• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

J4 ›› 2010, Vol. 32 ›› Issue (6): 1-8.doi: 10.3969/j.issn.1007130X.2010.

• 论文 •    下一篇

Agent、目标与情景结合的需求方法

刘璘1,毛新军2   

  1. (1.清华大学软件学院,北京 100084;2.国防科学技术大学计算机学院,湖南 长沙 410073)
  • 收稿日期:2008-04-13 修回日期:2008-07-10 出版日期:2010-06-01 发布日期:2010-06-01
  • 通讯作者: 刘璘 E-mail:linliu@tsinghua.edu.cn
  • 作者简介:刘璘(1973),女,辽宁辽阳人,博士,副教授,研究方向为需求工程与知识管理;毛新军,教授,研究方向为人工智能与Agent技术。
  • 基金资助:

    国家973计划资助项目(2009CB320706);国家自然科学基金资助项目(60873064,90818026)

Agent,Goal and Scenario Integrated Requirement Analysis Methodology

LIU Lin1,MAO Xinjun2   

  1. (1.School of Software,Tsinghua University,Beijing 100084;
    2.School of Computer Science,National University of Defense Technology,Changsha 410073,China)
  • Received:2008-04-13 Revised:2008-07-10 Online:2010-06-01 Published:2010-06-01

摘要:

本文以面向Agent的软件工程技术研究为核心,提出了一种Agent、目标与情景结合的需求分析方法。该方法以用类自然语言SSDL撰写的一组情景实例为输入,其中每个情景实例均包含参与交互的外部参与者和系统内部提供相应服务的Agent的信息,表明实例要达成的业务目标。根据这些情景实例进行文法的归纳学习,学习的结果是系统的形式需求规约—一种带属性的上下文无关文法,称为系统情景文法。最后,将系统情景文法转化为用AgentZ语言描述的Agent系统需求模型。

关键词: 软件Agent, 情景实例, 形式规约, 文法归纳学习

Abstract:

This paper sets out from the agentoriented software engineering paradigm, proposes an requirement analysis methodology integrating with agent, goal and scenario technology. In the proposed approach, system inputs are textual descriptions of the interactions between various agents within the system and the environment, annotated with informations on intended purpose of the scenarios. At the beginning stage of the proposed AOA method, original scenarios in SSDL are described by endusers. These scenarios are then transformed into an internal representation  ScenarioTree. Then an inductive learning procedure will be started, during which the scenario descriptions are decomposed, clustered, and generalised. The learning result is an abstract grammar  an attribute grammar. The attributes and attribute computing rules are used to reinforce the expressiveness of the grammar.

Key words: oftware agents;scenario;formal specification;inductive grammar learning

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