Evaluation of automated-trading strategies using an artificial market

The purpose of this study was to extend the simulation of artificial markets to the practical application. We constructed an artificial-market system with an interface with the automated-trading strategies. Then, using an artificial-market simulation, we conducted two types of evaluations of automated-trading strategies that could participate in a Kaburobo competition. (1) Our system evaluated the risks and returns of the strategies in various market environments. An evaluation using the artificial market was able to provide better information than a conventional evaluation using a back test. (2) Our system could also test the market impact of automated-trading strategies. Our results revealed that the market impact of the strategies may not only depend on their rule content but also on the way they are combined with other strategies.     

Development of an artificial market model based on a field study

An artificial market model for analyzing economic phenomena has been developed based on the results of a field study. In the construction of the model, the forms of the virtual dealers' utility function and learning methods were derived from field data. The results of simulating the artificial market were compared with the field data. First, the temporal movements of agents' forecasts in the simulation were compared with those of actual dealers as given by survey data. Next, the factors that the virtual dealers regarded as significant in forecasting were categorized. The categories were compared to those of actual dealers and found to be similar. Finally, the temporal changes in virtual dealers' opinions were compared with data obtained through interviews with actual dealers. The proposed model was found to enable repeated and detailed analysis of dealer thinking under various conditions in real time.     

基于多Agent的新产品市场扩散仿真研究

基于多Agent的仿真是一种基于微观层次的建模仿真方法,具有广泛适用性、分布性、智能性和交互性等特征。该文探讨了多Agent仿真的基本思路和系统分析过程,构建了一个基于多Agent的新产品市场扩散仿真模型,并在AgentSheets平台上实现了该模型。     

网络化系统下的企业战略级经营评价决策支持系统研究

网络化系统是指网络环境下的复杂系统,现代企业正是处于典型的此类系统。文章分析了目前企业决策支持系统的存在的问题,并针对实现一个企业战略级经营评价决策支持系统提出了相应的解决方法、策略和支持技术。     

基于多Agent的机车电路仿真及其实现

针对机车电路复杂的连锁关系以及现有仿真建模方法存在的问题,本文提出了基于多Agent机车仿真建模方法,构建了基于多Agent的仿真模型并在JADE平台上仿真实现,结果表明利用多Agent间的交互、协作来实现机车内部电路的自治化,是一种更可行、更经济且具有人工智能特性的独特方法。     

一个基于XML和多Agent系统的远程教学模型

针对目前远程教学中所存在的一些不足，将多Agent系统和XML技术引入到远程教学中，从而建立了一个含有四类Agent、八种Web服务的远程教学模型XA_DTS。通过该模型，远程教学能够实现有针对性的个性化教学，同时能够激发学生的主观能动性，促进学生的主动学习。     

一个基于XML和多Agent系统的远程教学模型

针对目前远程教学中所存在的一些不足,将多Agent系统和XML技术引入到远程教学中,从而建立了一个含有四类Agent、八种Web服务的远程教学模型XA_DTS。通过该模型,远程教学能够实现有针对性的个性化教学,同时能够激发学生的主观能动性,促进学生的主动学习。     

The study of protection coordination technology in power system based on Multi-Agent

This paper studies mainly how to make use of Multi-Agent technology to solve the complex problems which are caused by unsuitable coordination between protection and untimely action and protection fault activity in large power system, and realizes timely the coordination among protections effectively in when losing some or all information. Thus it overcomes the difficult of solving single or shore range information, enhances correspond and coordination among the intelligent protect Agent, raises the stable and reliable property, and promotes the process of power network for large area.     

Perspectives on systems biology

Systems biology is a new field in biology that aims at system-level understanding of biological systems, such as cells and organisms. Molecular biology has already made remarkable contribution to our understanding of biological systems, and its current focus is on the identification of genes and the functions of their products; that is, on the components of systems. There is no doubt that molecular biology will progress even faster and finally identify all the components of biological systems. As such a moment approaches, major importance need to be placed on the establishment of methodologies and techniques that enable us to understand biological systems as systems. This paper overviews the field of systems biology. Hiroaki Kitano, Ph.D.: Hiroaki Kitano is a Senior Researcher at Sony Computer Science Laboratories, Inc., a Project Director of Kitano Symbiotic Systems Project, Japan Science and Technology Corporation and a visiting associate at California Institute of Technology. He received a B.A. in Physics from the International Christian University, Tokyo, and a Ph.D. in Computer Science from Kyoto University. Since 1988, he has been a visiting researcher at the Center for Machine Translation at Carnegie Mellon University. Kitano received Computers and Thought Award from the International Joint Conferences on Artificial Intelligence in 1993. His research interests include RoboCup, computational molecular biology, engineering use of the mophogenesis process, and evolutionary systems.     

基于多Agent的天战系统建模与仿真方法研究

简单介绍了复杂系统理论及其建模方法,分析了天战系统的总体行为特征,指出对天战系统的仿真研究需采用复杂适应系统（CAS）理论的研究方法,即采用基于Agent的建模仿真方法学来开展研究,概述了Agent和多Agent系统的概念及基于多Agent的仿真,并给出了一个基于高层体系结构（HLA）的天战系统的仿真框架,最后展望了将来的研究工作。     

基于智能多代理技术的电力系统协调保护

本文主要研究如何合理利用计算机人工智能多代理（Multi-Agent）技术,来解决大电力系统中不断出现的因保护配合不当、保护动作不能及时或失误等复杂问题,分别实现了保护在正常运行、失去局部或全部对侧信息的情况下,能适时的、有效地实现保护间的协调,从而克服了单智能Agent只能解决和处理单一的、小范围信息的困难,加强了各智能保护Agent之间的通信和协调,提高大电力系统的稳定性和可靠性,促进大区域电网联网的进程。     

一种基于多智能体系统的机器人群组织形式

提出了一种基于多智能体的机器人群高层的分层式组织形式及运行机制,它包括机器人组织的定义、组成类型以及组成的实现算法。机器人社会中的组员既可独立隶属于一个群体,也可动态分属于多个群体,该结构不仅有效地利用了资源,而且还可以提高系统的鲁棒性和抗毁性。     

基于神经网络的协商学习机制

多agent协商研究中,如何通过学习提高协商效率是一个重要的课题,目前的研究多采用简单的学习算法,学习效果不好。论文首先提出了一个两方多回合交互协商框架,然后依据协商历史结果、协商双方初次出价等信知,对协商结果信息进行预测,从而确定协商交互中的推理策略,并利用BP神经网络的自适应、自学习能力对协商结果预测机制进行学习。随后的验证系统表明,该机制通过对协商结果的有效预测,提高了协商交互的效率和协商个体的效用。     

Identification of load pockets and market power in electric power systems

In this paper, we present a spectral method for the identification of load pockets and the application of practical techniques for the measurement of market power. Market power is a serious concern in electric energy markets, especially in the area of a load pocket. Common definitions for market power, which rely on a comparison between market prices and a so-called competitive price, are difficult to use in practice because the competitive price is not known when the market is not competitive. The competitive price cannot be computed from data naturally available to the market. Our technique focuses on the identification of participants with the ability to increase revenues by increasing prices, an ability not present in a competitive market. We then propose measures for quantifying the extent to which market power is being exercised. These measures can be computed from data available to the market; they are practical. We present results from a 30 bus, 6 generator system, which illustrates that generators in a load pocket have and can exploit joint market power.     

Cluster analysis in industrial market segmentation through artificial neural network

Market segmentation has commonly applied cluster analysis. This study intends to make the comparison of conventional two-stage method with proposed two-stage method through the simulated data. The proposed two-stage method is the combination of self-organizing feature maps and K-means method. The simulation results show that the proposed scheme is better than the conventional two-stage method based on the rate of misclassification.     

Automatic multiple circle detection based on artificial immune systems

Hough transform (HT) has been the most common method for circle detection, exhibiting robustness but adversely demanding a considerable computational load and large storage. Alternative approaches for multiple circle detection include heuristic methods built over iterative optimization procedures which confine the search to only one circle per optimization cycle yielding longer execution times. On the other hand, artificial immune systems (AIS) mimic the behavior of the natural immune system for solving complex optimization problems. The clonal selection algorithm (CSA) is arguably the most widely employed AIS approach. It is an effective search method which optimizes its response according to the relationship between patterns to be identified, i.e. antigens (Ags) and their feasible solutions also known as antibodies (Abs). Although CSA converges to one global optimum, its incorporated CSA-Memory holds valuable information regarding other local minima which have emerged during the optimization process. Accordingly, the detection is considered as a multi-modal optimization problem which supports the detection of multiple circular shapes through only one optimization procedure. The algorithm uses a combination of three non-collinear edge points as parameters to determine circles candidates. A matching function determines if such circle candidates are actually present in the image. Guided by the values of such function, the set of encoded candidate circles are evolved through the CSA so the best candidate (global optimum) can fit into an actual circle within the edge map of the image. Once the optimization process has finished, the CSA-Memory is revisited in order to find other local optima representing potential circle candidates. The overall approach is a fast multiple-circle detector despite considering complicated conditions in the image.     

A Nash equilibrium simulation model for the competitiveness evaluation of the auction based day ahead electricity market

This paper presents a simulation model based on the Nash equilibrium notion for the auction based day ahead electricity generation market. The presented model enhances a previous formalism proposed in the related literature by employing empirical data distributions of the market clearing price as registered by the market authority (e.g. the Independent System Operator). The model is effective when power suppliers with different generation capacities are considered, differently from the starting model that unrealistically assumes equal capacities. The proposed approach aims at evaluating the electricity market competitiveness with regard to the bidder strategies in order to prevent their anticompetitive actions. The framework is applied to a real data set regarding the Italian electricity market to enlighten its effectiveness in different scenarios, varying the number and capacity of participating bidders. The model can be employed as a basis for a decision support tool both for market participants (to define their optimal bidding strategy) and regulators (to avoid collusive strategies).     

Market micro-structure analysis by multiagent simulation in X-Economy -- comparison among technical indices

We are developing an agent and server library referred to as X-Economy, by which we can execute multiagent simulations and network games for financial and economic systems. To this end, we analyzed the characteristics of network games in a financial context and compared them with traditional ones. X-Economy has also provided a new research direction in market micro-structure analysis. We executed several kinds of multiagent simulations for technical traders (indices) and obtained non-trivial suggestions regarding the relationship between the market randomness and the effectiveness of technical indices. For instance, the performance of complex technical indices seemed to deeply depend on the characteristics and nature of a market when a market became complex, i.e. it moved far from the Wiener process.     

Design principles of adaptive cellular immunity for artificial immune systems

Artificial immune systems (AISs) have been proposed as a new computing paradigm. This paper reviews design principles of adaptive cellular immunity, based on the immunological literature rather than the simplified mathematical models which have thus far dominated the development of framework for design, interpretation, and application of AISs. An earlier version of this work was presented as a position paper at the ARTIST Network for Artificial Immune Systems meeting held on 8th–9th November 2004.