深度Q学习的二次主动采样方法

实现深度Q学习的一种方式是深度Q网络（Deep Q-networks,DQN）.经验回放方法利用经验池中的样本训练深度Q网络,构造经验池需要智能体与环境进行大量交互,这样会增加成本和风险.一种减少智能体与环境交互次数的有效方式是高效利用样本.样本所在序列的累积回报对深度Q网络训练有影响.累积回报大的序列中的样本相对于累积回报小的序列中的样本更能加速深度Q网络的收敛速度,并提升策略的质量.本文提出深度Q学习的二次主动采样方法.首先,根据序列累积回报的分布构造优先级对经验池中的序列进行采样.然后,在已采样的序列中根据样本的TD-error（Temporal-difference error）分布构造优先级对样本采样.随后用两次采样得到的样本训练深度Q网络.该方法从序列累积回报和TD-error两个方面选择样本,以达到加速深度Q网络收敛,提升策略质量的目的.在Atari平台上进行了验证.实验结果表明,用经过二次主动采样得到的样本训练深度Q网络取得了良好的效果.     

基于多智能体强化学习的乳腺癌致病基因预测

通过分析基因突变过程, 提出利用强化学习对癌症患者由正常状态至患病状态的过程进行推断, 发现导致患者死亡的关键基因突变. 首先, 将基因视为智能体, 基于乳腺癌突变数据设计多智能体强化学习环境; 其次, 为保证智能体探索到与专家策略相同的策略和满足更多智能体快速学习, 根据演示学习理论, 分别提出两种多智能体深度Q网络: 基于行为克隆的多智能体深度Q网络和基于预训练记忆的多智能体深度Q网络; 最后, 根据训练得到的多智能体深度Q网络进行基因排序, 实现致病基因预测. 实验结果表明, 提出的多智能体强化学习方法能够挖掘出与乳腺癌发生、发展过程密切相关的致病基因.     

面向样本扩充的新型风格迁移网络研究

全监督语义分割网络在训练时需要耗费大量的人力与时间成本来标注样本。所以减少人工标注样本的时间,同时提升语义分割效果,对于深度学习网络的快速部署和应用推广具有重要意义。提出一种基于改进图像风格迁移网络（CycleGAN-AD）的样本扩充方法。以CycleGAN为基础,在生成器中引入注意力机制并将深度残差网络改为密集连接卷积网络。利用计算机批量产生自带标签的模拟样本,使用CycleGAN-AD网络将模拟样本风格迁移成为真实样本风格（标签不变）,并用于扩充训练样本。对石墨电极的钢印字符进行语义分割的实验结果表明,采用CycleGAN-AD网络进行样本扩充后,其分割效果得到显著提升,MIoU值最高升至0.826 0。可见,提出的样本扩充方法有希望在显著减少人工标注工作量的同时,获得高质量的训练样本。     

序列多智能体强化学习算法

针对当前多智能体强化学习算法难以适应智能体规模动态变化的问题,文中提出序列多智能体强化学习算法(SMARL).将智能体的控制网络划分为动作网络和目标网络,以深度确定性策略梯度和序列到序列分别作为分割后的基础网络结构,分离算法结构与规模的相关性.同时,对算法输入输出进行特殊处理,分离算法策略与规模的相关性.SMARL中的...     

双输入流深度反卷积的插值神经网络

在实际工作中深度学习方法通常不具备大量的训练样本，因此提出了双输入流深度反卷积生成神经网络的构架，依据给定的条件产生新的目标图像，从而扩充训练样本集。该神经网络的整体架构由双输入的卷积网络和一个反卷积网络输出构成，其中双输入卷积网络接收目标物体不同视角的两张图片并提取抽象特征，而反卷积网络则利用抽象特征和设定的参数产生新的插值目标图像。在ShapeNetCore数据集上的实验结果显示，在相同数量的训练样本空间中，与未扩展数据集的卷积网络相比，双输入流深度反卷积生成神经网络的识别率提高了20%左右。结果表明，双输入流深度反卷积生成神经网络无需输入目标物类别，可生成新参数条件下的目标图像，扩充训练样本空间，从而提高识别率，可用于少样本的目标物多角度识别。     

结合向前状态预测和隐空间约束的强化学习表示算法

虽然深度强化学习能够解决很多复杂的控制问题, 但是需要付出的代价是必须和环境进行大量的交互, 这是深度强化学习所面临的一大挑战. 造成这一问题的原因之一是仅依靠值函数损失难以让智能体从高维的复杂输入中提取有效特征. 导致智能体对所处状态理解不足, 从而不能正确给状态分配价值. 因此, 为了让智能体认识所处环境, 提高强化学习样本效率, 本文提出一种结合向前状态预测与隐空间约束的表示学习方法(regularized predictive representation learning, RPRL). 帮助智能体从高维视觉输入中学习并提取状态特征, 以此来提高强化学习样本效率. 该方法用前向的状态转移损失作为辅助损失, 使智能体学习到的特征包含环境转移的相关动态信息. 同时在向前预测的基础上添加正则化项对隐空间的状态表示进行约束, 进一步帮助智能体学习到高维度输入的平滑、规则表示. 该方法在DeepMind Control (DMControl)环境中与其他的基于模型的方法以及加入了表示学习的无模型方法进行比较, 都获得了更好的性能.     

基于BN-DDPG轻量级强化学习算法的智能兵棋推演

兵棋推演与智能算法融合成为当前军事应用领域的研究热点,利用深度强化学习技术实现仿真推演中决策过程的智能化,可显著减少人为经验对决策过程的影响,提高推演效率和灵活性.现有基于DRL算法的决策模型,其训练时间过长,算力开销过大,无法满足作战任务的实时性需求.本文提出一种基于轻量级深度确定性策略梯度(BN-DDPG)算法的智能推演方法,根据推演规则,采用马尔可夫决策过程描述推演过程中的决策行为,以actorcritic体系为基础,构建智能体训练网络,其中actor网络使用自定义混合二进制神经网络,减少计算量;同时根据经验样本的状态和回报值建立双缓冲池结构,采用环境相似度优先提取的方法对样本进行采样,提高训练效率;最后基于自主研制的仿真推演平台进行实例验证.结果表明, BN-DDPG算法可简化模型训练过程,加快模型收敛速度,显著提高推演决策的准确性.     

智能车辆深度强化学习的模型迁移轨迹规划方法

针对智能驾驶车辆传统路径规划中出现车辆模型跟踪误差和过度依赖问题,提出一种基于深度强化学习的模型迁移的智能驾驶车辆轨迹规划方法.首先,提取真实环境的抽象模型,该模型利用深度确定性策略梯度(DDPG)和车辆动力学模型,共同训练逼近最优智能驾驶的强化学习模型;其次,通过模型迁移策略将实际场景问题迁移至虚拟抽象模型中,根据该环境中训练好的深度强化学习模型计算控制与轨迹序列;而后,根据真实环境中评价函数选择最优轨迹序列.实验结果表明,所提方法能够处理连续输入状态,并生成连续控制的转角控制序列,减少横向跟踪误差;同时通过模型迁移能够提高模型的泛化性能,减小过度依赖问题.     

稀疏场景下基于理性好奇心的多智能体强化学习

强化学习当前越来越多地应用于多智能体系统。在强化学习中,奖励信号起引导智能体学习的作用,然而多智能体系统任务复杂,可能只在任务结束时才能获得环境的反馈,导致奖励稀疏,大幅降底算法的收敛速度和效率。为解决稀疏奖励问题,提出一种基于理性好奇心的多智能体强化学习方法。受内在动机理论的启发,将好奇心思想扩展到多智能体中,并给出理性好奇心奖励机制,利用分解求和的网络结构将不同排列的联合状态编码到同一特征表示,减少联合状态的探索空间,将网络的预测误差作为内在奖励,引导智能体去研究新颖且有用的效用状态。在此基础上,引入双值函数网络对Q值进行评估,采用最小化算子计算目标值,缓解Q值的过估计偏差和方差,并采用均值优化策略提高样本利用。在追捕任务和合作导航任务的环境中进行实验评估,结果表明,在最困难的追捕任务中,该方法相较于基线算法,胜率提高15%左右,所需时间步降低20%左右,在合作导航任务中也具有较快的收敛速度。     

基于样本扩充和改进2DPCA的单样本人脸识别

针对大多数人脸识别方法在单个训练样本条件下识别性能下降的问题,提出了结合多种样本扩充方法和改进二维主成分分析(2DPCA)的人脸识别算法。通过分析各种样本扩充方法的优缺点,用多种样本扩充方法来生成虚拟样本,以充分利用单一样本所提供的信息。采用改进的2DPCA方法对生成的虚拟样本进行特征提取,对训练样本进行分块,并用类内平均值规范后的分块来构造总体散布矩阵。在ORL和Yale人脸库上的实验表明,所提出的方法在识别性能方面优于普通的2DPCA方法,优于单一的样本扩充方法。     

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。     

How do children do mathematics with LOGO?

Abstract  This paper considers some results of a study designed to investigate the kinds of mathematical activity undertaken by children (aged between 8 and 11) as they learned to program in LOGO. A model of learning modes is proposed, which attempts to describe the ways in which children used and acquired understanding of the programming/mathematical concepts involved. The remainder of the paper is concerned with discussing the validity and limitations of the model, and its implications for further research and curriculum development.