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.     

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.     

呼叫中心智能排班系统关键技术

分析学习率和训练精度对BP神经网络训练最大次数、收敛时间和话务量预测精度的影响;根据呼叫中心历史话务量数据的日变化特点,提出并验证采用分时间段多次调用BP神经网络模型的方法比整体预测所得话务量预测结果精度更高;基于话务量预测结果,使用Erlang-C公式进行坐席数预测,结合呼叫中心的典型班次、设定的服务水平等参数进行坐席数曲线拟合,得到每个典型班次对应的话务员数量;开发呼叫中心智能排班系统,通过合理的排班实现大型呼叫中心资源的合理配置。     

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.     

用CTI中间件实现呼叫中心的多媒体接入

现代多媒体交互中心的主要设备CTI中间件,需要实现对电话渠道、Email渠道、Web互动渠道的统一管理,并能够根据用户信息和座席技能、工作状态等多种因素,将呼叫进行统一分配和路由,由最适合的座席进行应答和处理。论文论述了一个支持多媒体统一排队的CTI中间件的设计,对软件总体结构进行了设计,并对该软件中实现多媒体统一接入的媒体服务器模块进行了深入的探讨。该软件已经投入到实际的应用中,实践证明,利用该软件可以提示呼叫中心的服务水平。     

CAMPS: a constraint-based architecturefor multiagent planning and scheduling

A new integrated architecture for distributed planning and scheduling is proposed that exploits constraints for problem decomposition and coordination. The goal is to develop an efficient method to solve densely constrained planning/scheduling problems in a distributed manner without sacrificing solution quality. A prototype system (CAMPS) was implemented, in which a set of intelligent agents try to coordinate their actions for satisfying planning/scheduling results by handling several intra- and inter-agent constraints. The repair-based methodology for distributed planning/scheduling is described, together with the constraint-based mechanism of dynamic coalition formation among agents.     

MASACAD: a multiagent based approach to information customization

MASACAD is a multiagent information customization system that adopts the machine-learning paradigm to advise students by mining the Web. In the distributed problem-solving paradigm, systems can distribute among themselves the processes necessary to accomplish a given task. Given the number of problems that distributed processing can address, AI researchers have directed significant effort toward developing specialized problem-solving systems that can interact in their search for a solution. The multiagent-system paradigm embodies this approach.     

A sequential cooperative game theoretic approach to scheduling multiple large-scale applications in grids

Scheduling large-scale applications in heterogeneous distributed computing systems is a fundamental NP-complete problem that is critical to obtaining good performance and execution cost. In this paper, we address the scheduling problem of an important class of large-scale Grid applications inspired by the real world, characterized by a huge number of homogeneous, concurrent, and computationally intensive tasks that are the main sources of performance, cost, and storage bottlenecks. We propose a new formulation of this problem based on a cooperative distributed game-theory-based method applied using three algorithms with low time complexity for optimizing three important metrics in scientific computing: execution time, economic cost, and storage requirements. We present comprehensive experiments using simulation and real-world applications that demonstrate the effectiveness of our approach in terms of time and fairness compared to other related algorithms.     

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 clustering for group formation and task allocation in multiagent systems: A swarm intelligence approach

Most clustering methods rely on central data structures and/or cannot cope with dynamically changing settings. Besides, these methods need some hints about the target clustering. However, issues related to the current use of Internet resources (distribution of data, privacy, etc.) require new ways of dealing with data clustering. In multiagent systems this is also becoming an issue as one wishes to group agents according to some features of the environment in order to have agents accomplishing the available tasks in an efficient way. In this paper we discuss the application of a clustering algorithm that is inspired by swarm intelligence techniques such as organization of bee colonies and task allocation among social insects. This application involves a complex task allocation scenario, the RoboCup Rescue, where tasks with different characteristics must be allocated to agents with different capabilities. Our results have shown that clustering agents is effective in this scenario as agents act in a more coordinated way.     

Dynamic decision support framework for production scheduling using a combined genetic algorithm and multiagent model

Due to the dynamic nature, complexity, and interactivity of production scheduling in an actual business environment, suitable combined and hybrid methods are necessary. This paper takes prefabricated concrete components as an example and develops the dynamic decision support framework based on a genetic algorithm and multiagent system (MAS) to optimize and simulate the production scheduling. First, a multiobjective genetic algorithm is integrated into the MAS for preliminary optimization and a series of near‐optimal solutions are obtained. Subsequently, considering the resource constraints and uncertainties, the MAS is used to simulate complex real‐world production environments. Considering the different types of uncertainty factors, the paper proposes the corresponding dynamic scheduling method and uses MAS to generate the optimal production schedule. Finally, a practical prefabricated construction case is used to validate the proposed model. The results show that the model can effectively address the occurrence of uncertain events and can provide dynamic decision support for production scheduling.     

基于Asterisk与OpenVPN的企业集团IP分布式呼叫中心

构建一个企业集团的IP分布式呼叫中心,其成本、安全性、稳定性和高效性是必须考虑的重要因素。在给出企业集团IP分布式呼叫中心架构的同时,以一个具体的实例阐述了呼叫中心实现的技术方案与实现过程。系统采用OpenVPN保证语音传输安全,运用智能路由的规则来处理各种话务,提出了以Asterisk系统内部交换协议(IAX2)为中继的通信模式。测试结果表明,在CPU使用率、负载、带宽占用、延时和语音质量方面,基于Asterisk与OpenVPN的IAX2中继通信模式都优于代表性的SIP中继模式,所提出的方案可以解决抖动带来的声音延时,避免声音传输时的断续现象,弥补由于包的顺序错乱而导致的语音出错,改善了VoIP通信的语音质量。     

Holonic job shop scheduling using a multiagent system

Manufacturing job shop scheduling is a notoriously difficult problem that lends itself to various approaches - from optimal algorithms to suboptimal heuristics. We combined popular heuristic job shop-scheduling approaches with emerging AI techniques to create a dynamic and responsive scheduler. We fashioned our job shop scheduler's architecture around recent holonic manufacturing systems architectures and implemented our system using multiagent systems. Our scheduling approach is based on evolutionary algorithms but differs from common approaches by evolving the scheduler rather than the schedule. A holonic, multiagent systems approach to manufacturing job shop scheduling evolves the schedule creation rules rather than the schedule itself. The authors test their approach using a benchmark agent-based scheduling problem and compare performance results with other heuristic-scheduling approaches.     

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.     

Internal model approach to cooperative robust output regulation for linear uncertain time‐delay multiagent systems

In this paper, we study the cooperative robust output regulation problem for linear uncertain multiagent systems with both communication delay and input delay by the distributed internal model approach. The problem includes the leader‐following consensus problem of linear multiagent systems with time delay as a special case. We first generalize the internal model design method to systems with both communication delay and input delay. Then, under a set of standard assumptions, we have obtained the solution to the problem via both the state feedback control law and the output feedback control law. In contrast to the existing results, our results apply to general linear uncertain multiagent systems, accommodate a large class of leader signals, and achieve asymptotic tracking and disturbance rejection at the same time.     

A novel approach to determine a tactical and operational decision for dynamic appointment scheduling at nuclear medical center

In this research, appointment scheduling is addressed in a nuclear medical center. A finite-horizon Markov Decision Process as dynamic programming is applied to formulate the problem by considering the patients' choice behavior, and different no-show rate for patients. The proposed model determines a tactical and operational decision for patient appointments. Based on the tactical decision; How many patients request for hospitalization as they call in and to what slot should they be assigned? According to the operational decision, should a walk-in patient hospitalization request be accepted? Also, this decision determines which patients must receive the services for each slot. One of the distinguishing contributions of this research is that two algorithms and one mathematical programming are developed hierarchically to solve exactly and deal with an intractable dimension of the Markov Decision Process model. Simulation tools are applied to compare the performance of optimal policies with First-Come-First-Serve policy based on a real case. The results show that the proposed model presents a more effective and efficient scheduling compared with current policies for scheduling. More revenue, lower patients waiting during the working day, and lower postponed patients are the results of the proposed model rather than the current policies for scheduling. Then, the impact of revenues, waiting costs, penalty costs, and center’s capacity on the results has been investigated. By increasing revenue and capacity and decreasing waiting costs and penalty costs, the total net revenue is increased.     

ExPlanTech: multiagent support for manufacturing decision making

ExPlanTech's multiagent approach offers a unified framework for decision-making support and provides a proven alternative to known mathematical and system science-modeling technologies for simulating the manufacturing process. ExPlanTech provides technological support for various manufacturing problems and comprises different components, which you can assemble to develop a customized system that supports a user's decision making in different aspects of production planning. The system should help human users size resources and time requirements for a particular order, creating production plans, optimizing material resources manipulation, managing and optimizing supply chain relationships, visualizing and analyzing medium- and long-term manufacturing processes, and accessing external data.     

The cooperative estimation of distribution algorithm: a novel approach for semiconductor final test scheduling problems

A large number of studies have been conducted in the area of semiconductor final test scheduling (SFTS) problems. As a specific example of the simultaneous multiple resources scheduling problem, intelligent manufacturing planning and scheduling based on meta-heuristic methods, such as the genetic algorithm (GA), simulated annealing, and particle swarm optimization, have become common tools for finding satisfactory solutions within reasonable computational times in real settings. However, only a few studies have analyzed the effects of interdependent relations during group decision-making activities. Moreover, for complex and large problems, local constraints and objectives from each managerial entity and their contributions toward global objectives cannot be effectively represented in a single model. This paper proposes a novel cooperative estimation of distribution algorithm (CEDA) to overcome these challenges. The CEDA extends a co-evolutionary framework incorporating a divide-and-conquer strategy. Numerous experiments have been conducted, and the results confirmed that CEDA outperforms hybrid GAs for several SFTS problems.     

A decomposition approach to multi-vehicle cooperative control

We use a decomposition approach to generate cooperative strategies for a class of multi-vehicle control problems. By introducing a set of tasks to be completed by the team of vehicles and a task execution method for each vehicle, we decompose the problem into a combinatorial component and a continuous component. The continuous component of the problem is captured by task execution, and the combinatorial component is captured by task assignment. In this paper, we present a solver for task assignment that generates near-optimal assignments quickly and can be used in real-time applications. To motivate our methods, we apply them to an adversarial game between two teams of vehicles. One team is governed by simple rules and the other by our algorithms. In our study of this game we found phase transitions, showing that the task assignment problem is most difficult to solve when the capabilities of the adversaries are comparable. Finally, we utilize our algorithms in a hierarchical model predictive control architecture with a variable replanning rate at each level to provide feedback in dynamically changing and uncertain environments.     

利用VBA及API32实现报表的动态设计

利用VBA和API将Windows和MicrosoftOffice系列应用软件的功能巧妙结合在一起,开发出一种新的具有较强实用性的报表制作方法。