再励学习及其在移动机器人行为规划中的应用

再励学习（Reinforcement Learning,RL）是一种成功地结合动态编程和控制问题的机器智能方法,它将动态编程和有监督学习方法结合到机器学习系统中,通常用于解决预测和控制两类问题。提出了以矢量形式表示的评估函数,为了实现多维再励学习,用一专门的神经网络（Q网络）实现评判网络,研究其在移动机器人行为规划中的应用。     

From computational intelligence to Web intelligence

The authors explore three topics in computational intelligence: machine translation, machine learning and user interface design and speculate on their effects on Web intelligence. Systems that can communicate naturally and learn from interactions will power Web intelligence's long term success. The large number of problems requiring Web-specific solutions demand a sustained and complementary effort to advance fundamental machine-learning research and incorporate a learning component into every Internet interaction. Traditional forms of machine translation either translate poorly, require resources that grow exponentially with the number of languages translated, or simplify language excessively. Recent success in statistical, nonlinguistic, and hybrid machine translation suggests that systems based on these technologies can achieve better results with a large annotated language corpus. Adapting existing computational intelligence solutions, when appropriate for Web intelligence applications, must incorporate a robust notion of learning that will scale to the Web, adapt to individual user requirements, and personalize interfaces.     

机器学习在RoboCup中的应用研究

RoboCup is a particularly good domain for studying multi-agent systems.A wide variety of MAS issues can be studied in robotic soccer,in which the theory,algorithm and architecture of agent system can be evaluated.Because of the inherent complexity of MAS,there are many interests in using machine learning techniques to handle it.This paper investigates and discusses the machine-learning techniques used in RoboCup.The background is firstly presented and the application of machine learning in RoboCup is lately demonstrated with some top simulation teams.The machine-learning system in NDSocTeam is also introduced.Finally some open issues in this field are pointed out.     

A new approach of clustering based machine-learning algorithm

Machine-learning research is to study and apply the computer modeling of learning processes in their multiple manifestations, which facilitate the development of intelligent system. In this paper, we have introduced a clustering based machine-learning algorithm called clustering algorithm system (CAS). The CAS algorithm is tested to evaluate its performance and find fruitful results. We have been presented some heuristics to facilitate machine-learning authors to boost up their research works. The InfoBase of the Ministry of Civil Services is used to analyze the CAS algorithm. The CAS algorithm is compared with other machine-learning algorithms like UNIMEM, COBWEB, and CLASSIT, and was found to have some strong points over them. The proposed algorithm combined advantages of two different approaches to machine learning. The first approach is learning from Examples, CAS supports Single and Multiple Inheritance and Exceptions. CAS also avoids probability assumptions which are well understood in concept formation. The second approach is learning by Observation. CAS applies a set of operators that have proven to be effective in conceptual clustering. We have shown how CAS builds and searches through a clusters hierarchy to incorporate or characterize an object.     

基于学习的鲁棒自适应评判控制研究进展

在作为人工智能核心技术的机器学习领域,强化学习是一类强调机器在与环境的交互过程中进行学习的方法,其重要分支之一的自适应评判技术与动态规划及最优化设计密切相关.为了有效地求解复杂动态系统的优化控制问题,结合自适应评判,动态规划和人工神经网络产生的自适应动态规划方法已经得到广泛关注,特别在考虑不确定因素和外部扰动时的鲁棒自适应评判控制方面取得了很大进展,并被认为是构建智能学习系统和实现真正类脑智能的必要途径.本文对基于智能学习的鲁棒自适应评判控制理论与主要方法进行梳理,包括自学习鲁棒镇定,自适应轨迹跟踪,事件驱动鲁棒控制,以及自适应H_∞控制设计等,并涵盖关于自适应评判系统稳定性、收敛性、最优性以及鲁棒性的分析.同时,结合人工智能、大数据、深度学习和知识自动化等新技术,也对鲁棒自适应评判控制的发展前景进行探讨.     

Universal Intelligence: A Definition of Machine Intelligence

A fundamental problem in artificial intelligence is that nobody really knows what intelligence is. The problem is especially acute when we need to consider artificial systems which are significantly different to humans. In this paper we approach this problem in the following way: we take a number of well known informal definitions of human intelligence that have been given by experts, and extract their essential features. These are then mathematically formalised to produce a general measure of intelligence for arbitrary machines. We believe that this equation formally captures the concept of machine intelligence in the broadest reasonable sense. We then show how this formal definition is related to the theory of universal optimal learning agents. Finally, we survey the many other tests and definitions of intelligence that have been proposed for machines.     

人工智能赋能的数据管理技术研究

大数据时代,数据规模庞大、数据管理应用场景复杂,传统数据库和数据管理技术面临很大的挑战.人工智能技术因其强大的学习、推理、规划能力,为数据库系统提供了新的发展机遇.人工智能赋能的数据库系统通过对数据分布、查询负载、性能表现等特征进行建模和学习,自动地进行查询负载预测、数据库配置参数调优、数据分区、索引维护、查询优化、查询调度等,以不断提高数据库针对特定硬件、数据和负载的性能.同时,一些机器学习模型可以替代数据库系统中的部分组件,有效减少开销,如学习型索引结构等.分析了人工智能赋能的数据管理新技术的研究进展,总结了现有方法的问题和解决思路,并对未来研究方向进行了展望.     

Artificial intelligence applications in the telecommunications industry

Abstract: Artificial intelligence (AI) has been applied to the telecommunications industry for more than a decade. The purpose of this paper is to examine the application of AI in the telecommunications industry sector. Our research finds that AI's first main application in telecommunications is in the network management area. Expert systems and machine learning are the two AI techniques that have been widely used in telecommunications, while machine learning and distributed artificial intelligence are the two AI techniques which are most promising for the future. The research also finds that different AI techniques have their unique applications in the telecommunications industry.     

Interacting meaningfully with machine learning systems: Three experiments

Although machine learning is becoming commonly used in today's software, there has been little research into how end users might interact with machine learning systems, beyond communicating simple “right/wrong” judgments. If the users themselves could work hand-in-hand with machine learning systems, the users’ understanding and trust of the system could improve and the accuracy of learning systems could be improved as well. We conducted three experiments to understand the potential for rich interactions between users and machine learning systems. The first experiment was a think-aloud study that investigated users’ willingness to interact with machine learning reasoning, and what kinds of feedback users might give to machine learning systems. We then investigated the viability of introducing such feedback into machine learning systems, specifically, how to incorporate some of these types of user feedback into machine learning systems, and what their impact was on the accuracy of the system. Taken together, the results of our experiments show that supporting rich interactions between users and machine learning systems is feasible for both user and machine. This shows the potential of rich human–computer collaboration via on-the-spot interactions as a promising direction for machine learning systems and users to collaboratively share intelligence.     

一种基于群等变卷积的度量元学习算法

传统机器学习方法泛化性能不佳,需要通过大规模数据训练才能得到较好的拟合结果,因此不能快速学习训练集外的少量数据,对新种类任务适应性较差,而元学习可实现拥有类似人类学习能力的强人工智能,能够快速适应新的数据集,弥补机器学习的不足。针对传统机器学习中的自适应问题,利用样本图片的局部旋转对称性和镜像对称性,提出一种基于群等变卷积神经网络（G-CNN）的度量元学习算法,以提高特征提取能力。利用G-CNN构建4层特征映射网络,根据样本图片中的局部对称信息,将支持集样本映射到合适的度量空间,并以每类样本在度量空间中的特征平均值作为原型点。同时,通过同样的映射网络将查询机映射到度量空间,根据查询集中样本到原型点的距离完成分类。在Omniglot和miniImageNet数据集上的实验结果表明,该算法相比孪生网络、关系网络、MAML等传统4层元学习算法,在平均识别准确率和模型复杂度方面均具有优势。     

Japanese-to-English translations of tense, aspect, and modality using machine-learning methods and comparison with machine-translation systems on market

This paper describes experiments carried out utilizing a variety of machine-learning methods (the k-nearest neighborhood, decision list, maximum entropy, and support vector machine), and using six machine-translation (MT) systems available on the market for translating tense, aspect, and modality. We found that all these, including the simple string-matching-based k-nearest neighborhood used in a previous study, obtained higher accuracy rates than the MT systems currently available on the market. We also found that the support vector machine obtained the best accuracy rates (98.8%) of these methods. Finally, we analyzed errors against the machine-learning methods and commercially available MT systems and obtained error patterns that should be useful for making future improvements.     

无人系统的视觉感知安全研究

随着现代科技的不断革新,以机器学习尤其是深度学习为代表的人工智能技术正在改变无人系统的发展,推动无人作战等作战形态快速演变,对未来战争带来颠覆性影响。然而由于深度学习的不可解释性、脆弱性等问题,人工智能技术在现实应用中产生了诸多不确定性和安全风险。本文聚焦人工智能技术在军事无人系统中的安全问题,从视觉感知的角度出发,重点分析了安全风险来源、对抗样本理论和视觉感知对抗攻击方法和防御对策,最后对无人系统领域人工智能应用的安全问题进行了总结。     

Comparing and experimenting machine learning techniques for code smell detection

Several code smell detection tools have been developed providing different results, because smells can be subjectively interpreted, and hence detected, in different ways. In this paper, we perform the largest experiment of applying machine learning algorithms to code smells to the best of our knowledge. We experiment 16 different machine-learning algorithms on four code smells (Data Class, Large Class, Feature Envy, Long Method) and 74 software systems, with 1986 manually validated code smell samples. We found that all algorithms achieved high performances in the cross-validation data set, yet the highest performances were obtained by J48 and Random Forest, while the worst performance were achieved by support vector machines. However, the lower prevalence of code smells, i.e., imbalanced data, in the entire data set caused varying performances that need to be addressed in the future studies. We conclude that the application of machine learning to the detection of these code smells can provide high accuracy (>96 %), and only a hundred training examples are needed to reach at least 95 % accuracy.     

Machine learning and deep learning

Electronic Markets - Today, intelligent systems that offer artificial intelligence capabilities often rely on machine learning. Machine learning describes the capacity of systems to learn from...     

针对深度学习模型的对抗性攻击与防御

以深度学习为主要代表的人工智能技术正在悄然改变人们的生产生活方式,但深度学习模型的部署也带来了一定的安全隐患.研究针对深度学习模型的攻防分析基础理论与关键技术,对深刻理解模型内在脆弱性、全面保障智能系统安全性、广泛部署人工智能应用具有重要意义.拟从对抗的角度出发,探讨针对深度学习模型的攻击与防御技术进展和未来挑战.首先介绍了深度学习生命周期不同阶段所面临的安全威胁.然后从对抗性攻击生成机理分析、对抗性攻击生成、对抗攻击的防御策略设计、对抗性攻击与防御框架构建4个方面对现有工作进行系统的总结和归纳.还讨论了现有研究的局限性并提出了针对深度学习模型攻防的基本框架.最后讨论了针对深度学习模型的对抗性攻击与防御未来的研究方向和面临的技术挑战.     

人工智能的逆向工程——反向智能研究综述

在大数据时代,人工智能得到了蓬勃发展,尤其以机器学习、深度学习为代表的技术更是取得了突破性进展.随着人工智能在实际场景中的广泛应用,人工智能的安全和隐私问题也逐渐暴露出来,并吸引了学术界和工业界的广泛关注.以机器学习为代表,许多学者从攻击和防御的角度对模型的安全问题进行了深入的研究,并且提出了一系列的方法.然而,当前对机器学习安全的研究缺少完整的理论架构和系统架构.从训练数据逆向还原、模型结构反向推演、模型缺陷分析等角度进行了总结和分析,建立了反向智能的抽象定义及其分类体系.同时,在反向智能的基础上,将机器学习安全作为应用对其进行简要归纳.最后探讨了反向智能研究当前面临的挑战以及未来的研究方向.建立反向智能的理论体系,对于促进人工智能健康发展极具理论意义.     

基于参数化量子电路的量子卷积神经网络模型及应用

量子神经网络结合了量子计算与经典神经网络模型的各自优势, 为人工智能领域的未来发展提供了一种 全新的思路. 本文提出一种基于参数化量子电路的量子卷积神经网络模型, 能够针对欧几里得结构数据与非欧几里 得结构数据, 利用量子系统的计算优势加速经典机器学习任务. 在MNIST数据集上的数值仿真结果表明, 该模型具 有较强的学习能力和良好的泛化性能.     

Acquisition of Children's Addition Strategies: A Model of Impasse-Free,Knowledge-Level Learning

When children learn to add, they count on their fingers, beginning with the simple SUM strategy and gradually developing the more sophisticated and efficient MIN strategy. The shift from SUM to MIN provides an ideal domain for the study of naturally occurring discovery processes in cognitive skill acquisition. The SUM-to-MIN transition poses a number of challenges for machine-learning systems that would model the phenomenon. First, in addition to the SUM and MIN strategies, Siegler and Jenkins (1989) found that children exhibit two transitional strategies, but not a strategy proposed by an earlier model. Second, they found that children do not invent the MIN strategy in response to impasses, or gaps in their knowledge. Rather, MIN develops spontaneously and gradually replaces earlier strategies. Third, intricate structural differences between the SUM and MIN strategies make it difficult, if not impossible, for standard, symbol-level machine-learning algorithms to model the transition. We present a computer model, called GIPS, that meets these challenges. GIPS combines a relatively simple algorithm for problem solving with a probabilistic learning algorithm that performs symbol-level and knowledge-level learning, both in the presence and absence of impasses. In addition, GIPS makes psychologically plausible demands on local processing and memory. Most importantly, the system successfully models the shift from SUM to MIN, as well as the two transitional strategies found by Siegler and Jenkins.     

Introduction to the special section on learning and security for multi-agent systems

This special section features six articles that provide an overview of the emerging research topics at the intersection learning, security, and multi-agent systems. Recent years have witnessed a surge in the number of works at their intersections, and they have appeared in system and control communities as well as many other communities in artificial intelligence, cyber–physical systems, and economics. The articles in this special section give accessible and comprehensive tutorials and surveys for a broad systems and control audience, covering topics including adversarial machine learning, multi-agent reinforcement learning, cyber resilience, resilient control systems, and game design. It is hopeful that this special section will spawn future interest and cross-disciplinary collaborations in this emerging transdisciplinary research area.     

Implementation of machine-learning classification in remote sensing: an applied review

Machine learning offers the potential for effective and efficient classification of remotely sensed imagery. The strengths of machine learning include the capacity to handle data of high dimensionality and to map classes with very complex characteristics. Nevertheless, implementing a machine-learning classification is not straightforward, and the literature provides conflicting advice regarding many key issues. This article therefore provides an overview of machine learning from an applied perspective. We focus on the relatively mature methods of support vector machines, single decision trees (DTs), Random Forests, boosted DTs, artificial neural networks, and k-nearest neighbours (k-NN). Issues considered include the choice of algorithm, training data requirements, user-defined parameter selection and optimization, feature space impacts and reduction, and computational costs. We illustrate these issues through applying machine-learning classification to two publically available remotely sensed data sets.