the application of metrics for validating and improving the MAS in terms of response time. The MAS has been tested with 600 agents representing 200 citizens, showing its performance.     

Dynamic event-based consensus of multi-agent systems with secure mode resilient to DoS attack

In this article, the resilient leaderless consensus problem for a multi-agent system (MAS) under denial-of-service(DoS) attack is investigated. The DoS attack is carried out with multiple strategies. The sufficient condition for MASs achieving consensus under multi-mode DoS attack is developed. In order to actively alleviate the influence caused by the DoS attack, the MAS switches between normal and secure modes. Once a DoS attack occurs, the agents will switch into the secure mode with a lower open-loop divergence rate. A dynamic event-based consensus protocol is proposed, driving the MAS achieve consensus while saving communication resources effectively. Moreover, rigorous proof analysis demonstrates the Zeno-free property of the developed dynamic event-triggered mechanism. Finally, a numerical simulation is provided to illustrate the effectiveness of the proposed theoretical results.