显式知识推理和深度强化学习结合的动态决策

近年来,深度强化学习在序列决策领域被广泛应用并且效果良好,尤其在具有高维输入、大规模状态空间的应用场景中优势明显.然而,深度强化学习相关方法也存在一些局限,如缺乏可解释性、初期训练低效与冷启动等问题.针对这些问题,提出了一种基于显式知识推理和深度强化学习的动态决策框架,将显式的知识推理与深度强化学习结合.该框架通过显式知识表示将人类先验知识嵌入智能体训练中,让智能体在强化学习中获得知识推理结果的干预,以提高智能体的训练效率,并增加模型的可解释性.将显式知识分为两种,即启发式加速知识与规避式安全知识.前者在训练初期干预智能体决策,加快训练速度;而后者将避免智能体作出灾难性决策,使其训练过程更为稳定.实验表明,该决策框架在不同强化学习算法上、不同应用场景中明显提高了模型训练效率,并增加了模型的可解释性.     

基于规则推理网络的分类模型

为了缓解神经网络的“黑盒子”机制引起的算法可解释性低的问题,基于使用证据推理算法的置信规则库推理方法(以下简称RIMER)提出了一个规则推理网络模型.该模型通过RIMER中的置信规则和推理机制提高网络的可解释性.首先证明了基于证据推理的推理函数是可偏导的,保证了算法的可行性;然后,给出了规则推理网络的网络框架和学习算法,利用RIMER中的推理过程作为规则推理网络的前馈过程,以保证网络的可解释性;使用梯度下降法调整规则库中的参数以建立更合理的置信规则库,为了降低学习复杂度,提出了“伪梯度”的概念;最后,通过分类对比实验,分析了所提算法在精确度和可解释性上的优势.实验结果表明,当训练数据集规模较小时,规则推理网络的表现良好,当训练数据规模扩大时,规则推理网络也能达到令人满意的结果.     

实体异构性下证据链融合推理的多属性群决策

针对多属性群决策中可解释性证据融合推理的实体异构性问题,给出了一个实体异构性下证据链融合推理的多属性群决策方法.基于证据推理理论,引入证据链关联的概念,从多数据表提供的数据矩阵中获取可区分的近邻证据集,推导了各数据表的相似度矩阵,并构建半正定矩阵的二次优化模型,共享群决策专家的经验知识.使用Dempster正交规则,论证了异构实体之间可解释性推理中可信度融合的合理性,并使用证据融合规则集成各个数据表的近邻证据中获得的可信度,验证了调和多源异构数据中不一致信息的有效性.通过具有实体异构性的心脏病多决策数据诊断实例说明了方法的可行性与合理性.     

一种融合胶囊网络的分类方法

目前的ADMET分类方法在对具有多特征性和特征关联性的化合物数据进行ADMET分类时存在不足。而且,对ADMET分类结果不具备可解释性。针对上述问题,提出一种融合胶囊网络的分类模型（CapsMC）。CapsMC模型首先提出一种feature-to-image图像转换算法。使用该算法将特征之间的关联关系和依赖关系作为考量纳入到分类依据中,实现特征的多层次提取。其次,探索胶囊网络的高级应用,提出一种认知推理机制。使用该机制对特征进行认知推理,实现ADMET的可解释性分类。模型在五种ADMET数据集上的实验结果表明,CapsMC模型可以高效实现ADMET的可解释性分类。     

可解释的知识图谱推理方法综述

近年来,以深度学习模型为基础的人工智能研究不断取得突破性进展,但其大多具有黑盒性,不利于人类认知推理过程,导致高性能的复杂算法、模型及系统普遍缺乏决策的透明度和可解释性。在国防、医疗、网络与信息安全等对可解释性要求严格的关键领域,推理方法的不可解释性对推理结果及相关回溯造成较大影响,因此,需要将可解释性融入这些算法和系统中,通过显式的可解释知识推理辅助相关预测任务,形成一个可靠的行为解释机制。知识图谱作为最新的知识表达方式之一,通过对语义网络进行建模,以结构化的形式描述客观世界中实体及关系,被广泛应用于知识推理。基于知识图谱的知识推理在离散符号表示的基础上,通过推理路径、逻辑规则等辅助手段,对推理过程进行解释,为实现可解释人工智能提供重要途径。针对可解释知识图谱推理这一领域进行了全面的综述。阐述了可解释人工智能和知识推理相关概念。详细介绍近年来可解释知识图谱推理方法的最新研究进展,从人工智能的3个研究范式角度出发,总结了不同的知识图谱推理方法。提出对可解释的知识图谱推理研究前景和未来研究方向。     

面向图像分类的深度模型可解释性研究综述

深度学习目前在计算机视觉、自然语言处理、语音识别等领域得到了深入发展,与传统的机器学习算法相比,深度模型在许多任务上具有较高的准确率.然而,作为端到端的具有高度非线性的复杂模型,深度模型的可解释性没有传统机器学习算法好,这为深度学习在现实生活中的应用带来了一定的阻碍.深度模型的可解释性研究具有重大意义而且是非常必要的,近年来许多学者围绕这一问题提出了不同的算法.针对图像分类任务,将可解释性算法分为全局可解释性和局部可解释性算法.在解释的粒度上,进一步将全局解释性算法分为模型级和神经元级的可解释性算法,将局部可解释性算法划分为像素级特征、概念级特征以及图像级特征可解释性算法.基于上述分类框架,总结了常见的深度模型可解释性算法以及相关的评价指标,同时讨论了可解释性研究面临的挑战和未来的研究方向.认为深度模型的可解释性研究和理论基础研究是打开深度模型黑箱的必要途径,同时可解释性算法存在巨大潜力可以为解决深度模型的公平性、泛化性等其他问题提供帮助.     

基于知识线记忆的多分类器集成算法

多分类器系统作为混合智能系统的分支,集成了具有多样性的分类器集合,使整体得到更优的分类性能.结果融合是该领域中的一个重要问题,在相同分类器成员下,好的融合策略可以有效提升系统整体的分类正确率.随着模型安全性得到重视,传统融合策略可解释性差的问题凸显.本文基于心理学中的知识线记忆理论进行建模,参考人类决策过程,提出了一种...     

一种基于自适应证据推理规则的集成学习方法

当前集成学习中的结合策略难以兼顾各个基学习器之间的信息和模型的可解释性。使用证据推理（evidential reasoning,ER）规则作为结合策略,将各个基学习器结果作为证据参与融合,可以较好地解决以上问题。但传统ER规则的证据参数是单一的,对不同的基学习器模型使用相同的证据参数显然是不合理的。为此,提出一种基于自适应证据推理（adaptive-evidential reasoning,A-ER）规则的集成学习方法,该方法在每次证据融合前对证据的类别进行判断,针对不同的证据类别自适应分配不同的证据参数。通过不同的分类案例表明,该方法与案例中其他方法相比具有更高的分类精度,证明了该方法使证据参数设置更加合理且具有更好的可解释性和泛化能力。     

融合XGBoost与SHAP的冠心病预测及其特征分析模型

为了解决冠心病诊断模型中性能无法满足临床应用要求、缺乏可解释性的问题,提出一种融合XGBoost与SHAP的冠心病预测及其特征分析模型。在对数据集进行特征工程的基础上,将处理好的数据集输入XGBoost模型进行训练,并且对模型进行优化,进一步提高了模型的性能表现;其次,与基于SVM、朴素贝叶斯等六种机器学习模型以及八种主流机器学习模型进行实验对比,参数优化后的XGBoost模型在准确率、特异度、F1值和AUC值四个指标上分别达到0.9942、0.9970、0.9941和0.9998,均优于已有模型;最后引入SHAP框架增强模型可解释性,综合四种模型特征重要性排序结果,识别出影响冠心病的重要因素,为医生作出正确的诊断提供决策参考。     

基于粗糙集的约简算法及规则融合方法的研究

从不同的角度分析了属性约简的两种重要方法:区分矩阵法和基于属性重要性.根据数据集的实际情况提出了一种基于粗糙集的区分矩阵和属性重要性相结合的启发式算法,并获得了属性约简集.在约简集的基础上分析了静态决策推理规则及算法.在相容决策系统中利用集合向量包含度构造了规则融合的方法,从而得到动态条件规则的极大近似决策值.在知识满足分类质量要求的前提下,根据规则融合方法,对任意给定的样本知识可以判别知识的实际归属类.     

Only connect: teaching, technology and telesis

Abstract This paper describes an approach to the design of interactive multimedia materials being developed in a European Community project. The developmental process is seen as a dialogue between technologists and teachers. This dialogue is often problematic because of the differences in training, experience and culture between them. Conditions needed for fruitful dialogue are described and the generic model for learning design used in the project is explained.     

European Community policy and the market

Abstract This paper starts with some reflections on the policy considerations and priorities which are shaping European Commission (EC) research programmes. Then it attempts to position the current projects which seek to capitalise on information and communications technologies for learning in relation to these priorities and the apparent realities of the marketplace. It concludes that while there are grounds to be optimistic about the contribution EC programmes can make to the efficiency and standard of education and training, they are still too technology driven.     

一种自适应子融合集成多分类器方法

融合集成方法已经广泛应用在模式识别领域,然而一些基分类器实时性能稳定性较差,导致多分类器融合性能差,针对上述问题本文提出了一种新的基于多分类器的子融合集成分类器系统。该方法考虑在度量层融合层次之上通过对各类基多分类器进行动态选择,票数最多的类别作为融合系统中对特征向量识别的类别,构成一种新的自适应子融合集成分类器方法。实验表明,该方法比传统的分类器以及分类融合方法识别准确率明显更高,具有更好的鲁棒性。     

Avoiding semantic and temporal gaps in developing software intensive systems

Development of software intensive systems (systems) in practice involves a series of self-contained phases for the lifecycle of a system. Semantic and temporal gaps, which occur among phases and among developer disciplines within and across phases, hinder the ongoing development of a system because of the interdependencies among phases and among disciplines. Such gaps are magnified among systems that are developed at different times by different development teams, which may limit reuse of artifacts of systems development and interoperability among the systems. This article discusses such gaps and a systems development process for avoiding them.     

Designing economic np control charts: A programmed simulation approach

This paper presents control charts models and the necessary simulation software for the location of economic values of the control parameters. The simulation program is written in FORTRAN, requires only 10K of main storage, and can run on most mini and micro computers. Two models are presented - one describes the process when it is operating at full capacity and the other when the process is operating under capacity. The models allow the product quality to deteriorate to a further level before an existing out-of-control state is detected, and they can also be used in situations where no prior knowledge exists of the out-of-control causes and the resulting proportion defectives.     

The development of robot art

Going through a few examples of robot artists who are recognized worldwide, we try to analyze the deepest meaning of what is called “robot art” and the related art field definition. We also try to highlight its well-marked borders, such as kinetic sculptures, kinetic art, cyber art, and cyberpunk. A brief excursion into the importance of the context, the message, and its semiotics is also provided, case by case, together with a few hints on the history of this discipline in the light of an artistic perspective. Therefore, the aim of this article is to try to summarize the main characteristics that might classify robot art as a unique and innovative discipline, and to track down some of the principles by which a robotic artifact can or cannot be considered an art piece in terms of social, cultural, and strictly artistic interest. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Explanation and prediction: an architecture for default and abductive reasoning

Although there are many arguments that logic is an appropriate tool for artificial intelligence, there has been a perceived problem with the monotonicity of classical logic. This paper elaborates on the idea that reasoning should be viewed as theory formation where logic tells us the consequences of our assumptions. The two activities of predicting what is expected to be true and explaining observations are considered in a simple theory formation framework. Properties of each activity are discussed, along with a number of proposals as to what should be predicted or accepted as reasonable explanations. An architecture is proposed to combine explanation and prediction into one coherent framework. Algorithms used to implement the system as well as examples from a running implementation are given.     

Three Process Perspectives: Organizations, Teams, and People

This paper provides the author's personal views and perspectives on software process improvement. Starting with his first work on technology assessment in IBM over 20 years ago, Watts Humphrey describes the process improvement work he has been directly involved in. This includes the development of the early process assessment methods, the original design of the CMM, and the introduction of the Personal Software Process (PSP)^SM and Team Software Process (TSP){^SM}. In addition to describing the original motivation for this work, the author also reviews many of the problems he and his associates encountered and why they solved them the way they did. He also comments on the outstanding issues and likely directions for future work. Finally, this work has built on the experiences and contributions of many people. Mr. Humphrey only describes work that he was personally involved in and he names many of the key contributors. However, so many people have been involved in this work that a full list of the important participants would be impractical.     

基于复小波噪声方差显著修正的SAR图像去噪

提出了一种基于复小波域统计建模与噪声方差估计显著性修正相结合的合成孔径雷达(Synthetic Aperture Radar,SAR)图像斑点噪声滤波方法。该方法首先通过对数变换将乘性噪声模型转化为加性噪声模型,然后对变换后的图像进行双树复小波变换(Dualtree Complex Wavelet Transform,DCWT),并对复数小波系数的统计分布进行建模。在此先验分布的基础上,通过运用贝叶斯估计方法从含噪系数中恢复原始系数,达到滤除噪声的目的。实验结果表明该方法在去除噪声的同时保留了图像的细节信息,取得了很好的降噪效果。     

基于QTE的蒙古文显示机制研究与实现

蒙古语言是中国蒙古族使用的通用语言,由于蒙古文区别于其他文字的书写方式和其自身变形机制等特点,在很多通用的文字处理引擎中都不被支持。在嵌入式产品开发与应用领域中Linux加QTE已经成为流行方式。该文给出了一种在QTE环境上实现基于标准Unicode的蒙古文点阵显示和变形算法, 并自定义了支持蒙古文的QTE组件,扩展了QTE功能,为在Linux加QTE方式的嵌入式体系结构中处理蒙古文提供了一种解决方法。