面向网构软件的构件自适应机制研究*

结合软件agent技术,从构件角度出发,通过对传统构件进行包装,加入行为规则驱动和行为推导两大机制,提出了一种能够在运行时实时适应用户需求变化和运行环境变化的自适应构件。在自适应构件的运行和支撑方面,实现了一个可动态定制自适应构件行为规则的工具,改进了现有的构件支撑平台使得能满足自适应构件的运行需求;最后通过一个例子说明了自适应构件的设计、实现、运行和演化等一系列工作流程。初步实践表明,上述机制和整套框架为开发具有自适应性的网构软件提供了有效支持。     

一种基于自主构件的网构软件协作框架

网构软件的协作具有真分布、无全局规划、非强制性等新的特征,这对协作提出了新的要求.在传统构件的基础上,借鉴Agent领域中对自主性的理解,使用自主构件这一概念来对具有自主性的计算资源进行建模,并描述了一种基于自主构件的协作框架.该框架中借助环境改变来引导自主构件间的协作行为,协作过程中没有直接干预其他自主构件的行为,更没有强迫其他自主构件参与,但自主构件却能采取所期望的行动最终实现协作目标.     

基于适应性构件模型的软件产品线设计和实现(英文)

在当前面向特征的软件产品线开发方法中,需求级的可变性分析、可变点表示以及面向应用的定制已经得到了较好的支持。但是,从需求级的定制和裁剪( 特征模型) 到实现级( 体系结构和构件) 的映射仍然存在许多困难。针对这一问题,文章提出了一种基于适应性构件模型的软件产品线开发方法。这种适应性构件模型引入基于特征的领域模型作为构件端口( 包括内部端口和外部端口) 的语义基础。另一方面, 适应性构件模型所具有的微体系结构使得面向特定应用的构件行为定制成为可能。为了实现构件级面向特征的定制, 构件内部负责内部和外部协作的控制中心与构件的计算逻辑被分离开来执行经定制后的构件行为协议和端口语义。构件协作和计算功能的分离使针对构件行为的面向应用的定制更加便利。这样,产品线应用开发中需求级的特征定制就可以映射为体系结构和构件级的结构和行为调整。     

自主构件自适应策略的在线定制及动态评估

开放分布式环境下自适应软件的研究已引起学术界、工业界的广泛关注.但分布在网络上的软件实体是由不同的组织独立开发并部署的,它们代表各自的组织(或所有者)自主地采取行动,在构造分布式环境下的自适应系统时,不能再将构成单元视为被动的受管对象,而应将其建模为具有主动行为能力的计算实体,并在这一层面设计和封装系统的自适应逻辑.然而,在现阶段对于自主计算实体的研究中,大多缺乏对于自适应策略的动态加载和动态演化的支持.提出了一种支持策略动态加载的自主构件模型,使得自主构件能够在运行时习得新的自适应策略和行为,实现了一种基于质量运行时动态评估的自主构件的自适应机制,使得自主构件能够自行评估自适应策略的优劣并选择最佳的策略加以适应,在保证自身目标得以实现的同时,提高了服务质量.另外,还详细描述了自主构件的实现方案及其运行支撑,通过实验展示了自主构件基于质量动态评估的自适应过程以及自适应策略的动态加载过程.     

构件交互行为监测需求管理机制的研究

在开放网络环境中,鉴于分布式软件规模庞大且交互行为错综复杂,为有效控制交互行为监测的粒度、维度和强度,给出了监测需求管理机制,设计了构件反射器,能支持动态配置、自主调整和自动部署监测需求,提高了监测的可控性、自主性和可用性;在此基础上实现了该监测需求管理机制并应用于分布式电子商务网站中,结果表明该机制是有效的。     

一种基于构件演算的主动构件精化方法

现代构件系统通常包含多个并发执行的主动构件,这使得验证构件系统的正确性变得十分困难.通过对构件演算进行扩展,提出了一种主动构件的精化方法.在构件接口层引入契约.契约使用卫式设计描述公共方法和主动活动的功能规约.通过一对发散、失败集合定义契约的动态行为,并利用发散、失败集合之间的包含关系定义契约间的精化关系.证明了应用仿真技术确认契约精化关系的定理.定义构件的语义为其需求接口契约到其服务接口契约的函数,以此为基础,可以通过契约的精化来证明构件的精化.给出了构件的组装规则.在构件系统自底向上的构造过程中,应用构件的精化方法和组装规则可以保证最终系统的正确性.     

构件演化中的系统行为一致性的研究

构件技术的发展,减少了开发应用程序的时间和复杂度,同时也为软件提供了更好的动态演化能力.基于构件的软件系统是通过在构件间建立交互关系,将多个构件组织成一个统一的整体得到的.因此在构件演化时,例如时构件功能实现进行改变,可能导致系统运行偏离原来的系统行为.我们的工作就是在构件演化时对系统行为进行一致性检查,保证构件的功能实现的变化不会使系统行为偏离原来的系统.在本文中首先通过Petri-net的形式化方法,对系统实现中包括的构件的功能实现和构件间的交互进行建模,并通过以上信息推导得到系统行为.在此基础之上,根据基于行为继承理论的行为一致性规则的要求对定义的系统功能行为进行验证,以保证构件演化时系统行为的一改变符合行为的一致性要求,同时又保证了构件演化的灵活性.     

一种支持软件可信演化的构件模型

对环境的适应是软件保证其可信的重要手段.当应用场景超出开发阶段的预设时,软件的环境适应能力需要能够在线调整,以保证其行为和结果仍可符合用户预期.这一调整的前提是软件工程层面的高效支持机制.基于关注点分离原则和动态软件体系结构技术,提出了一种支持软件环境适应能力细粒度在线调整的构件模型ACOE(adaptive component model for open environment).ACOE将软件环境适应能力中的感知、决策、执行等关注点封装为独立的构件和连接子,通过动态软件体系结构技术来支持它们的在线重配置,从而使第三方可在必要时通过有选择性的更新来调整适应能力.实现了支持ACOE构件模型的容器原型,并通过实验验证了其有效性.     

构件化领域框架设计与实现

文中介绍了由东北大学软件中心研究的构件化领域框架系统ＮＥＳＣ。它以构件技术、软件体系结构技术和应用软件开发技术为基础,采用了界面、规则和存储的层次化结构,实现了规则控制、时空关系描述、可视化信息表达、构件静态组合、功能动态组合、分布式构件管理等功能。框架体系结构设计通过应用－技术二维划分、功能提取、局部封闭和全局分层等技术实现,可望达到合理规划设计构件,简化依赖关系,提高运动效率,增强构件通用性以     

软件构件技术在农业专家系统开发平台中的研究与应用*

分析了构件间的集成规则,并研究了知识表示构件和推理机构件。在公共的构件底层框架的基础上,研究了具有可自定制功能的农业专家系统平台的组装流程。     

A method for protocol‐based collision avoidance between autonomous marine surface craft

This paper is concerned with the in‐field autonomous operation of unmanned marine vehicles in accordance with convention for safe and proper collision avoidance as prescribed by the Coast Guard Collision Regulations (COLREGS). These rules are written to train and guide safe human operation of marine vehicles and are heavily dependent on human common sense in determining rule applicability as well as rule execution, especially when multiple rules apply simultaneously. To capture, the flexibility exploited by humans, this work applies a novel method of multiobjective optimization, interval programming, in a behavior‐based control framework for representing the navigation rules, as well as task behaviors, in a way that achieves simultaneous optimal satisfaction. We present experimental validation of this approach using multiple autonomous surface craft. This work represents the first in‐field demonstration of multiobjective optimization applied to autonomous COLREGS‐based marine vehicle navigation. © 2006 Wiley Periodicals, Inc.     

Crowds by Example

We present an example-based crowd simulation technique. Most crowd simulation techniques assume that the behavior exhibited by each person in the crowd can be defined by a restricted set of rules. This assumption limits the behavioral complexity of the simulated agents. By learning from real-world examples, our autonomous agents display complex natural behaviors that are often missing in crowd simulations. Examples are created from tracked video segments of real pedestrian crowds. During a simulation, autonomous agents search for examples that closely match the situation that they are facing. Trajectories taken by real people in similar situations, are copied to the simulated agents, resulting in seemingly natural behaviors.     

Self-organizing network services with evolutionary adaptation

This paper proposes a novel framework for developing adaptive and scalable network services. In the proposed framework, a network service is implemented as a group of autonomous agents that interact in the network environment. Agents in the proposed framework are autonomous and capable of simple behaviors (e.g., replication, migration, and death). In this paper, an evolutionary adaptation mechanism is designed using genetic algorithms (GAs) for agents to evolve their behaviors and improve their fitness values (e.g., response time to a service request) to the environment. The proposed framework is evaluated through simulations, and the simulation results demonstrate the ability of autonomous agents to adapt to the network environment. The proposed framework may be suitable for disseminating network services in dynamic and large-scale networks where a large number of data and services need to be replicated, moved, and deleted in a decentralized manner.     

Real-time behavior-based robot control

Behavior-based systems form the basis of autonomous control for many robots, but there is a need to ensure these systems respond in a timely manner. Unexpected latency can adversely affect the quality of an autonomous system’s operations, which in turn can affect lives and property in the real-world. A robots ability to detect and handle external events is paramount to providing safe and dependable operation. This paper presents a concurrent version of a behavior-based system called the Real-Time Unified Behavior Framework, which establishes a responsive basis of behavior-based control that does not bind the system developer to any single behavior hierarchy. The concurrent design of the framework is based on modern software engineering principles and only specifies a functional interface for components, leaving the implementation details to the developers. In addition, the individual behaviors are executed by a real-time scheduler, guaranteeing the responsiveness of routines that are critical to the autonomous system’s safe operation. Experimental results demonstrate the ability of this approach to provide predictable temporal operation, independent of fluctuations in high-level computational loads.     

Development of A Behavior‐Based Cooperative Search Strategy for Distributed Autonomous Mobile Robots Using Zigbee Wireless Sensor Network

To achieve efficient and objective search tasks in an unknown environment, a cooperative search strategy for distributed autonomous mobile robots is developed using a behavior‐based control framework with individual and group behaviors. The sensing information of each mobile robot activates the individual behaviors to facilitate autonomous search tasks to avoid obstacles. An 802.15.4 ZigBee wireless sensor network then activates the group behaviors that enable cooperative search among the mobile robots. An unknown environment is dynamically divided into several sub‐areas according to the locations and sensing data of the autonomous mobile robots. The group behaviors then enable the distributed autonomous mobile robots to scatter and move in the search environment. The developed cooperative search strategy successfully reduces the search time within the test environments by 22.67% (simulation results) and 31.15% (experimental results).     

Unified Behavior Framework for Reactive Robot Control

Behavior-based systems form the basis of autonomous control for many robots. In this article, we demonstrate that a single software framework can be used to represent many existing behavior based approaches. The unified behavior framework presented, incorporates the critical ideas and concepts of the existing reactive controllers. Additionally, the modular design of the behavior framework: (1) simplifies development and testing; (2) promotes the reuse of code; (3) supports designs that scale easily into large hierarchies while restricting code complexity; and (4) allows the behavior based system developer the freedom to use the behavior system they feel will function the best. When a hybrid or three layer control architecture includes the unified behavior framework, a common interface is shared by all behaviors, leaving the higher order planning and sequencing elements free to interchange behaviors during execution to achieve high level goals and plans. The framework’s ability to compose structures from independent elements encourages experimentation and reuse while isolating the scope of troubleshooting to the behavior composition. The ability to use elemental components to build and evaluate behavior structures is demonstrated using the Robocode simulation environment. Additionally, the ability of a reactive controller to change its active behavior during execution is shown in a goal seeking robot implementation.     

A knowledge model for unifying deductive and non-deductiveheterogeneous databases

A new, much-needed knowledge model for integrating heterogeneous knowledge represented by independently developed deductive rules and diverse data models has been developed and is presented. It has the strength and expressiveness necessary to unify seemingly disparate, autonomous components. Separate components, including rules, are modeled as functional units. A complete set of relationships, or links, between units based on domain interdependencies is defined, along with a set of integration operators. This approach resolves the unique and complex forms of conflict that arise from integrating deductive rules from autonomous sources, such as conflicts in meaning, redundancies and subsumption. This model has significant application to the creation of a common representation for future shared environments     

A framework for defining and learning fuzzy behaviors for autonomous mobile robots

In this paper we show our work on the use of fuzzy behaviors in the field of autonomous mobile robots. We address here how we use learning techniques to efficiently coordinate the conflicts between the different behaviors that compete with each other to take control of the robot. We use fuzzy rules to perform such fusion. These rules can be set using expert knowledge, but as this can be a complex task, we show how to automatically define them using genetic algorithms. We also describe the working environment, which includes a custom programming language (named BG) based on the multi‐agent paradigm. Finally, some results related to simple goods‐delivery tasks in an unknown environment are presented. © 2002 John Wiley & Sons, Inc.     

Designing Stable Finite State Machine Behaviors Using Phase Plane Analysis and Variable Structure Control

This paper discusses how phase plane analysis can be used to describe the overall behavior of single and multiple autonomous robotic vehicles with finite state machine rules. The importance of this result is that we can begin to design provably stable group behaviors from a set of simple control laws and appropriate switching points with decentralized variable structure control. The ability to prove stable group behavior is especially important for applications such as locating military targets or land mines. In this paper, we demonstrate how phase plane analysis has been used to explain the behavior of a 16 cm³ autonomous line-tracking robot with four finite states. After which, the analysis is extended to include the design of a decentralized variable structure controller that guides multiple vehicles to a goal while avoiding each other.     

一种优化相关规划的发现方法

提出了一种基于相关性的框架.在此框架内,使用残差分析来判断两项之间是否是独立的.残差分析可以使得我们很容易地获得包含负蕴含规则在内的真正的关联（而不是并发）规则,而且不需要指定支持度和可信度阈值.为了提高发现规则的质量,文中使用遗传算法来发现优化规则.在人工数据集和真实数据集上的实验结果表明文中的算法在发现规则的有趣性上优于类Apriori算法.