智能规划识别及其应用的研究

智能规划识别已经被广泛地用于自然语言理解、知识推理、情景演算等计算机系统的许多方面。文章介绍了国内外学者的相关研究,包括规划识别研究的历史与现状、问题与展望。并介绍了规划识别的各种方法及规划识别的应用。     

基于规划识别的多智能体协作研究及应用

多智能体系统是规划识别的一个有效应用平台，提出一种基于规划识别多智能体协作算法，对对抗环境和非对抗环境中的基于规划识别的多智能体协作算法进行了分析，实现了对队友和对手行为目的的认识和建模，减少了协作主体间需要通信的时间厦难度。该协作算法应用到多智能体的有效测试平台机器人足球赛中，试验结果证明，该算法在通信受限、信息受限或信息延时的系统中可有效预测队友和对手的行为，从而实现智能体间的协作。     

基于规划识别的入侵检测研究

规划识别是人工智能的重要研究分支之一,在入侵检测领域中已有初步的应用。本文在介绍规划识别和入侵检测基本概念的基础上,按照规划识别方法分门别类地研究了基于事件层的规划识别、基于贝叶斯网络的规划识别、基于扩展目标规划图的规划识别、彩色Petri网、对手规划、行为状态图等在入侵检测领域的应用现状和进展;接着深入分析了规划识别和入侵检测的关系和相似之处;最后讨论了基于规划识别的入侵检测存在的问题,并指出了未来的发展趋势。本文综述了智能规划在入侵检测中应用的关键技术和存在的问题,研究内容对于相关人员从事入侵检测研究具有重要的参考价值。     

基于流量智能识别网络安全风险的技术研究

任何形式的信息传输都会面临信息丢失、攻击和窃取等风险,为此针对现阶段网络加密流量进行检测,以此实现对目标网络安全态势的感知。随着人们对网络信息安全防范意识的不断增强,HTTPS和VPN等加密形式开始逐渐应用到各种网络当中,这在一定程度上会破坏明文数据的数据格式和统计特点,导致一些恶意攻击流量可以通过防火墙的隔离危害用户网络。为此基于加密技术的加密协议随机性与网络上下报文等,研究并设计加密流量智能识别网络体系框架,提高对加密流量数据的深度检测能力,以期为加密流量的检测工作提供帮助。     

规划识别的研究及其应用

规划识别是人工智能研究领域的一个重要分支.由于近年来的广泛应用,规划识别的重要性被越来越多的学者所认同.对规划识别领域的大量文献进行广泛而深入研究,从整体上阐述了规划识别问题,较为全面地介绍了规划识别的发展历程、分类、方法以及应用,并着重介绍了规划识别目前较为流行的技术方法和热门应用,公开了几个未解决的问题.     

基于事件驱动与分级多层黑板模型的态势评估方法

通过分析态势评估的过程,指出态势评估的本质是根据已发生事件,预测未来发生事件的趋势。本文分析了态势评估中已有黑板模型的不足,以及黑板模型的优缺点,提出基于事件驱动与分级多层黑板模型的态势评估方法,克服了因黑板边界划分带来的黑板推理的不准确性,使得态势评估更加准确与直观。     

基于特定入侵检测问题的规划识别模型的研究

该文在特定的入侵检测问题中,对于规划识别技术进行了初步研究,提出了基于特定入侵检测问题的规划识别模型。     

基于信息融合原理的智能故障诊断模型

针对传统设备故障诊断方法中存在的局限性,文章在对设备智能故障原理和方法初步探索和研究的基础上,提出了基于信息融合原理的智能故障诊断方法。该方法利用多源异质传感器采集设备的各种特征信息,并采用模糊神经网络融合诊断中心作为诊断判决的执行机构,从而实现准确诊断设备故障诊断原因以及对设备运行工况进行态势评估的过程。并且通过对单传感器与多传感器信息融合诊断结果的对比表明多传感器信息融合诊断比单个传感器具有更高的准确性。     

多Agent规划识别跟踪模型在态势估计中的应用

1 引言美国联合领导实验室数据融合委员会JDL/DTS定义态势评估为:综合固定和运动物体的配置、环境数据、理论数据和所谓性能数据(如敌方车辆运输能力、传感器观测性能等)以估计和推测敌方作战计划的一种方法,即确定敌人要做什么(行动)、企图达到什么目的(目标)等,从而确定己方的兵力部署。规划识别是根据Agent的行为序列来推断Agent所追求目标的过程,着重于对当前已发生行为的分析和抽象,因而对动态问题有很好的适应性,与态势估计中通过观察、分析战场中军事单元的动态行为来识别其计划的要求相一致,因此,本文在分析态势估计问题本质特征的基础上,采用多Agent规划识别理论来解决态势估计问题。     

基于改进GLR算法的智能识别英语翻译模型设计

GLR算法模型翻译识别结果存在数据点重合的情况,精确度无法得到有效保障。为了准确的识别短语,设计了基于改进GLR算法的短语智能识别算法,该算法构建标记规模约74万个英汉单词的短语语料库,使短语具备可搜索功能,通过短语中心点构建短语结构,可获得词性识别结果,依据解析线性表的句法功能校正词性识别结果中的英汉结构歧义,最终获得识别的内容。实际测评结果显示,该算法克服了GLR的弊端,相对统计算法和动态记忆算法提高了运算速度和处理性能,更加适合机器翻译任务,为在智能机器翻译领域提供了新的思路。     

Active acquisition of user models: Implications for decision-theoretic dialog planning and plan recognition

This article investigates the implications ofactive user model acquisition upon plan recognition, domain planning, and dialog planning in dialog architectures. A dialog system performs active user model acquisition by querying the user during the course of the dialog. Existing systems employ passive strategies that rely on inferences drawn from passive observation of the dialog. Though passive acquisition generally reduces unnecessary dialog, in some cases the system can effectively shorten the overall dialog length by selectively initiating subdialogs for acquiring information about the user.We propose a theory identifying conditions under which the dialog system should adoptactive acquisition goals. Active acquisition imposes a set ofrationality requirements not met by current dialog architectures. To ensure rational dialog decisions, we propose significant extensions to plan recognition, domain planning, and dialog planning models, incorporating decision-theoretic heuristics for expected utility. The most appropriate framework for active acquisition is a multi-attribute utility model wherein plans are compared along multiple dimensions of utility. We suggest a general architectural scheme, and present an example from a preliminary implementation.The author will be at the Department of Computer Science, University of Toronto, untilThe author will be at the Department of Computer Science, University of Toronto, untilThe author will be at the Department of Computer Science, University of Toronto, untilThe author will be at the Department of Computer Science, University of Toronto, untilThe author will be at the Department of Computer Science, University of Toronto, untilThe author will be at the Department of Computer Science, University of Toronto, until     

On the interaction between plan recognition and intelligent interfaces

Plan recognition is an active research area in automatic reasoning, as well as a promising approach to engineering interfaces that can exploit models of user's plans and goals. Much research in the field has focused on the development of plan recognition algorithms to support particular user/system interactions, such as found in naturally occurring dialogues. However, two questions have typically remained unexamined: 1) exactly what kind of interface tasks can knowledge of a user's plans be used to support across communication modalities, and 2) how can such tasks in turn constrain development of plan recognition algorithms? In this paper we present a concrete exploration of these issues. In particular, we provide an assessment of plan recognition, with respect to the use of plan recognition in enhancing user interfaces. We clarify how use of a user model containing plans makes interfaces more intelligent and interactive (by providing an intelligent assistant that supports such tasks as advice generation, task completion, context-sensitive responses, error detection and recovery). We then show how interface tasks in turn provide constraints that must be satisfied in order for any plan recognizer to construct and represent a plan in ways that efficiently support these tasks. Finally, we survey how interfaces are fundamentally limited by current plan recognition approaches, and use these limitations to identify and motivate current research. Our research is developed in the context of CHECS, a plan-based design interface.     

基于非线性工艺规划思想的车间动态调度系统

描述了一种基于非线性工艺规划思想的车间动态调度系统的集成框架结构,其核心模块为计划调度模块,该模块调用由遗传算法和启发式调度相结合的调度算法生成动态调度方案。其中所提出的遗传编码的设计充分考虑工艺路径的柔性,并根据此编码提出调度方案的构造方法,同时相应地改进了遗传操作,从而实现了调度的全局最优性和可行性。     

指针式仪表读数的机器视觉智能识别方法

为快速准确地自动识别指针式仪表读数,采用机器视觉技术,结合减影法和Hough变换法对仪表读数进行智能识别.对指针式仪表图像进行图像二值化、形态学处理和边缘检测预处理;利用Hough变换检测仪表中的指针,计算得到指针方向和定位圆形,实现指针式仪表的智能识别.实验结果表明:读数识别的平均相对误差为0.91%,精度较高,能较好地识别指针式仪表读数.     

基于CPN的规划识别及多步骤攻击检测方法

在Kautz规划识别算法基础上,利用CPN作为新的规划表示和识别方法.与目前规划识别领域广泛使用的Kautz表示方法相比,新的表示方法更加简便与高效.以多步骤攻击检测作为实例,通过计算行为间的变迁关系,以重新得到攻击全貌.     

A dempster-shafer approach to modeling agent preferences for plan recognition

Plan recognition is an important task whenever a system has to take into account an agent's actions and goals in order to be able to react adequately. Most plan recognizers work by merely maintaining a set of equally plausible plan hypotheses each of which proved compatible with recent observations without taking into account individual preferences of the currently observed agent. Such additional information provides a basis for ranking the hypotheses so that the best one can be selected whenever the system is forced to react (e.g., to provide help to the user of a software system to accomplish his goals). Furthermore, hypotheses with low valuations can be excluded from considerations at an early stage. In this paper, an approach to the quantitative modeling of the required agent-related data and their use in plan recognition is presented. It relies on the DempsterShafer Theory and provides mechanisms for the initialization and update of corresponding numerical values.     

基于贝叶斯网络的脱机手写体汉字智能识别

针对汉字识别的超多类问题,将贝叶斯网络分类器引入小样本字符集脱机手写体汉字识别中．对手写大写数字汉字的小样本字符集构造识别系统,同时与传统的欧氏距离方法进行比较,实验表明该算法将识别率提高到92．4％,在小样本字符集脱机手写体识别中具有较强的实用性和良好的扩展性．     

Controlling inference in plan recognition

An algorithm based on an assessment of the completeness of an explanation can be used to control inference in a plan recognition system: If the explanation is complete, inference is stopped. If the explanation is incomplete, inference is continued. If it cannot be determined whether the explanation is complete, then the system weighs the strength of its interest in continuing the analysis against the estimated cost of doing so. This algorithm places existing heuristic approaches to the control of inference in plan recognition into a unified framework. The algorithm rests on the principle that the decision to continue processing should be based primarily on the explanation chain itself, not on external factors. Only when an analysis of the explanation chain proves inconclusive should outside factors weigh heavily in the decision. Furthermore, a decision to discontinue chaining should never be final; other components of the system should have the opportunity to request that an explanation chain be extended. An implementation of the algorithm, called PAGAN, demonstrates the usefulness of this approach.Dr. James Mayfield is Assistant Professor of Computer Science at the University of Maryland at Baltimore County. He completed his Ph.D. in 1989 at the University of California at Berkeley, under the direction of Dr. Robert Wilensky. His paper reflects his ongoing interest in plan recognition. Dr. Mayfield's other research interests include the detection and resolution of ambiguity, and the automatic conversion of linear text to hypertext.     

A probabilistic plan recognition algorithm based on plan tree grammars

We present the PHATT algorithm for plan recognition. Unlike previous approaches to plan recognition, PHATT is based on a model of plan execution. We show that this clarifies several difficult issues in plan recognition including the execution of multiple interleaved root goals, partially ordered plans, and failing to observe actions. We present the PHATT algorithm's theoretical basis, and an implementation based on tree structures. We also investigate the algorithm's complexity, both analytically and empirically. Finally, we present PHATT's integrated constraint reasoning for parametrized actions and temporal constraints.     

Intelligent setup planning in manufacturing by neural networks based approach

Setup planning is considered the most significant but also difficult activity in Computer Aided Process Planning (CAPP), and has a strong impact on manufacturability, product quality and production cost. Indeed, setup planning activity deserves much attention in CAPP. The setup planning in manufacturing consists mainly of three steps, namely, setup generation, operation sequence, and setup sequence. In this paper, the Kohonen self-organizing neural networks and Hopfield networks are adopted to solve such problems in setup planning efficiently. Kohonen self-organizing neural networks are utilized, according to the nature of the different steps in setup planning, to generate setups in terms of the constraints of fixtures/jigs, approach directions, feature precedence relationships, and tolerance relationships. The operation sequence problem and the setup sequence problem are mapped onto the traveling salesman problem, and are solved by Hopfield neural networks. This paper actually provides a complete research basis to solve the setup planning problem in CAPP, and also develops the most efficient neural networks based approaches to solve the setup planning problem in manufacturing. Indeed, the results of the proposed approaches work towards the optimal solution to the intelligent setup planning in manufacturing.