A Strategy of Dynamic Reasoning in Knowledge-Based System with Fuzzy Production Rules

Fuzzy production rules have been successfully applied to represent uncertainty in a knowledge-based system. The knowledge organized as a knowledge base is static. On the other hand, a real system such as the stock market is dynamic in nature. Therefore we need a strategy to reflect the dynamic nature of a system when we make reasoning with a knowledge-based system.This paper proposes a strategy of dynamic reasoning that can be used to takes account the dynamic behavior of decision-making with the knowledge-based system consisted of fuzzy rules. A degree of match (DM) between actual input information and antecedent of a rule is represented by a value in interval [0, 1]. Weights of relative importance of attributes in a rule are obtained by the AHP (Analytic Hierarchy Process) method. Then these weights are applied as exponents for the DM, and the DMs in a rule are combined, with the Min operator, into a single DM for the rule. In this way, the importance of attributes of a rule, which can be changed from time to time, can be reflected to reasoning in knowledge-based system with fuzzy rules.With the proposed reasoning procedure, a decision maker can take his judgment on the given decision environment into a static knowledge base with fuzzy rules when he makes decision with the knowledge base. This procedure can be automated as a pre-processing system for fuzzy expert systems. Thereby the quality of decisions could be enhanced.     

基于Multi Agent的图象理解

最近十多年来,具有专家系统外壳的图象理解软件的开发一直是一个重要的研究课题。然而,多数基于知识的图象理解系统或者局限于诸如分割之类的特定操作上,或者局限于像理解建筑物的航空图象这样特定的应用。由于这些系统的目标和知识结构的局限性,因而不能将它们推广到其它领域。此研究就是试图解决此问题：首先将Agent构造环境扩展为一个Muliti Agent的图象理解系统,而此系统是知识入口的开发工具,它为用户提供一个类似于专家系统外壳的界面。这样一方面能加速图象理解系统的开发,另一方面方便那些缺乏图象理解知识的人开发图象理解应用程序。     

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.     

Communications Systems Driven by Software Agent Technology

The application of software agent technology to the management of communications' infrastructures is a challenging domain as it requires management on different time scales and has many interacting components. This paper looks at the potential benefits that may be gained from the application of agent technology to communications systems and surveys recent developments. Recent work using distributed network management by adopting co-operating and self-interested agent models of collaboration are described. The paper provides an introduction to the authors' perceptions of agent technology, followed by a discussion of some issues that need to be addressed for agent technology to be of practical use in the communications domain. Following sections describe how agent technology has been used for network management, legacy telecommunications systems, and telecommunications integration. The authors discuss how the emerging standards for agent technology can be used in an applied situation of providing a virtual private network and mobile agent technology used for fully distributed network control.     

基于强化学习的多Agent系统

1 引言强化学习允许自主Agent能够在没有关于任务和环境的先验知识的条件下通过不断地反射学习提高自己完成任务的能力,但是强化学习需要大量的计算,也就意味着大量的时间的消耗。对于许多实时系统的计算能力是一个大的挑战。进一步,如果是在多个Agent组成的Agent社会中,每个Agent的行动都有可能导致环境状态的改变,使得普通的相对单个     

A retraining methodology for enhancing agent intelligence

Data mining has proven a successful gateway for discovering useful knowledge and for enhancing business intelligence in a range of application fields. Incorporating this knowledge into already deployed applications, though, is highly impractical, since it requires reconfigurable software architectures, as well as human expert consulting. In an attempt to overcome this deficiency, we have developed Agent Academy, an integrated development framework that supports both design and control of multi-agent systems (MAS), as well as “agent training”. We define agent training as the automated incorporation of logic structures generated through data mining into the agents of the system. The increased flexibility and cooperation primitives of MAS, augmented with the training and retraining capabilities of Agent Academy, provide a powerful means for the dynamic exploitation of data mining extracted knowledge. In this paper, we present the methodology and tools for agent retraining. Through experimented results with the Agent Academy platform, we demonstrate how the extracted knowledge can be formulated and how retraining can lead to the improvement – in the long run – of agent intelligence.     

Enabling run-time composition and support for heterogeneous pervasive multi-agent systems

User needs-driven and computer-supported development of pervasive heterogeneous and dynamic multi-agent systems remains a great challenge for agent research community. This paper presents an innovative approach to composing, validating and supporting multi-agent systems at run-time. Multi-agent systems (MASs) can and should be assembled quasi-automatically and dynamically based on high-level user specifications which are transformed into a shared and common goal–mission. Dynamically generating agents could also be supported as a pervasive service. Heterogeneity of MASs refers to diverse functionality and constituency of the system which include mobile as well as host associated software agents. This paper proposes and demonstrates on-demand and just-in-time agent composition approach which is combined with run-time support for MASs. Run-time support is based on mission cost-efficiency and shared objectives which enable termination, generation, injection and replacement of software agents as the mission evolves at run-time. We present the formal underpinning of our approach and describe the prototype tool – called eHermes, which has been implemented using available agent platforms. Analysis and results of evaluating eHermes are presented and discussed.     

Modeling conversation policies using permissions and obligations

Both conversation specifications and policies are required to facilitate effective agent communication. Specifications provide the order in which speech acts can occur in a meaningful conversation, whereas policies restrict the specifications that can be used in a certain conversation based on the sender, receiver, messages exchanged thus far, content, and other context. We propose that positive/negative permissions and obligations be used to model conversation specifications and policies. We also propose the use of ontologies to categorize speech acts such that high level policies can be defined without going into specifics of the speech acts. This approach is independent of the syntax and semantics of the communication language and can be used for different agent communication languages. Our policy based framework can help in agent communication in three ways: (i) to filter inappropriate messages, (ii) to help an agent to decide which speech act to use next, and (iii) to prevent an agent from sending inappropriate messages. Our work differs from most existing research on communication policies because it is not tightly coupled to any domain information such as the mental states of agents or specific communicative acts. Contributions of this work include: (i) an extensible framework that is applicable to varied domain knowledge and different agent communication languages, and (ii) the declarative representation of conversation specifications and policies in terms of permitted and obligated speech acts.     

基于进化HMM 模型的动态Agent 系统建模方法

提出一种基于隐式Markov模型（HMM）的进化建模方法．使用进化算法随机搜索HMM的模型空间，自动选择HMM的结构和参数-完成对动态智能体系统行为的建模，学习智能体对周围环境的分割和反映方式．实验结果表明，该方法可以很好地搜索HMM的模型空间，并且避免了人工确定HMM模型结构的困难和手工设计模型所需的多次反复．     

供应链系统异构知识集成方法研究

供应链系统流动着大量的异构信息和知识．为了有效地利用现有知识对供应链进行优化管理和运作．供应链不仅要实现信息系统的集成而且需要实现知识集成。本文运用领域本体论(Ontology)、多Agent等技术研究供应链系统异构知识集成的方法．应用资源描述框架模式(RDFS)作为信息和知识的核心转换技术．实现异构知识的转换和集成,为供应链系统异构知识集成提供一个切实可行的技术解决方案。该方案可以保证全程信息数据和知识的正确性、及时性和完整性,进一步促使信息流为决策人员提供及时、可靠和科学的有效决策支持。     

Developing Real Applications With Agent Technologies

In this paper, we present an agent library to develop multi-agent applications. Such an agent library has been realised taking into account FIPA specifications. FIPA specifications give the minimum amount of technology deemed necessary for the management of agents in an open agent system, including agent roles, an agent communication language, an agent management content language, and a standard way to interact with non-agentised software.Therefore, the library offers the prototypes of the agents necessary for the management of a FIPA agent open system and an agent prototype that the user can specialise to build the other agents necessary for her/his application. The library has been implemented both in C++ and Java. The two implementations support a logical distribution, i.e., the agents correspond to different threads, and a physical distribution, i. e., agents of the same applications run on different machines. The Java library has been used to develop a multi-agent system, that facilitates users to use a video on demand service, and a first prototype of the system is under experimentation. The C++ library is used for the development of a multi-agent system that should integrate the different software modules performing sensory data interpretation, planning, faults diagnosis, . . . of a robot working in a space station.     

SAFE: secure agent roaming for e-commerce

The development of the Internet has made a powerful impact on the concept of commerce. E-commerce, a new way to conduct business, is gaining more and more popularity. Despite its rapid growth, there are limitations that hinder the expansion of e-commerce. The primary concern for most people when talking about online shopping is security. Due to the open nature of the Internet, personal financial details necessary for online shopping can be stolen if sufficient security mechanism is not put in place. How to provide the necessary assurance of security to consumers remains a question mark despite various past efforts. Another concern is the lack of intelligence. The Internet is an ocean of information depository. It is rich in content but lacks the necessary intelligent tools to help one locate the correct piece of information. Intelligent agent, a piece of software that can act on behalf of its owner intelligently, is designed to fill this gap. However, no matter how intelligent an agent is, if it remains on its owner's machine and does not have any roaming capability, its functionality is limited. With the roaming capability, more security concerns arise. In response to these concerns, SAFE, secure roaming agent for e-commerce, is designed to provide secure roaming capability to intelligent agents (Guan and Yang, 1999).     

基于模糊认知图的情感Agent模型研究

人工情感是人工心理的一个主要研究内容。从研究人工情感出发,提出一种基于模糊认知图的情感Agent建模的方法。模糊认知图模型通过在传统认知图模型中引入模糊测度来量化概念间因果关系的影响程度。Agent的知识由内部组元的状态以及组元之间的关系权值进行描述,用简单数值运算代替了复杂的符号逻辑来实现Agent的智能推理和决策。通过实验表明,该模型设计简单、易于扩展、适用性好。     

Using Decision Trees for Agent Modeling: Improving Prediction Performance

A modeling system may be required to predict an agent's future actions under constraints of inadequate or contradictory relevant historical evidence. This can result in low prediction accuracy, or otherwise, low prediction rates, leaving a set of cases for which no predictions are made. A previous study that explored techniques for improving prediction rates in the context of modeling students' subtraction skills using Feature Based Modeling showed a tradeoff between prediction rate and predication accuracy. This paper presents research that aims to improve prediction rates without affecting prediction accuracy. The FBM-C4.5 agent modeling system was used in this research. However, the techniques explored are applicable to any Feature Based Modeling system, and the most effective technique developed is applicable to most agent modeling systems. The default FBM-C4.5 system models agents' competencies with a set of decision trees, trained on all historical data. Each tree predicts one particular aspect of the agent's action. Predictions from multiple trees are compared for consensus. FBM-C4.5 makes no prediction when predictions from different trees contradict one another. This strategy trades off reduced prediction rates for increased accuracy. To make predictions in the absence of consensus, three techniques have been evaluated. They include using voting, using a tree quality measure and using a leaf quality measure. An alternative technique that merges multiple decision trees into a single tree provides an advantage of producing models that are more comprehensible. However, all of these techniques demonstrated the previous encountered trade-off between rate of prediction and accuracy of prediction, albeit less pronounced. It was hypothesized that models built on more current observations would outperform models built on earlier observations. Experimental results support this hypothesis. A Dual-model system, which takes this temporal factor into account, has been evaluated. This fifth approach achieved a significant improvement in prediction rate without significantly affecting prediction accuracy.     

Toward a new generic behavior model for human centered system simulation

The simulation of Human Centered Systems (HCS) has attracted increasingly attention in recent research. These systems involve individuals playing key roles, such as workers in manufacturing systems, soldiers in military operations, and investors in stock markets. The complexity simulating such systems is due to the need for modeling individual and group behavior and the integration of psychological and socio-technical aspects that can affect individual and HCS global performance. Although several models have been proposed to simulate such systems, most of them suffer from limitations pertaining to the integration of some factors, an inadequacy that will be discussed and elaborated on in this paper. The current study presents a new model for HCS simulation based on recent social and psychological theories. A model implementation example involving the simulation of a manufacturing system, considered as a HCS, is presented.     

公共agent请求代理体系结构CARBA开放性验证研究

1 引言八十年代中叶计算机技术界出现了“开放系统”这一名称。在随后的商战中,开放系统日显其威力。高举此旗的厂商日趋兴旺,而反其道者日益衰落。此时用户所面临的困惑问题是:何为开放系统?如何组建自己单位即将构造的开放系统?我们经过近十年的研究,成功地完成了开放系统描述的研     

多智能体遗传算法用于线性系统逼近

提出了一种新的参数优化方法--多智能体遗传算法,来求解线性系统逼近问题. 该方法中每个智能体代表一个候选解,即搜索空间中的一个实值向量.所有智能体生存在一 个网格状的环境中,且每个智能体占据一个格点不能移动.为了增加能量,它们将与其邻域 进行合作或竞争,也可以利用自身的知识.因此,设计了4个进化算子来模拟智能体间的竞 争、合作、自学习等行为.该方法利用这些智能体与智能体间的相互作用来达到优化逼近模 型中参数的目的;此外,还采用了一种动态扩展搜索空间的方法以解决算法所需的搜索空间 难以确定的问题.实验中,利用一个稳定和一个非稳定的线性系统逼近问题来验证算法的性 能,并与两种新近提出的方法作了比较.结果表明,该文方法优于其它方法,能够用较少的计 算量找到高质量的逼近模型,具有良好的性能和实际应用价值.     

Heuristic constraints enforcement for training of and knowledgeextraction from a fuzzy/neural architecture. I. Foundation

Using fuzzy/neural architectures to extract heuristic information from systems has received increasing attention. A number of fuzzy/neural architectures and knowledge extraction methods have been proposed. Knowledge extraction from systems where the existing knowledge limited is a difficult task. One of the reasons is that there is no ideal rulebase, which can be used to validate the extracted rules. In most of the cases, using output error measures to validate extracted rules is not sufficient as extracted knowledge may not make heuristic sense, even if the output error may meet the specified criteria. The paper proposes a novel method for enforcing heuristic constraints on membership functions for rule extraction from a fuzzy/neural architecture. The proposed method not only ensures that the final membership functions conform to a priori heuristic knowledge, but also reduces the domain of search of the training and improves convergence speed. Although the method is described on a specific fuzzy/neural architecture, it is applicable to other realizations, including adaptive or static fuzzy inference systems. The foundations of the proposed method are given in Part I. The techniques for implementation and integration into the training are given in Part II, together with applications     

支持细粒度协作的CAD系统在线集成研究

研究了一种支持细粒度协作的遗产CAD系统的在线集成方法,提出通过中间件agent来封装单机CAD系统应用.构造了中间件agent的外封装器和内封装器结构,并描述其工作过程.设计了细粒度协作的通讯机制,并采用基于标准宏参命令的通讯协议来支持遗产CAD系统的集成.初步实现了一个集成两种遗产CAD应用的原型系统.     

基于协同多Agent的智能化产生式CAPP系统框架

该文提出的协同多Agent产生式系统,将一个复杂的工艺设计问题分解成几个简单的问题,分别采用Agent进行解决,各个Agent具有独自的知识和推理结构,在协同的机理下组合成一个有机的系统。采用多Agent协同结构的智能化产生式CAPP系统能够有效地解决产生式系统的灵活性、智能化、协同化和虚拟化,并能够不断地产生新的知识和方法,并加以保存。该文提出的采用多Agent协同结构的智能化产生式CAPP系统框架及其运行机制,能够在实践中发挥较大的作用,有助于产生新的产生式CAPP系统。