基于Scenario的MAS环境建模方法

对多Agent系统（MAS）的环境建模将有助于促进复杂MAS的分析和设计。本文提出了一种基于Scenario的MAS环境建模方法,该方法通过描述MAS的Scenario导出和分析MAS的环境,并采用高层的组织抽象思想对MAS的环境进行建模。本文通过一个电子商务系统的案例分析说明如何利用该方法指导MAS的开发。     

MASCF: A generic process-centered methodological framework for analysis and design of multi-agent supply chain systems

Multi-agent systems (MAS) are becoming popular for modeling complex systems such as supply chains. However, development of multi-agent systems remain quite involved and extremely time consuming. Currently, there exist no generic methodologies for modeling supply chains using multi-agent systems. In this research, we propose a generic process-centered methodological framework, Multi-Agent Supply Chain Framework (MASCF), to simplify MAS development for supply chain (SC) applications. MASCF introduces the notion of process-centered organization metaphor, and creatively adopts Supply Chain Operations Reference (SCOR) model to a well-structured generic MAS analysis and design methodology, Gaia, for multi-agent supply chain system (MASCS) development. The popular Tamagotchi case was designed and analyzed using MASCF. The validity of the framework was established by implementing MASCF output of Tamagotchi SC using the Java Agent DEvelopment Framework (JADE).     

A dynamic scheme for scheduling complex tasks in manufacturing systems based on collaboration of agents

The characteristics of scheduling tasks in the real world is a dynamic and challenging issue as the processes and the companies involved may change from time to time. For small flexible enterprises to respond to business opportunities, an effective scheme to facilitate dynamic coalition, share the core competencies and resources and support inter-enterprise collaboration must be developed. Although multi-agent systems (MAS) provide a paradigm for modeling these characteristics, scheduling tasks in MAS is a complex problem due to the computational complexity involved, distributed architecture for scheduling tasks by individual agents and dependency of different agents’ workflows. How to develop a problem solver that can be applied in MAS to achieve coherent and consistent workflow schedules that can meet a customer’s order is an important issue. In this paper, we propose a solution methodology for scheduling workflows in MAS. Our solution combines the multi-agent system architecture to dynamically discover services, workflow and activity models to specify the capabilities of agents, contract net protocol to facilitate negotiation and coordination of agents and optimization theories to optimize the cost for fulfilling an order. A problem solver for scheduling tasks in MAS has been implemented. An application scenario has also been provided to verify our solution methodology.     

Consensus control of discrete-time multi-agent systems with privacy preservation

Due to the dependence of consensus theory on multi-agent systems (MAS) and the need to openly exchange status information with neighbors on each agent, private status and information are inevitably exposed to the public. In this paper, we present a consensus privacy protection algorithm to solve the consensus privacy problem of second-order MAS. The proposed approach is based on the Paillier algorithm with semi-homomorphic characteristics while combining a distributed control perspective to solve the privacy issues of sharing system status information. Under the conditions of our design, as long as there is at least one trustworthy neighbor node, the privacy of the topology nodes can be protected, and thus, the interconnected nodes are unable to detect the state information of systems from one another. We provide a new method for the privacy of the state information of the discrete-time MAS and use restricted hardware devices to implement the consensus control. Finally, experiments verify the effectiveness of our method, while meeting the actual applicability     

A DISTRIBUTED MULTI-CRITERIA APPROACH FOR TRAFFIC REGULATION IN PUBLIC TRANSPORTATION SYSTEMS

Given the dynamic character of public transportation systems, it is difficult to respect accurately theoretical timetables. In real conditions, many disturbances may occur in transportation networks and cause troubles in vehicle schedules. In order to keep transportation systems as stable as possible, a real-time traffic regulation has to be performed by optimizing some regulation criteria that represent exploitation objectives. In this article, we deal with the regulation problem as a multi-criteria optimization one for which we propose a nonaggregative approach based on multi-agent systems. For a given disturbance, the approach firstly ensures an anytime generation of Pareto solutions set by using a distributed Tabu search. Then, the best compromise solution is determined through a multi-criteria evaluation process. Therefore, two multi-criteria evaluation procedures are also suggested: Plurality voting procedure and fuzzy-based procedure. In order to assess the distributed approach, an experimental study was performed on the base of real scenarios.     

MOBMAS: A methodology for ontology-based multi-agent systems development

Ontologies offer significant benefits to multi-agent systems: interoperability, reusability, support for multi-agent system (MAS) development activities (such as system analysis and agent knowledge modeling) and support for MAS operation (such as agent communication and reasoning). This paper presents an ontology-based methodology, MOBMAS, for the analysis and design of multi-agent systems. MOBMAS is the first methodology that explicitly identifies and implements the various ways in which ontologies can be used in the MAS development process and integrated into the MAS model definitions. In this paper, we present comprehensive documentation and validation of MOBMAS.     

ADK: An Agent Development Kit Based on a Formal Design Model for Multi-Agent Systems

The advent of multi-agent systems has brought us opportunities for the development of complex software that will serve as the infrastructure for advanced distributed applications. During the past decade, there have been many agent architectures proposed for implementing agent-based systems, and also a few efforts to formally specify agent behaviors. However, research on narrowing the gap between agent formal models and agent implementation is rare. In this paper, we propose a model-based approach to designing and implementing intelligent agents for multi-agent systems (MAS). Instead of using formal methods for the purpose of specifying agent behavior, we bring formal methods into the design phase of the agent development life cycle. Specifically, we use the formalism called agent-oriented G-net model, which is based on the G-net formalism (a type of high-level Petri net), to serve as the high-level design for intelligent agents. Based on the high-level design, we further derived the agent architecture and the detailed design for agent implementation. To demonstrate the feasibility of our approach, we developed the toolkit called ADK (Agent Development Kit) that supports rapid development of intelligent agents for multi-agent systems and we discuss the role of inheritance in agent-oriented development. As a potential solution for automated software development, we summarize the procedure to generate a model-based design of application-specific agents. Finally, to illustrate an application built on ADK, we present an air-ticket trading example.     

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).     

Methodological design and comparative evaluation of a MAS providing AmI

Researches on Ambient Intelligent and Ubiquitous Computing using wireless technologies have increased in the last years. In this work, we review several scenarios to define a multi-agent architecture that support the information needs of these new technologies, for heterogeneous domain. Our contribution consists of designing in a methodological way a Context Aware System (involving location services) using agents that can be used in very different domains. We describe all the steps followed in the design of the agent system. We apply a hybridizing methodology between GAIA and AUML. Additionally we propose a way to compare different agent architectures for Context Aware System using agent interactions. So, in this paper, we describe the assignment of weight values to agents interaction in two different MAS architectures for Context Aware problems solving different scenarios inspired in FIPA standard negotiation protocols.     

THE GAIA2JADE PROCESS FOR MULTI-AGENT SYSTEMS DEVELOPMENT

In this paper we present the Gaia2JADE process concerning how one can implement a multi-agent system with the JADE framework using the Gaia methodology for analysis and design purposes. This process is particularly dedicated to the conversion of Gaia models to JADE code. It is described using the Software Process Engineering Metamodel (SPEM) and extends the one proposed by FIPA for describing the Gaia modeling process. Thus, it proposes to potential MAS developers a process that covers the full software development lifecycle. This work is based on the experience we have acquired by applying this process for implementing a real-word multi-agent system conceived for providing e-services to mobile users. With this paper, we share this experience with future multi-agent systems (MAS) developers, who would like to follow this process, taking into account several technical issues that emerged during the implementation phase, helping them to easily model and implement their systems.     

Flexible coordinator design for modeling resource sharing in multi-agent systems

One approach to modeling multi-agent systems (MASs) is to employ a method that defines components which describe the local behavior of individual agents, as well as a special component, called a coordinator. The coordinator component coordinates the resource sharing behavior among the agents. The agent models define a set of local plans, and the combination of local plans and a coordinator defines a system’s global plan. Although earlier work has provided the base functionality needed to synthesize inter-agent resource sharing behavior for a global, conflict-free MAS environment, the lack of coordination flexibility limits the modeling capability at both the local plan level and the global plan level. In this paper, we describe a flexible design method that supports a range of coordinator components. The method defines four levels of coordination and an associated four-step coordinator generation process, which allows for the design of coordinators with increasing capabilities for handling complexity associated with resource coordination. Colored Petri net based simulation is used to analyze various properties that derive from different coordinators and synthesis of a reduced coordinator component is discussed for cases that involve homogeneous agents.     

Towards modeling and runtime verification of self-organizing systems

According to the fact that the intrinsic dynamism of self-organizing systems challenges the existing methods of engineering for modeling reliable complex systems, in this paper, we propose a new formal-based method to model self-organizing systems. The capabilities of the proposed method which are used to address several challenges in design, development and analysis of self-organizing systems are: modularity and robustness, decentralized control and scalability, required adaptation types, flexible and adaptive control mechanism, separation of adaptation and business logic, and safe adaptation. To evaluate the proposed method, we use self-organizing traffic management system as a case study and exploit the proposed method for modeling this dynamic system. Moreover, we propose and employ a novel policy-based runtime verification mechanism to ensure that the safety properties are satisfied by the implementation at runtime. We provide our case study prototype using Java and the Ponder2 toolkit and apply our runtime verification method to show its proper reaction capabilities to the property violations. This benefit is the result of using dynamic policies in our method to control the behavior of systems.     

Context-aware workflow management for virtual enterprises based on coordination of agents

Although virtual enterprises (VE) make it possible for small flexible enterprises to form a collaborative network to respond to business opportunities through dynamic coalition and sharing of the core competencies and resources, they also pose new challenges and issues. Creation of VE involves dynamically established partnerships between the partners and relies on a flexible coordination scheme. The dynamic organizations formed in VE present a challenge in the development of a new methodology to dynamically allocate re-sources and deliver the relevant information to the right people at the right time. A key issue is the development of an effective workflow management scheme for VE. Multi-agent systems (MAS) provide a flexible architecture to deal with changes based on dynamic organization and collaboration of autonomous agents. Despite the extensive studies and research results on MAS, development of a design methodology to support coordination and operations is critical to the success and adoption of VE. The objectives of this research are to propose a design methodology to facilitate coordination and development of context-aware workflow management systems and achieve effective resource allocation for VE based on MAS architecture. To achieve these objectives, a scheme for coordination of agents is proposed. Petri net models are used in the coordination scheme to describe workflows and capture resource activities in VE. The interactions between agents lead to a dynamic workflow model for VE. Based on the aforementioned model, we propose architecture to dynamically generate context-aware graphical user interface to guide the users and control resource allocation based on the state of VE. An order management example is used throughout this paper to illustrate the proposed design methodology.     

Consensus reaching in multi-agent packet-switched networks with non-linear coupling

An important task for multi-agent systems (MAS) is to reach a consensus, e.g. to align their velocity vectors. Recent results propose appropriate consensus protocols to achieve such tasks, but most of them do not consider the effect of communication constraints such as the presence of time-delays in the exchange of information between the agents. In this article, we provide conditions for a non-linear, locally passive MAS of arbitrary size to reach a consensus, when the agents communicate over a packet-switched network that is characterised by a given topology. Both the cases of constant and switching topologies are considered. The nature of the communication channel imposes constraints that are modelled using stochastic delays of arbitrary distribution. We first embed this model in another, distributed but deterministic delay model and provide conditions for the error introduced by this simplification. In our main result, we provide conditions for the locally passive MAS with distributed delays to reach a consensus. In the case of a fixed topology, the underlying directed graph has to contain a spanning tree. In the case of a switching topology, only the union graph of all graphs that persist over time is required to contain a spanning tree. These conditions are independent of the distribution and the size of the packet delays. To show attractivity of the consensus set, we use an invariance principle for systems described by functional differential equations based on an appropriate Lyapunov–Razumikhin function. This methodological approach is the main contribution of this work and can also be applied to other consensus problems with delays. We illustrate our results by numerical simulations showing synchronisation of non-linear Kuramoto oscillators over a digital network.     

基于值分布的多智能体分布式深度强化学习算法

近年来深度强化学习在一系列顺序决策问题中取得了巨大的成功,使其为复杂高维的多智能体系统提供有效优化的决策策略成为可能.然而在复杂的多智能体场景中,现有的多智能体深度强化学习算法不仅收敛速度慢,而且算法的稳定性无法保证.本文提出了基于值分布的多智能体分布式深度确定性策略梯度算法(multi-agent distribut...     

Game theoretical applications for multi-agent systems

We consider game-theoretic principles for design of cooperative and competitive (non-cooperative self-interested) multi-agent systems. Using economic concepts of tâtonnement and economic core, we show that cooperative multi-agent systems should be designed in games with dominant strategies that may lead to social dilemmas. Non-cooperative multi-agent systems, on the other hand, should be designed for games with no clear dominant strategies and high degree of problem complexity. Further, for non-cooperative multi-agent systems, the number of learning agents should be carefully managed so that solutions in the economic core can be obtained. We provide experimental results for the design of cooperative and non-cooperative MAS from telecommunication and manufacturing industries.     

一种基于知识树和约束的柔性活动动态细化方法

柔性工作流在应对业务建模过程中的动态不确定因素、提高工作流系统的柔性具有巨大的优势,然而,柔性活动的动态细化一直是柔性工作流建模和应用的一个难点。因此,提出一种基于知识树和约束的柔性活动动态细化方法。该方法以知识树的包含和泛化关系作为启发信息,以活动选取约束和时序约束作为指导和校验,实现柔性活动的动态细化。在介绍了知识树及其蕴含关系以及活动选取约束和时序约束规则的基础上,给出了柔性活动的动态细化算法,描述了活动选取校验和时序约束校验算法。最后给出了算法的实现和实例分析,其结果表明了所提方法的有效性,能够很好地解决柔性活动的细化问题。     

Extending process automation systems with multi-agent techniques

This paper presents a design of a process automation system extended with multi-agent systems (MAS) and experiments with its implementation. According to this design, MAS can be used to extend the functionality of ordinary process automation systems at higher levels of control. Anticipated benefits of this include enhanced reconfigurability, responsiveness and flexibility of the resulting automation system. The design also takes into account particular characteristics of process automation. An agent platform for process automation is presented as a basis for applying MAS. A FIPA-compliant agent platform is extended with process automation specific functionality. The platform utilizes a hierarchical agent organization and a BDI-agent model. Two applications are implemented using the platform. One of these shows how the techniques of distributed planning can be applied in discrete control. The other provides a model for supervisory continuous control using the techniques of distributed search. Experiments performed with a laboratory test environment using the applications are presented. They are able to demonstrate the feasibility of the approach in test scenarios.     

Integrating skills into multi-agent systems

Currently, an important topic of robotic research is the design and development of multi-agent robot systems (MASs). In these a number of autonomous robots cooperate and coordinate themselves in order to pursue given goals. The agents of an MAS not only have to work autonomously or in cooperation with other agents, but in dynamic, relatively unstructured environments. Therefore, the agents require agent-specific but flexible skills to cope with their tasks and the environment's variability. On the other hand, the actions to be performed by agents in an MAS have to meet certain requirements imposed by the MAS's structure. The representation of actions has to support planning, inter-agent communication, task negotiation etc. In this paper, we describe a method of combining the agent-specific nature of skills with the requirements for a general action knowledge representation inherent to MASs, by presenting elementary operations (EOs) that provide an appropriate interface.