战术计划识别模型的分析、描述与设计

分析计划识别问题的共性特点,根据军事领域的需求分析,建立战术计划识别模型的逻辑描述与形式化描述,设计实现一个以“匹配、生成假设、假设排序、动态跟踪预测”为主线的战术计划识别模型,并给出具体的应用实例,数据结果表明该模型比较能够反映实际中战术意图识别的特点,并在知识获取与推理效率等瓶颈问题上有了较大的改进。     

基于反绎学习的裁判文书量刑情节识别

针对司法领域标记数据匮乏、标注质量不高、存在强逻辑性导致裁判文书量刑情节识别效果不佳的问题,提出一种基于反绎学习的量刑情节识别模型ABL-CON。首先结合神经网络与领域逻辑推理,通过半监督学习方法,使用置信学习方法表征情节识别置信度;然后修正无标签数据经过神经网络产生的不合逻辑的错误情节,重新训练识别模型,以提高识别精度。在自构建的司法数据集上的实验结果表明,使用50%标注数据与50%无标注数据的ABL-CON模型在Macro_F1值和Micro_F1值上分别达到了90.35%和90.58%,优于同样条件下的BERT和SS-ABL,也超越了使用100%标注数据的BERT模型。ABL-CON模型通过逻辑反绎修正不符合逻辑的标签能够有效提高标签的逻辑合理性以及标签的识别能力。     

基于细粒度信息集成的意图识别和槽填充联合模型

意图识别和槽位填充是构建口语理解(SLU)系统的两项主要任务，两者相互联合的模型是对话系统的研究热点。这两个任务紧密相连，槽位填充通常高度依赖于意图信息。针对最近联合模型中：固定阈值很难在不同领域中选择出正向的投票，且复杂的意图信息不能充分地引导槽位填充的问题。提出了一种基于细粒度信息集成的意图识别和槽填充联合模型。其中，将由意图解码器获取的意图信息与各单词的编码表示拼接，形成意图引导的集成编码表示，从而为单词级槽位填充提供细粒度的意图信息。同时，通过计算最大意图得分和最小意图得分的中间值获得逻辑自适应阈值，并用其代替固定阈值。逻辑自适应阈值可随不同意图标签的得分分布而变化。通过在两个多标签数据集上的实验结果验证了提出的模型的性能。     

深度学习算法在问句意图分类中的应用研究

在聊天机器人多轮对话中如何根据上下文理解用户的意图是多轮交互中的一个重点问题，也是一个难点问题。现有的问句理解方法大多是针对单句的，且侧重于某种句式结构的理解。如何根据上下文语境对当前用户的意图进行识别，而不仅仅是针对单轮进行一个个分析，使得对话在一个连续的语境下具备细粒度的理解能力，是一个亟待解决的问题。针对以上问题，提出了一种基于深度学习的自然语言问句多意图分类方法，其中涉及到的用户意图包含闲聊类、音乐类、新闻类、算术类、餐饮类、订票类、天气类、服务类等13类。首先使用自然语言处理的相关技术对多轮对话进行处理分析，识别出其中的关键词，然后使用深度学习方法和分层分类技术构建了二分类和多分类深度学习模型，学习上下文语境和语义关系，共同对用户意图进行识别。通过实验证明了构建的深度学习模型对用户意图识别的准确率分别为94.81%、93.49%。因此，所提方法基本能够解决自然语言问句意图识别的问题。     

态势估计模型的研究与实现

态势估计属于数据融合中的二级融合，它的目标就是在一级融合的基础上，通过对各个作战对象行为、状态、事件、企图及其关系的分析，给出参战各方力量部署、作战能力、效能尽可能准确、完整的军事态势估计和感知，并对战术画面进行解释，辨别敌方意图。但现代战争涉及到的作战对象多、协同关系复杂、战术机动频繁，建立一个完善的态势估计模型存在很多困难。该文提出了基于态势觉察、态势理解和态势预测的三级态势估计功能模型；给出了基于模板的计划识别推理框架，重点讨论了基于多级分层黑板模型在态势估计中的应用。     

基于直觉模糊Petri网的敌意图识别方法研究

现代战争中由于战场态势的不确定性,敌战术意图的识别也应作为一个动态的不确定性决策问题进行研究.针对该不确定性动态识别问题,综合利用直觉模糊集合理论与Petri网理论的优点,构建了敌战术意图识别的直觉模糊Petri网模型及其推理算法,在推理过程中充分发挥Petri网的动态并行推理能力,使得推理更加高效,由于非隶属参数的作用,推理结果更加准确可信.通过建模仿真给出该模型的应用步骤,验证了其正确性和合理性.     

改进高维数据相似度的目标意图识别方法

对于实际战场中目标属性要素呈现出的多样化,传统目标意图识别方法不能够较全面地建立属性之间的相似度模型.为了更好地阐述实际战场的复杂情况,提高目标意图识别的准确度,提出了一种利用改进的空间相似度与属性相似度融合的高维数据相似度模型,以全面地计算目标各种属性状态对态势意图的支持程度,再利用得到的高维数据相似度通过D-S证据理论对目标进行序贯识别.仿真实验表明:该方法具有有效性以及能够提高目标意图识别的准确度,为解决目标战术意图识别提供了新的方法.     

网络入侵意图识别方法综述

复合攻击的检测是近年来IDS着力解决的一个重要问题。研究表明，解决这个问题的根本途径在于建立有效的模型积累、识别多报文间的上下文关系，从而进一步对入侵的意图进行精确判断。本文跟踪了近年来意图识别领域的技术发展，详细介绍了几种有代表性方法的核心思想，分析它们适用的范围和存在的问题，比较了各自的优劣所在，最后总结了这个领域的难点问题和发展趋势。     

面向任务口语理解研究现状综述

口语理解是对话系统重要的功能模块，语义槽填充和意图识别是面向任务口语理解的两个关键子任务。近年来，联合识别方法已经成为解决口语理解中语义槽填充和意图识别任务的主流方法，介绍两个任务由独立建模到联合建模的方法，重点介绍基于深度神经网络的语义槽填充和意图识别联合建模方法，并总结了目前存在的问题以及未来的发展趋势。     

基于隐马尔可夫模型的攻击意图识别技术研究

攻击意图识别是海量报警数据处理的重要技术。隐马尔可夫模型HMM能够很好地对复杂攻击行为建模，但对含干扰因素报警序列的攻击意图识别效果不够理想。本文为此提出了改进方案，并根据攻击意图识别的特殊性定义了新的解码问题，设计了解码算法。     

A formal description of tactical plan recognition

Plan recognition can roughly be described as the problem of finding the plan(s) underlying the observed behaviour of agent(s). Of course, usually, the observed behaviour and available background knowledge does not determine the underlying plan, and therefore one can typically at best generate (reasonable) plan hypotheses. Traditionally, plan recognition has been studied, formalized and implemented in areas like story understanding and user modelling. In this paper, we propose a formal definition of tactical plan recognition, i.e. the recognition of enemy plans. We will focus on military applications, where this task of tactical plan recognition is crucial, but this task is relevant for every application where one has to deal with intelligent adversial agents.Tactical plan recognition differs from traditional plan recognition in a number of ways. For example, an enemy will often try to avoid making his plans known. We will not pay much explicit attention to this feature. We will focus on another important characteristic feature of tactical plan recognition, namely that the identity of the observed enemy objects, for which plans are to be recognized, may be unknown. A consequence of this is that it is typically not known which observations originate from the same objects.Our formalization of plan recognition is based on classical abduction. The concepts of classical abduction can readily be applied to plan recognizers for identified observations, as has been done by Lin and Goebel [18] and Bauer and Paul [7]. However, for tactical plan recognition some adaptations have to be made. Here the plan recognizer will not only have to generate plan hypotheses, but also assignment hypotheses, which correspond to formal links of objects to observations. A choice for an assignment is essentially a decision concerning the question which observations originate from the same objects.For observations with stochastic variables the probability of an assignment hypothesis is calculated, rather than the probability of the plan hypotheses. For this, Reid’s multiple hypothesis tracking formula can be adapted to calculate the assignment hypothesis probability.     

基于智能规划的计划识别模型

为了解决战场态势评估问题,提出了一个基于智能规划的军事计划识别模型,给出了基于一阶谓词逻辑的计划识别模型描述方法,论述了基于该模型的计划假设识别方法。模型可以根据世界模型和作战目标的要求实时生成新的计划,来满足当前计划识别的需要,克服了Kautz计划识别框架的不足。     

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。     

基于深度学习的SAR目标识别DSP设计

SAR图像目标识别主要针对桥梁、机场等战略军事目标以及飞机、坦克、汽车等战术目标,进行精确的识别分类及定位,是SAR图像解译的重要一环。首先,构建C6678的卷积神经网络主要处理层,然后结合C6678的处理及存储特性,对卷积层和网络调度进行优化设计,完成了YOLOv3-TINY目标识别网络在C6678上的设计实现方法。该方法能够对常用卷积神经网络模型进行重构及修改,解决了C6678等多核DSP处理平台运行深度学习网络的难题。实验结果表明,该方法在检测性能上与GPU一致,考虑到机载SAR的实时图像帧率,虽然该方法在C6678的实时性能相对GPU还有较大差距,但其能够满足机载SAR实时处理需求。     

一种基于规划知识图的规划识别算法

提出了一种新的规划表示方法及规划知识图的概念.与目前规划识别领域广泛使用的Kautz方法相比,新的规划表示方法更加简便与直观.由于在规划知识图中增加了支持程度的概念,使得规划的识别可以随着收集到的新证据而合理地加以改变.所提出的基于规划知识图的规划识别算法把规划的识别问题转化为图搜索问题,因而其效率更高,但其识别结果与Kautz方法是一致的.     

A meta-rule approach to flexible plan recognition in dialogue

Although a number of researchers have demonstrated that reasoning on a model of the user's plans and goals is helpful in language understanding and response generation, current models of plan inference cannot handle naturally occurring dialogue. This paper argues that model building from less than ideal dialogues has a great deal in common with processing ill-formed input. It defines well-formedness constraints for information-seeking dialogues and contends that strategies for interpreting ill-formed input can be applied to the problem of modeling the user's plan during an ill-formed dialogue. It presents a meta-rule approach for hypothesizing the cause of dialogue ill-formedness, and describes meta-rules for relaxing the plan inference process and enabling the consideration of alternative hypotheses. The advantages of this approach are that it provides a unified framework for handling both well-formed and ill-formed dialogue, avoids unnatural interpretations when the dialogue is proceeding smoothly, and facilitates a nonmonotonic plan recognition system.Rhonda Eller is a Ph.D. candidate in Computer Science at the University of Delaware. She received her B.S. in Computer Science from Millersville University of Pennsylvania in 1987, and her M.S. degree in the same field from the University of Delaware. Her primary interests lie in the areas of natural language processing, plan recognition, and user modelling. This paper summarizes the current state of her thesis work on repair of an incorrectly inferred plan for a user.Sandra Carberry is an associate professor of computer science at the University of Delaware. Her research interests include discourse understanding, user modeling, planning and plan recognition, and intelligent natural language interfaces. This paper describes work that is part of an ongoing research project to develop a robust model of plan recognition.     

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.     

多分类器融合实现机型识别

针对空战目标识别中机型识别这一问题,提出了基于多分类器融合的识别方法。该方法以战术性能参数为输入,便于满足空战的实时性要求。通过广泛收集数据,得到机型识别的分类特征,选取分类特征的子集作为单分类器的特征,用BP网络设计单分类器,然后选用性能优良的和规则进行分类器融合,求得最终的决策。实验结果表明,多分类器融合的识别性能明显优于参与融合的分类器,也优于相同输入的单分类器。该方法的另一特点是能够进行缺省推理,因而有较强的抗干扰能力,适合真实战场环境的需要。     

Recognizing the agent''s goals incrementally:planning graph as a basis

Plan recognition, the inverse problem of plan synthesis, is important wherever a system is expected to produce a kind of cooperative or competitive behavior. Most plan recognizers, however, suffer the problem of acquisition and hand-coding a larger plan library. This paper is aims to show that modern planning techniques can help build plan recognition systems without suffering such problems. Specifically, we show that the planning graph, which is an important component of the classical planning system Graph-plan, can be used as an implicit, dynamic planning library to represent actions, plans and goals. We also show that modern plan generating technology can be used to find valid plans in this framework. In this sense, this method can be regarded as a bridge that connects these two research fields. Empirical and theoretical results also show that the method is efficient and scalable.