MASACAD: A multi-agent approach to information customization for the purpose of academic advising of students

The growth and advancement in the Internet and the World Wide Web has led to an explosion in the amount of available information. This staggering amount of information has made it extremely difficult for users to locate and retrieve information that is actually relevant to their task at hand. Dealing with this problem of “information overload” will need tools to customize the information space. In this paper we present MASACAD, a multi-agent system that learns to advise students by mining the Web and discuss important problems in relationship to information customization systems and smooth the way for possible solutions. The main idea is to approach information customization using a multi-agent paradigm in combination with a number of aspects from the domains of machine learning, user modeling, and Web mining.     

A modern approach to multiagent development

Multiagent systems (MAS) development frameworks aim at facilitating the development and administration of agent-based applications. Currently relevant tools, such as JADE, offer huge possibilities but they are generally linked to a specific technology (commonly Java). This fact may limit some application domains when deploying MAS, such as low efficiency or programming language restrictions. To contribute to the evolution of multiagent development tools and to overcome these constraints, we introduce a multiagent platform based on the FIPA standards and built on top of a modern object-oriented middleware. Experimental results prove the scalability and the short response-time of the proposal and justify the design and development of modern tools to contribute the multiagent technology.     

Distributed scheduling to support a call center: A cooperative multiagent approach

This article describes a multiagent system architecture to increase the value of 24-hour-a day call center service. This system supports call centers in making appointments with clients on the basis ofknowledge ofemployees and their schedules. Relevant activities are scheduled for employees in preparation ofsuch appointments. The multiagent system architecture is based on principled design, using the compositional development method for multiagent systems DESIRE. To schedule procedures in which more than one employee is involved, each employee is represented by its own personal assistant agent, and a work manager agent coordinates the schedules of the personal assistant agents and clients through the call center. The multiagent system architecture has been applied to the banking domain, in cooperation with and partially funded by the Rabobank.     

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).     

Distributed problem solving in multiagent systems: a spring net approach

This paper presents a new spring net approach for distributed problem solving in MAS. Distributed artificial intelligence consists of distributed problem solving and multi-agent systems. We extend such specialized DPS and MASs to a general MAS, such that an agent may make a trade-off between selfishness and unselfishness, thus adjusting its own personality and autonomy. This alternative to traditional approaches can deal with a variety of complicated social interactions and autonomous behaviors occurring in multiagent systems.     

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.     

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.     

Simulation and evaluation of urban bus-networks using a multiagent approach

Evolution of public road transportation systems requires analysis and planning tools to improve service quality. A wide range of road transportation simulation tools exist with a variety of applications in planning, training and demonstration. However, few simulation models take into account traveler behaviors and vehicle operation specific to public transportation. We present in this paper a bus-network simulation tools which include these specificities and allows to analyze and evaluate a bus-network at diverse space and time scales. We adopt a multiagent approach to describe the global system operation as behaviors of numerous autonomous entities such as buses and travelers.     

Planning for dynamic multiagent planar manipulation with uncertainty: a game theoretic approach

This paper addresses the planning problem for multiagent dynamic manipulation in the plane. The objective of planning is to design the forces exerted on the object by agents with which the object can follow a given trajectory in spite of the uncertainty on pressure distribution. The main novelty of the proposed approach is the integration of noncooperative and cooperative games between agents in an hierarchical manner. Based on a dynamic model of the pushed object, the coordination problem is solved in two levels. In the lower control level, a fictitious force controller is designed by using a minimax technique to achieve the tracking performance. The design procedure is divided into two steps. First, a linear nominal controller is designed via full-state linearization with desired eigenvalues assignment. Next, a minimax control scheme is specified to optimally attenuate the worst-case effect of the uncertainty due to pressure distribution and achieve a minimax tracking performance. In the coordination level, a cooperative game is formulated between agents to distribute the fictitious force, and the objective of the game is to minimize the worst-case interaction force between agents and the object. Simulations are carried out for two-agent and three-agent manipulations, results demonstrate the effectiveness of the planning method.     

Towards efficient multiagent task allocation in the RoboCup Rescue: a biologically-inspired approach

This paper addresses team formation in the RoboCup Rescue centered on task allocation. We follow a previous approach that is based on so-called extreme teams, which have four key characteristics: agents act in domains that are dynamic; agents may perform multiple tasks; agents have overlapping functionality regarding the execution of each task but differing levels of capability; and some tasks may depict constraints such as simultaneous execution. So far these four characteristics have not been fully tested in domains such as the RoboCup Rescue. We use a swarm intelligence based approach, address all characteristics, and compare it to other two GAP-based algorithms. Experiments where computational effort, communication load, and the score obtained in the RoboCup Rescue aremeasured, show that our approach outperforms the others.     

An approach to the pursuit problem on a heterogeneous multiagent system using reinforcement learning

Cooperation among agents is important for multiagent systems having a shared goal. In this paper, an example of the pursuit problem is studied, in which four hunters collaborate to catch a target. A reinforcement learning algorithm is employed to model how the hunters acquire this cooperative behavior to achieve the task. In order to apply Q-learning, which is one way of reinforcement learning, two kinds of prediction are needed for each hunter agent. One is the location of the other hunter agents and target agent, and the other is the movement direction of the target agent at next time step t. In our treatment we extend the standard problem to systems with heterogeneous agents. One motivation for this is that the target agent and hunter agents have differing abilities. In addition, even though those hunter agents are homogeneous at the beginning of the problem, their abilities become heterogeneous in the learning process. Simulations of this pursuit problem were performed on a continuous action state space, the results of which are displayed, accompanied by a discussion of their outcomes’ dependence upon the initial locations of the hunters and the speeds of the hunters and a target.     

Soft systems methodology to information systems: a Wittgensteinian approach

Abstract. A logical foundation for information system design requires a theory of meaning. Ideational theories attach meaning to the ideas in the private world of a conscious subject. By contrast Wittgenstein held that language and meaning were primarily public and that a private, purely subjective, language was impossible. The iterative debate among stake-holders that takes place in the practice of soft systems methodology (SSM) can be understood as a Wittgensteinian language game in which meaning is created not just discovered. The conceptual models used in SSM can be developed into logico-linguistic models which express stipulative definitions. These definitions can be taken as a logical basis for information system design.     

Favor-based decision: A novel approach to modeling the strategy diffusion in causal multiagent societies

In previous work on collective motion, agents always tend to imitate the behavior strategies of higher ranks; this model is called rank-based strategy diffusion. Unfortunately, this model is, by itself, insufficient in causal multiagent societies where agents may have causal links with each other. In causal environments, agents will develop positive (or negative) attitudes (favor) about those who can increase (or decrease) their own utilities. Naturally, for collective motion, agents will be inclined to imitate those who are well-favored and avoid those who are disfavored. This paper presents the concept of favor in causal environments, and presents a model for favor-based strategy diffusion. In the proposed model, agents in causal environments are inclined to associate with and imitate the strategies of those who are well-favored. Obviously, such diffusion effects well reflect the impact of causal relations in the real world.     

一种基于互信息的图像频域配准算法

图像配准是多源图像分析的关键步骤,是图像应用的基础。频域配准方法具有配准精度高和速度快的优点。P.Vandewalle的频域配准算法明显优于其他频域算法和一些空间域算法,对该算法进行了改进,仅使用了一半图像频谱灰度,在对分块后的频谱灰度进行分析时引入了互信息理论,实现了配准精度更高、速度更快的基于互信息的图像频域配准算法。     

A hierarchical approach to elastic registration based on mutual information

A hierarchical approach to elastic registration based on mutual information, in which the images are progressively subdivided, locally registered, and elastically interpolated, is presented. To improve the registration, a combination of prior and floating information on the joint probability is proposed. It is shown that such a combination increases the registration speed at the coarser levels in hierarchy, enables a registration of finer details, and provides additional guidance to the optimisation process. Besides, a threefold local registration consistency test and correction of shading were employed to increase the overall registration performance. The proposed hierarchical method for elastic registration was tested on an experimental database of 2D images of histochemically differently stained serial cross-sections of human skeletal muscle. The obtained results show that 95% of the images could be successfully registered. The inclusion of prior information is an important break through that may enable routine use of the mutual information cost function in a variety of 2D and 3D image registration algorithms in the future.     

基于多智能体的多机群网格模型

在由多计算机机群构成的网格环境下,为了实现数据并行型计算,提出了一个基于多智能体机制的网格开发模型.给出了由多计算机机群组成的网格、逻辑计算机机群、数据并行型计算和一系列Agent的定义.利用管理智能体、独立计算智能体、协同计算智能体以及协同计算组之间的协同计算机制来实现数据并行型计算.描述了网格计算过程.实践表明,该模型有效地适应了多机群网格环境的异构性、动态性等特性,提高了计算资源的利用率.该模型适合于基于网格的并行型计算.