An Architectural Modelfor Intelligent Network Management

1 IntroductionThe explosion in size and complexity of today's computer network means that NetworkManagement (NM) is a critical factor. The ever growing demand for distributed applicationscoupled with increasing corporate globalization often leads to users requiring constant network availability as the network becomes the primary strategic resource. The currellt NMbecomes increasingly inadequate, since the networks to be managed are heterogeneous anddynamic, and manapemellt functions, such as…     

基于图形理解的建筑结构三维重建技术

在实际应用中,现有的三维重建算法无法有效地应用到建筑领域.以建筑行业的基本制图规范为指导,结合图形识别理解和人工智能技术,提出了一种基于图形理解的建筑结构三视图自动识别与重建方法.该方法以自动识别建筑构件的轮廓为基础,通过对符号、注释等语义信息的理解与综合以及对相邻实体间拓扑关系的分析,快捷、有效地完成对建筑物的整体重建.实验结果表明,在少量人机交互的基础上,该方法对实际的建筑图是有效的.     

Emotions in autonomous agents: comparative analysis of mechanisms and functions

Emotion mechanisms are often used in artificial agents as a method of improving action selection. Comparisons between agents are difficult due to a lack of unity between the theories of emotion, tasks of agents and types of action selection utilised. A set of architectural qualities is proposed as a basis for making comparisons between agents. An analysis of existing agent architectures that include an emotion mechanism can help to triangulate design possibilities within the space outlined by these qualities. With this in mind, twelve autonomous agents incorporating an emotion mechanism into action selection are selected for analysis. Each agent is dissected using these architectural qualities (the agent architecture, the action selection mechanism, the emotion mechanism and emotion state representation, along with the emotion model it is based on). This helps to place the agents within an architectural space, highlights contrasting methods of implementing similar theoretical components, and suggests which architectural aspects are important to performance of tasks. An initial framework is introduced, consisting of a series of recommendations for designing emotion mechanisms within artificial agents, based on correlations between emotion roles performed and the aspects of emotion mechanisms used to perform those roles. The conclusion discusses how problems with this type of research can be resolved and to what extent development of a framework can aid future research.     

An empirical study on students’ ability to comprehend design patterns

Design patterns have become a widely acknowledged software engineering practice and therefore have been incorporated in the curricula of most computer science departments. This paper presents an observational study on students’ ability to understand and apply design patterns. Within the context of a postgraduate software engineering course, students had to deliver two versions of a software system; one without and one with design patterns. The former served as a poorly designed system suffering from architectural problems, while the latter served as an improved system where design problems had been solved by appropriate patterns. The experiment allowed the quantitative evaluation of students’ preference to patterns. Moreover, it was possible to assess students’ ability in relating design problems with patterns and interpreting the impact of patterns on software metrics. The overall goal was to empirically identify ways in which a course on design patterns could be improved.     

An architecture for dynamic and evolving cooperative software agents

This paper presents an architectural model to construct cooperative and evolutionary agent-based software systems. Here, a software system is defined as one consisting of a set of agents which interact by executing actions and by determining system functionality. Because the agents need intercommunication and coordination, we propose a model that allows this communication between agents and preserves system activity. This coordination model uses a blackboard as the means of communication between the system agents. The system architecture presented is dynamic in the sense that the modelled system can evolve and be adapted to its new functions. To do this, we distinguish two levels in the architecture: the system and the meta-system levels. The system level describes the system's structure and its functionality, while the meta-system level allows us to effect changes in the system in such a way as to preserve the system's integrity and restrictions. Modifications at the system level are carried out when the developer interacts at the meta-system level. System users interact only at the system level.     

Playing Detective: Reconstructing Software Architecture from Available Evidence

Because a system's software architecture strongly influences its quality attributes such as modifiability, performance, and security, it is important to analyze and reason about that architecture. However, architectural documentation frequently does not exist, and when it does, it is often out of sync with the implemented system. In addition, it is rare that software development begins with a clean slate; systems are almost always constrained by existing legacy code. As a consequence, we need to be able to extract information from existing system implementations and utilize this information for architectural reasoning. This paper presents Dali, an open, lightweight workbench that aids an analyst in extracting, manipulating, and interpreting architectural information. By assisting in the reconstruction of architectures from extracted information, Dali helps an analyst redocument architectures, discover the relationship between as-implemented and as-designed architectures, analyze architectural quality attributes and plan for architectural change.     

基于多agent的分布式储层地质统计分析系统的研究*

对储层建模方法进行了研究,针对储层建模相关软件的复杂度大、主观性强、处理速度慢等普遍问题,提出了基于多agent的分布式储层地质统计分析系统.对系统进行了多agent规划,设计了分层的系统模型,并阐述了各个agent的功能,最后对该模型进行了面向agent的设计.     

建筑工程图的解释识别方法

建筑工程图的自动识别技术对于提高建筑业自动化、信息化程度十分重要。提出了一种解释识别方法，将识别过程需利用的对象层次结构、对象间关系、识别顺序等按照一定的格式加以描述，使得识别过程建立在对这种描述自动解释的基础上，较大地提高了自动识别系统的规范性、可维护性、可扩充性和稳定性。     

Purpose-Based Expert Finding in a Portfolio Management System

Most of the research in the area of expert finding focuses on creating and maintaining centralized directories of experts' profiles, which users can search on demand. However, in a distributed multiagent-based software environment, the autonomous agents are free to develop expert models or model fragments for their own purposes and from their viewpoints. Therefore, the focus of expert finding is shifting from the collection at one place as much data about a expert as possible to accessing on demand from various agents whatever user information is available at the moment and interpreting it for a particular purpose. This paper outlines purpose-based expert modeling as an approach for finding an expert in a multiagent portfolio management system in which autonomous agents develop expert agent models independently and do not adhere to a common representation scheme. This approach aims to develop taxonomy of purposes that define a variety of context-dependent user modeling processes, which are used by the users' personal agents to find appropriate expert agents to advise users on investing strategies.