Agents and Service-Oriented Computing for Autonomic Computing: A Research Agenda

Autonomic computing is the solution proposed to cope with the complexity of today's computing environments. Self-management, an important element of autonomic computing, is also characteristic of single and multiagent systems, as well as systems based on service-oriented architectures. Combining these technologies can be profitable for all — in particular, for the development of autonomic computing systems.     

Opportunities for multiagent systems and multiagent reinforcement learning in traffic control

The increasing demand for mobility in our society poses various challenges to traffic engineering, computer science in general, and artificial intelligence and multiagent systems in particular. As it is often the case, it is not possible to provide additional capacity, so that a more efficient use of the available transportation infrastructure is necessary. This relates closely to multiagent systems as many problems in traffic management and control are inherently distributed. Also, many actors in a transportation system fit very well the concept of autonomous agents: the driver, the pedestrian, the traffic expert; in some cases, also the intersection and the traffic signal controller can be regarded as an autonomous agent. However, the “agentification” of a transportation system is associated with some challenging issues: the number of agents is high, typically agents are highly adaptive, they react to changes in the environment at individual level but cause an unpredictable collective pattern, and act in a highly coupled environment. Therefore, this domain poses many challenges for standard techniques from multiagent systems such as coordination and learning. This paper has two main objectives: (i) to present problems, methods, approaches and practices in traffic engineering (especially regarding traffic signal control); and (ii) to highlight open problems and challenges so that future research in multiagent systems can address them.     

The RoboCup Soccer Server and CMUnited Clients: Implemented Infrastructure for MAS Research

The RoboCup Soccer Server and associated client code is a growing body of software infrastructure that enables a wide variety of multiagent systems research. The Soccer Server is a multiagent environment that supports 22 independent agents interacting in a complex, real-time environment. AI researchers have been using the Soccer Server to pursue research in a wide variety of areas, including real-time multiagent planning, real-time communication methods, collaborative sensing, and multiagent learning. This article describes the current Soccer Server and the champion CMUnited soccer-playing agents, both of which are publically available and used by a growing research community. It also describes the ongoing development of FUSS, a new, flexible simulation environment for multiagent research in a variety of multiagent domains.     

面向Web服务的多Agent系统的通信机制

介绍了一种面向Web服务的多Agent系统的通信机制，该通信机制通过消息模板和基于本体论的知识表示语言，消除了多Agent系统中因通信语言的差异而产生的影响，实现了Agent之间的有效通信。     

多Agent系统的研究

自agent术语引起国内学者重视以来,人们一直试图寻找一个贴切的汉语名词,“代理人”“智能体洲结点”等概念应运而生。但这些概念都不能令人满意,因为无论是“智能体”、“代理人”,还是“结点”,都仅部分地反映了agent的特征,而未完全地反映其本质属性,故本文仍使用agent的英文形式。     

Environment as a first class abstraction in multiagent systems

The current practice in multiagent systems typically associates the environment with resources that are external to agents and their communication infrastructure. Advanced uses of the environment include infrastructures for indirect coordination, such as digital pheromones, or support for governed interaction in electronic institutions. Yet, in general, the notion of environment is not well defined. Functionalities of the environment are often dealt with implicitly or in an ad hoc manner. This is not only poor engineering practice, it also hinders engineers to exploit the full potential of the environment in multiagent systems. In this paper, we put forward the environment as an explicit part of multiagent systems.We give a definition stating that the environment in a multiagent system is a first-class abstraction with dual roles: (1) the environment provides the surrounding conditions for agents to exist, which implies that the environment is an essential part of every multiagent system, and (2) the environment provides an exploitable design abstraction for building multiagent system applications. We discuss the responsibilities of such an environment in multiagent systems and we present a reference model for the environment that can serve as a basis for environment engineering. To illustrate the power of the environment as a design abstraction, we show how the environment is successfully exploited in a real world application. Considering the environment as a first-class abstraction in multiagent systems opens up new horizons for research and development in multiagent systems.     

Multiagent systems and information retrieval our experience with X.MAS

Autonomous agents and multiagent systems have been successfully applied to a number of problems and have been largely used in different application fields. In particular, in this paper we are interested in information retrieval. In fact, in this field multiagent solutions are very useful and effective since they decouple the problem in a network of software agents that interact to solve problems that are beyond the individual capabilities or knowledge. In so doing, multiagent systems allow to overwhelm typical problems of single agent and centralized approaches. To discuss the lesson learnt in using the multiagent technology in the field of information retrieval, in this paper, we present our experience in using X.MAS, a generic multiagent architecture aimed at retrieving, filtering and reorganizing information according to user interests. To this end, after presenting X.MAS, we illustrate six applications built upon it. Our conclusion is that multiagent technology is quite effective to design and realize concrete information retrieval applications.     

Introduction to the special issue on normative multiagent systems

This special issue contains four selected and revised papers from the second international workshop on normative multiagent systems, for short NorMAS07 (Boella et al. (eds) Normative multiagent systems. Dagstuhl seminar proceedings 07122, 2007), held at Schloss Dagstuhl, Germany, in March 2007. At the workshop a shift was identified in the research community from a legal to an interactionist view on normative multiagent systems. In this editorial we discuss the shift, examples, and 10 new challenges in this more dynamic setting, which we use to introduce the papers of this special issue.     

Cooperative multiagent systems: a personal view of the state of theart

Scientific research and practice in multiagent systems focuses on constructing computational frameworks, principles, and models for how both small and large societies of intelligent, semiautonomous agents can interact effectively to achieve their goals. This article provides a personal view of the key application areas for cooperative multiagent systems, the major intellectual problems in building such systems, the underlying principles governing their design, and the major directions and challenges for future developments in this field     

A self-learning approach for validation of runtime adaptation in service-oriented systems

Ensuring that service-oriented systems can adapt quickly and effectively to changes in service quality, business needs and their runtime environment is an increasingly important research problem. However, while considerable research has focused on developing runtime adaptation frameworks for service-oriented systems, there has been little work on assessing how effective the adaptations are. Effective adaptation ensures the system remains relevant in a changing environment and is an accurate reflection of user expectations. One way to address the problem is through validation. Validation allows us to assess how well a recommended adaptation addresses the concerns for which the system is reconfigured and provides us with insights into the nature of problems for which different adaptations are suited. However, the dynamic nature of runtime adaptation and the changeable contexts in which service-oriented systems operate make it difficult to specify appropriate validation mechanisms in advance. This paper describes a novel consumer-centered approach that uses machine learning to continuously validate and refine runtime adaptation in service-oriented systems, through model-based clustering and deep learning.     

Learning in groups of traffic signals

Computer science in general, and artificial intelligence and multiagent systems in particular, are part of an effort to build intelligent transportation systems. An efficient use of the existing infrastructure relates closely to multiagent systems as many problems in traffic management and control are inherently distributed. In particular, traffic signal controllers located at intersections can be seen as autonomous agents. However, challenging issues are involved in this kind of modeling: the number of agents is high; in general agents must be highly adaptive; they must react to changes in the environment at individual level while also causing an unpredictable collective pattern, as they act in a highly coupled environment. Therefore, traffic signal control poses many challenges for standard techniques from multiagent systems such as learning. Despite the progress in multiagent reinforcement learning via formalisms based on stochastic games, these cannot cope with a high number of agents due to the combinatorial explosion in the number of joint actions. One possible way to reduce the complexity of the problem is to have agents organized in groups of limited size so that the number of joint actions is reduced. These groups are then coordinated by another agent, a tutor or supervisor. Thus, this paper investigates the task of multiagent reinforcement learning for control of traffic signals in two situations: agents act individually (individual learners) and agents can be “tutored”, meaning that another agent with a broader sight will recommend a joint action.     

TRAMMAS: A tracing model for multiagent systems

Agent's flexibility and autonomy, as well as their capacity to coordinate and cooperate, are some of the features which make multiagent systems useful to work in dynamic and distributed environments. These key features are directly related to the way in which agents communicate and perceive each other, as well as their environment and surrounding conditions. Traditionally, this has been accomplished by means of message exchange or by using blackboard systems. These traditional methods have the advantages of being easy to implement and well supported by multiagent platforms; however, their main disadvantage is that the amount of social knowledge in the system directly depends on every agent actively informing of what it is doing, thinking, perceiving, etc. There are domains, for example those where social knowledge depends on highly distributed pieces of data provided by many different agents, in which such traditional methods can produce a great deal of overhead, hence reducing the scalability, efficiency and flexibility of the multiagent system. This work proposes the use of event tracing in multiagent systems, as an indirect interaction and coordination mechanism to improve the amount and quality of the information that agents can perceive from both their physical and social environment, in order to fulfill their goals more efficiently. In order to do so, this work presents an abstract model of a tracing system and an architectural design of such model, which can be incorporated to a typical multiagent platform.     

Ontology-driven intelligent service for configuration support in networked organizations

Nowadays, organizations must continually adapt to market and organizational changes to achieve their most important goals. Migration to business services and service-oriented architectures provides a valuable opportunity to attain the organization objectives. This migration causes evolution both in organizational structure and in technology-enabling businesses to dynamically change vendors and services. One of the forms of organizational structures is the form of networked organization. Technologies of business intelligence and Web intelligence effectively support business processes within the networked organizations. While business intelligence focuses on development of services for consumer needs recognition, information search, and evaluation of alternatives; Web intelligence addresses advancement of Web-empowered systems, services, and environments. The paper proposes a technological ontology-driven framework for configuration support as applied to networked organization. The framework integrates concepts of business intelligence and Web intelligence into a collaboration environment of a networked organization on the base of attainment of knowledge logistics purposes. This framework referred to as KSNet is based on the integration of software agent technology and Web services. Knowledge logistics functions of KSNet are complemented by technological functions of knowledge-gathering agents. The services of these agents are implemented with CAPNET, a FIPA compliant agent platform. CAPNET allows consuming services of agents in a service-oriented way. Applicability of the approach is illustrated through a “Binni scenario”-based case study of a portable field hospital configuration. Alexander Smirnov received his M.E., Ph.D. and D.Sc. degrees from St. Petersburg, Russia, in 1979, 1984, and 1994, respectively. He is a Deputy Director for Research and a Head of Computer Aided Integrated Systems Laboratory at St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences (SPIIRAS). He is a full professor at St. Petersburg State Electrical Engineering University. His current research interests include corporate knowledge management, Web-services, group decision support systems, virtual enterprises, and supply chain management. Nikolay Shilov received his M.E. from St. Petersburg State Technical University, Russia, in 1998 and his Ph.D. from SPIIRAS, in 2005. He is a senior researcher at the Computer Aided Integrated Systems Laboratory of SPIIRAS. His current research interests include virtual enterprise configuration, supply chain management, knowledge management, ontology engineering and Web-services. Tatiana Levashova received her M.E. degree from St. Petersburg State Electrical Engineering University, in 1986. She is a lead programmer at Computer Aided Integrated Systems Laboratory of SPIIRAS. Her current research is devoted to knowledge-related problems such as knowledge representation, knowledge management, and ontology management. She has published more than 96 papers in reviewed journals and proceedings of international conferences. Leonid Sheremetov received his Ph.D. in Computer Science in 1990 from St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences where he has worked as a research fellow and a senior research fellow from 1982. Now, he is a principal investigator of the Research Program on Applied Mathematics and Computing of the Mexican Petroleum Institute where he leads the Distributed Intelligent Systems Group, and is also a part-time professor of the Artificial Intelligence Laboratory of the Centre for Computing Research of the National Polytechnic Institute, Mexico. His current research interests include multiagent systems, semantic WEB, decision support systems, and enterprise information integration. His group developed CAPNET agent platform and has been involved in several projects for the energy industry ranging from petroleum exploration and production to knowledge management, with special focus on industrial exploitation of agent technology. He is also member of Editorial Boards of several journals. Miguel Contreras obtained his M.S. degree in Computer Science in 2002 from the National Polytechnic Institute of Mexico. Now, he is a Ph.D. student of the Postgraduate Studies Program at the Mexican Petroleum Institute. He is one of the principal developers of the CAPNET agent platform. His current research interests include multiagent systems, service-oriented architectures, and enterprise information integration.     

具有复杂动力学的多智能体系统分布式优化综述

多智能体系统分布式优化由于其高效性、灵活性和可靠性等特点吸引了大量学者的关注,在多机器人协同控制、无线传感器网络、能源系统等领域具有广泛的应用前景.分布式优化的基本目标是利用智能体的个体目标函数梯度、自身及其邻居状态信息设计分布式控制协议,驱动所有智能体的状态或输出到全局目标函数的最优解,系统动力学是影响智能体状态演化的重要因素.鉴于此,在回顾现有连续时间分布式优化算法的基础上,根据系统动力学分类,尽可能全面地评述具有复杂动力学的多智能体系统分布式优化问题的最新研究进展,并对未来发展方向进行展望.     

网络结构化多Agent系统的任务分配

网络结构化多Agent系统既包括系统运行的底层物理网络,还包括Agent之间的交互网络。传统的任务分配方式并没有深入考虑到网络结构化的特点。文中首先论述网络结构化多Agent系统中任务分配的特点,介绍和分析基于底层网络拓扑与资源分布的任务分配方式、基于Agent交互网络与资源分布的任务分配方式和基于综合网络情境资源的任务分配方式。然后对相关工作进行综述,并与网络结构化多Agent系统任务分配模型进行比较分析。最后论述该方向的难点和未来要解决的问题。     

Approximately adaptive neural cooperative control for nonlinear multiagent systems with performance guarantee

This paper studies the cooperative control problem for a class of multiagent dynamical systems with partially unknown nonlinear system dynamics. In particular, the control objective is to solve the state consensus problem for multiagent systems based on the minimisation of certain cost functions for individual agents. Under the assumption that there exist admissible cooperative controls for such class of multiagent systems, the formulated problem is solved through finding the optimal cooperative control using the approximate dynamic programming and reinforcement learning approach. With the aid of neural network parameterisation and online adaptive learning, our method renders a practically implementable approximately adaptive neural cooperative control for multiagent systems. Specifically, based on the Bellman's principle of optimality, the Hamilton–Jacobi–Bellman (HJB) equation for multiagent systems is first derived. We then propose an approximately adaptive policy iteration algorithm for multiagent cooperative control based on neural network approximation of the value functions. The convergence of the proposed algorithm is rigorously proved using the contraction mapping method. The simulation results are included to validate the effectiveness of the proposed algorithm.     

Locality-sensitive task allocation and load balancing in networked multiagent systems: Talent versus centrality

With the development of large scale multiagent systems, agents are always organized in network structures where each agent interacts only with its immediate neighbors in the network. Coordination among networked agents is a critical issue which mainly includes two aspects: task allocation and load balancing; in traditional approach, the resources of agents are crucial to their abilities to get tasks, which is called talent-based allocation. However, in networked multiagent systems, the tasks may spend so much communication costs among agents that are sensitive to the agent localities; thus this paper presents a novel idea for task allocation and load balancing in networked multiagent systems, which takes into account both the talents and centralities of agents. This paper first investigates the comparison between talent-based task allocation and centrality-based one; then, it explores the load balancing of such two approaches in task allocation. The experiment results show that the centrality-based method can reduce the communication costs for single task more effectively than the talent-based one, but the talent-based method can generally obtain better load balancing performance for parallel tasks than the centrality-based one.     

基于多Agent技术的自动文摘研究

针对自动文摘研究所面临的问题和现有单机自动文摘系统的性能特点,提出了基于多Ａｇｅｎｔ技术的自动文摘系统的构想,给出了其体系结构,阐述了系统的工作原理。该文摘系统是一个面向Ｉｎｔｅｒｎｅｔ的分布式系统,且具有较好的可靠性和开放性。     

Service substitutability analysis based on behavior automata

Identifying a suitable service to substitute the failed service in Web service composition is a primary means to improve the robustness and dependability of service-oriented computing and cloud computing. Service behavioral substitutability analysis and verification is the main research subject of service-oriented computing. In this paper, based on Finite Automata and characteristics of service-oriented software systems, service behavior automata have been proposed to describe Web service behavior protocols formally; a conceptual hierarchy of service behavioral substitutability has been formally defined to increase service component reuse, and related verification algorithms have been proposed to analyze service behavioral substitutability automatically.