Intelligent multiagent application system in an AI system

This article presents an intelligent multiagent application system in AI. The research trend into multiagents is changing from a centralized computing environment to a distributed computing environment. Also, the research into multiagents can be changed to a mobile environment. Initially, the study of multiagents is from research into human modeling. Therefore, we fi rst present a brief concept of a mobile multiagent, and then we present some application areas for mobile multiagents, especially in elearning, bioinformatics, control, and information retrieval, etc. Finally, we present the research theme of multiagents in AI. This work was presented in part at the 12th International Symposium on Articial Life and Robotics, Oita, January 25–27, 2007     

An experts approach to strategy selection in multiagent meeting scheduling

In the multiagent meeting scheduling problem, agents negotiate with each other on behalf of their users to schedule meetings. While a number of negotiation approaches have been proposed for scheduling meetings, it is not well understood how agents can negotiate strategically in order to maximize their users’ utility. To negotiate strategically, agents need to learn to pick good strategies for negotiating with other agents. In this paper, we show how agents can learn online to negotiate strategically in order to better satisfy their users’ preferences. We outline the applicability of experts algorithms to the problem of learning to select negotiation strategies. In particular, we show how two different experts approaches, plays [3] and Exploration–Exploitation Experts (EEE) [10] can be adapted to the task. We show experimentally the effectiveness of our approach for learning to negotiate strategically.     

A layered approach to learning coordination knowledge in multiagent environments

Multiagent learning involves acquisition of cooperative behavior among intelligent agents in order to satisfy the joint goals. Reinforcement Learning (RL) is a promising unsupervised machine learning technique inspired from the earlier studies in animal learning. In this paper, we propose a new RL technique called the Two Level Reinforcement Learning with Communication (2LRL) method to provide cooperative action selection in a multiagent environment. In 2LRL, learning takes place in two hierarchical levels; in the first level agents learn to select their target and then they select the action directed to their target in the second level. The agents communicate their perception to their neighbors and use the communication information in their decision-making. We applied 2LRL method in a hunter-prey environment and observed a satisfactory cooperative behavior. Guray Erus received the B.S. degree in computer engineering in 1999, and the M.S. degree in cognitive sciences, in 2002, from Middle East Technical University (METU), Ankara, Turkey. He is currently a teaching and research assistant in Rene“ Descartes University, Paris, France, where he prepares a doctoral dissertation on object detection on satellite images, as a member of the intelligent perception systems group (SIP-CRIP5). His research interests include multi-agent systems and image understanding. Faruk Polat is a professor in the Department of Computer Engineering of Middle East Technical University, Ankara, Turkey. He received his B.Sc. in computer engineering from the Middle East Technical University, Ankara, in 1987 and his M.S. and Ph.D. degrees in computer engineering from Bilkent University, Ankara, in 1989 and 1993, respectively. He conducted research as a visiting NATO science scholar at Computer Science Department of University of Minnesota, Minneapolis in 1992–93. His research interests include artificial intelligence, multi-agent systems and object oriented data models.     

Application and development of biologically plausible neural networks in a multiagent artificial life system

The present article introduces the system BioAnt, which is a computational simulation of a small colony of ants (up to 99 members) in which every ant relies on a biologically more plausible artificial neural networks as control mechanism for guidance. The environment, in which the ants are placed, is three-dimensional, consisting of the anthill, sugar, water, earth elevations, walls and predators. The ants’ foraging behavior was successfully implemented as well as some basic defense mechanisms. Typical sensors and actuators of ants were modeled and the efficiency of the connectionist approach has been validated by the comparison with a simple symbolical approach. Apart from several surprising results on technical details, which are reported, the present approach clearly demonstrates the feasibility of such an implementation with connectionist and biologically more plausible principles, offering promising perspectives as a basis for further artificial life systems.     

A platform-independent metamodel for multiagent systems

Various agent-oriented methodologies and metamodels exist to design and develop multiagent systems (MAS) in an abstract manner. Frequently, these frameworks specialise on particular parts of the MAS and only few works have been invested to derive a common standardisation. This limits the impact of agent-related systems in commercial applications. In this paper, we present a metamodel for agent systems that abstracts from existing agent-oriented methodologies, programming languages, and platforms and could thus be considered as platform-independent. This metamodel defines the abstract syntax of a proposed domain-specific modelling language for MAS that is currently under development and provides furthermore the base to generate code out of the generated designs. This is done by applying the principles of model-driven development (MDD) and providing two model transformations that allow transforming the generated models into textual code that can be executed with JACK and JADE.     

Infrastructures and tools for multiagent systems for the new generation of distributed systems

In order for multiagent systems to be included in real domains (media and Internet, logistics, e-commerce, and health care), infrastructures and tools for multiagent systems should provide efficiency, scalability, security, management, monitoring, and other features related to building real applications. Thus, infrastructures and tools that support multiagent systems are needed, especially those that promote the adoption of agent-based systems by designers and programmers in both academia and industry. This special issue is a selection of contributions whose preliminary versions were presented at the ITMAS 2010 workshop, which was held in conjunction with the International Conference on Autonomous Agents and Multi-agent Systems.     

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.     

Distributed constraint optimization with MULBS: A case study on collaborative meeting scheduling

This paper introduces MULBS, a new DCOP (distributed constraint optimization problem) algorithm and also presents a DCOP formulation for scheduling of distributed meetings in collaborative environments. Scheduling in CSCWD can be seen as a DCOP where variables represent time slots and values are resources of a production system (machines, raw-materials, hardware components, etc.) or management system (meetings, project tasks, human resources, money, etc). Therefore, a DCOP algorithm must find a set of variable assignments that maximize an objective function taking constraints into account. However, it is well known that such problems are NP-complete and that more research must be done to obtain feasible and reliable computational approaches. Thus, DCOP emerges as a very promising technique: the search space is decomposed into smaller spaces and agents solve local problems, collaborating in order to achieve a global solution. We show with empirical experiments that MULBS outperforms some of the state-of-the-art algorithms for DCOP, guaranteeing high quality solutions using less computational resources for the distributed meeting scheduling task.     

A familiarity-based trust model for effective selection of sellers in multiagent e-commerce systems

In electronic marketplaces, trust is modeled, for instance, in order to allow buying agents to make effective selection of selling agents. Familiarity is often considered to be an important factor in determining the level of trust. In previous research, familiarity between two agents has been simply assumed to be the similarity between them. We propose an improved familiarity measurement based on the exploration of factors that affect a human’s feelings of familiarity. We also carry out experiments to show that the trust model with our improved familiarity measurement is more effective and more stable.     

Finite-time coordination in multiagent systems using sliding mode control approach

Finite-time stability in dynamical systems theory involves systems whose trajectories converge to an equilibrium state in finite time. In this paper, we use the notion of finite-time stability to apply it to the problem of coordinated motion in multiagent systems. Specifically, we consider a group of agents described by fully actuated Euler–Lagrange dynamics along with a leader agent with an objective to reach and maintain a desired formation characterized by steady-state distances between the neighboring agents in finite time. We use graph theoretic notions to characterize communication topology in the network determined by the information flow directions and captured by the graph Laplacian matrix. Furthermore, using sliding mode control approach, we design decentralized control inputs for individual agents that use only data from the neighboring agents which directly communicate their state information to the current agent in order to drive the current agent to the desired steady state. Sliding mode control is known to drive the system states to the sliding surface in finite time. The key feature of our approach is in the design of non-smooth sliding surfaces such that, while on the sliding surface, the error states converge to the origin in finite time, thus ensuring finite-time coordination among the agents in the network. In addition, we discuss the case of switching communication topologies in multiagent systems. Finally, we show the efficacy of our theoretical results using an example of a multiagent system involving planar double integrator agents.     

A fault-tolerant approach to robot teams

As the applications of mobile robotics evolve it has become increasingly less practical for researchers to design custom hardware and control systems for each problem. This paper presents a new approach to control system design in order to look beyond end-of-lifecycle performance, and consider control system structure, flexibility, and extensibility. Towards these ends the Control ad libitum philosophy was proposed, stating that to make significant progress in the real-world application of mobile robot teams the control system must be structured such that teams can be formed in real-time from diverse components. The Control ad libitum philosophy was applied to the design of the HAA (Host, Avatar, Agent) architecture: a modular hierarchical framework built with provably correct distributed algorithms. A control system for mapping, exploration, and foraging was developed using the HAA architecture and evaluated in three experiments. First, the basic functionality of the HAA architecture was studied, specifically the ability to: (a) dynamically form the control system, (b) dynamically form the robot team, (c) dynamically form the processing network, and (d) handle heterogeneous teams and allocate robots between tasks based on their capabilities. Secondly, the control system was tested with different rates of software failure and was able to successfully complete its tasks even when each module was set to fail every 0.5–1.5 min. Thirdly, the control system was subjected to concurrent software and hardware failures, and was still able to complete a foraging task in a 216 m² environment.     

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.     

基于MAS的城市应急中心系统设计

面向Agent编程（Agent—Oriented Programming,AOP）是一种重要的软件设计思想。基于Agent技术的软件工程方法构建的多Agent系统（Multiagent System,MAS）具有模块化程度较高、运行速度较快、鲁棒性和扩充性较强等优点,因此,MAS构建的CSCW平台逐渐应用于很多领域。另一方面,现有的城市应急中心（110／122／119）系统都是独立封闭的系统、各中心之间缺乏协调能力、只能机械地执行规定的动作,不能满足社会联动多态性服务的要求,该文在分析现状和缺陷的基础上,将AOP软件设计思想引入到了城市应急中心系统设计（乃至一般的应急中心系统设计）中,提出了一种基于MAS的城市应急中心系统框架模型,并详细阐述了系统设计中所涉及的关键技术。     

A multiagent approach to managing air traffic flow

Intelligent air traffic flow management is one of the fundamental challenges facing the Federal Aviation Administration (FAA) today. FAA estimates put weather, routing decisions and airport condition induced delays at 1,682,700 h in 2007 (FAA OPSNET Data, US Department of Transportation website, ), resulting in a staggering economic loss of over $41 billion (Joint Economic Commission Majority Staff, Your flight has been delayed again, 2008). New solutions to the flow management are needed to accommodate the threefold increase in air traffic anticipated over the next two decades. Indeed, this is a complex problem where the interactions of changing conditions (e.g., weather), conflicting priorities (e.g., different airlines), limited resources (e.g., air traffic controllers) and heavy volume (e.g., over 40,000 flights over the US airspace) demand an adaptive and robust solution. In this paper we explore a multiagent algorithm where agents use reinforcement learning (RL) to reduce congestion through local actions. Each agent is associated with a fix (a specific location in 2D space) and has one of three actions: setting separation between airplanes, ordering ground delays or performing reroutes. We simulate air traffic using FACET which is an air traffic flow simulator developed at NASA and used extensively by the FAA and industry. Our FACET simulations on both artificial and real historical data from the Chicago and New York airspaces show that agents receiving personalized rewards reduce congestion by up to 80% over agents receiving a global reward and by up to 90% over a current industry approach (Monte Carlo estimation).     

Conceptualizing a shared language subsystem for distributed decision support systems

Researchers have only recently begun to develop formal architectures for support systems that capitalize on advancements in distributed technologies. Conceptual models have been proposed for distributed versions of both DSS and GDSS. To date, however, these developments have largely been limited to techniques employing shared global data spaces. Research on other approaches to distributed support systems development, including that based on the shared language techniques of distributed artificial intelligence, is still lagging. To introduce this research area, this paper reviews architectural principles for distributed decision support and develops the notion of a shared language as an extension of the DSS language subsystem.     

Semi-automated user agents in distributed change management

Flexible integration of distributed design and manufacturing activities is one of the key issues in applying just-in-time principles along the logistics chain. The potential of AI based approaches in this field is clear, but the inherent asynchrony, concurrency, and inconsistency of distributed operations has reduced enthusiasm so far.This paper discusses coordination in the context of geographically distributed manufacturing of one-of-a-kind products. Cooperation of partners is based on semi-automated user agents which operate as an integrative layer between more dedicated tools and systems. The management of order changes in elevator manufacture is used as a case example.The work reported here is part of the DIMUN project (DIMUN, 1989) in the RACE program.     

Integration framework for intelligent manufacturing processes

In this article, we describe a new approach to applying distributed artificial intelligence techniques to manufacturing processes. The construction of intelligent systems is one of the most important techniques among artificial intelligence research. Our goal is to develop an integrated intelligent system for real time manufacturing processes. An integrated intelligent system is a large knowledge integration environment that consists of several symbolic reasoning systems (expert systems) and numerical computation packages. These software programs are controlled by a meta-system which manages the selection, operation and communication of these programs. A meta-system can be implemented in different language environments and applied to many disciplines. This new architecture can serve as a universal configuration to develop high performance intelligent systems for many complicated industrial applications in real world domains.To whom all correspondence should be addressed.     

A migration-based approach towards resource-efficient wireless structural health monitoring

Wireless sensor networks have emerged as a complementary technology to conventional, cable-based systems for structural health monitoring. However, the wireless transmission of sensor data and the on-board execution of engineering analyses directly on the sensor nodes can consume a significant amount of the inherently restricted node resources. This paper presents an agent migration approach towards resource-efficient wireless sensor networks. Autonomous software agents, referred to as “on-board agents”, are embedded into the wireless sensor nodes employed for structural health monitoring performing simple resource-efficient routines to continuously analyze, aggregate, and communicate the sensor data to a central server. Once potential anomalies are detected in the observed structural system, the on-board agents autonomously request for specialized software programs (“migrating agents”) that physically migrate to the sensor nodes to analyze the suspected anomaly on demand. In addition to the localized data analyses, a central information pool available on the central server is accessible by the software agents (and by human users), facilitating a distributed-cooperative assessment of the global condition of the monitored structure. As a result of this study, a 95% reduction of memory utilization and a 96% reduction of power consumption of the wireless sensor nodes have been achieved as compared with traditional approaches.     

基于Agent人工智能技术的分布式入侵检测系统设计

针对当前IDA系统中由于数据集中处理缺陷,影响了系统入侵检测精准性。提出了基于Agent人工智能技术的分布式入侵检测系统设计。在系统总体结构支持下,分析控制中心、网络主机、分区控制中心和Agent库。根据响应库中的响应规则采取对应的响应策略,利用通信模块及时判断入侵行为是否异常,使用S5720S-28P-SI-AC 24口全千兆三层网管企业级网络核心交换机,进行数据交换。选择AD2032型号的报警响应器,能够监视外来入侵行为。通过V1.2绿色电脑信息检测器,对系统内存和驱动磁盘进行全方位评估。分析主体通信的实现方式、通信消息格式和通信协议,设计以Agent为基础的数据移动过程。借助Libpcap库函数,设计入侵检测流程。设置攻击环境与参数,由系统调试结果可知,该系统最高检测精准度可达到99%,为保证网络安全使用提供设备支持。     

An agent-based approach to tool integration

Tool integration is a very difficult challenge. Problems may arise at different abstraction levels and from several sources such as heterogeneity of manipulated data, incompatible interfaces, or uncoordinated services, to name just a few examples. On the other hand, applications based on the coherent composition of activities, components, services, and data from heterogeneous sources are increasingly present in our everyday lives. Consequently, tool integration takes on increasing significance.In this paper we analyze the tool-integration problem at different abstraction levels and discuss different views on a layered software architecture that we have designed specifically for a middleware that supports the execution of distributed applications for the orchestration of human/system activities. We noticed that the agent paradigm provided a suitable technology for abstraction in tool integration. Throughout the paper, the discussion refers to a case study in the bioinformatics domain.