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欧盟人工智能法案中英文对照全套EU Artificial Intelligence Legislation Full Set ComparisonIntroductionThe European Union has recently introduced a comprehensive set of laws and regulations regarding artificial intelligence technology. The goal of these laws is to ensure the ethical and responsible development and use of AI across the EU member states. In this document, we will provide a full set comparison between the various legislations that make up the EU's AI regulations.DefinitionsBefore we delve into the specifics of the laws, it is important to establish some key definitions that are common across the various legislations:1. Artificial Intelligence (AI): Any software-based system or technology that exhibits behavior comparable to human cognitive abilities, such as reasoning, learning, problem-solving, perception, and/or interaction with the environment.2. High-Risk AI: AI systems that present a high risk to the health and safety, fundamental rights, or livelihoods ofindividuals. These systems are subject to stricter regulations and oversight.3. Biometric Data: Any information that relates to the physical, physiological, or behavioral characteristics of an individual, which allows for their unique identification.Now let's examine the key components of the EU's AI legislation:1. AI ActThe AI Act is the central piece of legislation that governs AI technology within the EU. It sets out the requirements for the development, deployment, and use of AI systems across various sectors. Some key provisions of the AI Act include:- Transparency: AI systems must be designed and developed in a transparent manner, with clear documentation of their operation and function.- Accountability: Developers and users of AI systems are held accountable for any harm caused by these systems.- Biometric Data: The use of biometric data in AI systems is strictly regulated, with explicit consent required for its collection and processing.2. Data Governance ActThe Data Governance Act complements the AI Act by establishing rules for the sharing and use of data in AI systems. Key provisions of the Data Governance Act include:- Data Sharing: Organizations are required to share data with other entities under certain conditions, to promote collaboration and innovation in AI development.- Data Protection: Data used in AI systems must be protected from unauthorized access or misuse, to ensure the privacy and security of individuals' data.- Data Portability: Individuals have the right to request the transfer of their data between different service providers, to facilitate competition and innovation.3. Data ActThe Data Act focuses specifically on the collection, processing, and use of personal data in AI systems. Key provisions of the Data Act include:- Consent: Individuals must provide explicit consent for the use of their personal data in AI systems, and have the right to withdraw this consent at any time.- Data Minimization: Organizations are required to limit the collection and processing of personal data to what is strictly necessary for the operation of AI systems.- Data Security: Measures must be taken to ensure the security and integrity of personal data in AI systems, to prevent unauthorized access or breaches.ConclusionThe EU's artificial intelligence legislation represents a significant step towards ensuring the ethical and responsible development and use of AI technology within the European Union. By establishing clear rules and standards for the operation of AI systems, these laws aim to protect the rights and well-being of individuals, while promoting innovation and competitiveness in the AI sector. It is crucial for organizations and developers to adhere to these regulations and uphold the principles of transparency, accountability, and data protection in all AI-related activities.。
I. IntroductionThe European Union (EU) has been at the forefront of regulatory innovation in the field of artificial intelligence (AI). The EUArtificial Intelligence Act (AI Act) is a landmark piece of legislation designed to ensure the safe, ethical, and sustainable development and deployment of AI across the EU. This act aims to create a harmonized regulatory framework that protects individuals, fosters innovation, and promotes trust in AI technologies.II. Scope and ObjectivesThe AI Act applies to AI systems deployed in the EU, regardless of where they are developed. It sets out a comprehensive set of rules and standards for AI systems, categorizing them into six risk levels based on their potential impact on human life and safety.The objectives of the AI Act are as follows:1. Ensure high levels of safety and trust in AI systems: The act establishes strict safety and transparency requirements for AI systems, particularly those with a high risk level.2. Promote innovation and competitiveness: The act aims to create alevel playing field for AI developers and businesses in the EU, while also encouraging the development of new AI technologies.3. Protect fundamental rights: The act includes provisions to protect individuals' fundamental rights, such as privacy, freedom of expression, and non-discrimination.4. Enhance consumer protection: The act provides clear guidelines for the marketing and labeling of AI products and services, ensuring that consumers are informed about the AI technologies they use.5. Facilitate cross-border cooperation: The act promotes the exchange of information and best practices among EU member states, fostering a coordinated approach to AI regulation.III. AI Systems and Risk CategoriesThe AI Act categorizes AI systems into six risk levels based on their potential impact on human life and safety:1. High Risk: These AI systems can pose a high risk to human life and safety, including those used in critical infrastructure, transport, healthcare, and law enforcement. Examples include autonomous vehicles, drones, and medical devices.2. Substantial Risk: These AI systems can pose a substantial risk to human life and safety, including those used in employment, education, and credit scoring. Examples include biometric identification systems and predictive maintenance systems.3. Limited Risk: These AI systems have a lower risk to human life and safety, including those used in leisure and entertainment, customer service, and marketing. Examples include chatbots and recommendation systems.4. Low Risk: These AI systems have a very low risk to human life and safety, including those used in administrative tasks, management, and control of non-essential processes. Examples include automated data processing and monitoring systems.5. No Risk: These AI systems have no risk to human life and safety, including those used for administrative purposes and research. Examples include automated data analysis and pattern recognition systems.6. Unspecified Risk: These AI systems fall outside the scope of the AI Act due to their lack of a clear impact on human life and safety. Examples include AI systems used for personal entertainment or hobbies.IV. Key Provisions of the AI ActThe AI Act includes several key provisions to ensure the safe and ethical use of AI:1. Framework for AI Risk Management: The act requires AI developers to conduct a risk assessment and implement appropriate risk management measures for AI systems. This includes identifying potential risks,evaluating their impact, and implementing measures to mitigate those risks.2. Transparency and Explainability: The act requires AI systems to be transparent and explainable, particularly those with a high risk level. This means that users should be able to understand how the AI system works and how it makes decisions.3. Data Quality and Privacy: The act sets out rules for the collection, processing, and storage of data used in AI systems. It also includes provisions to protect individuals' privacy and ensure the security of personal data.4. Human Oversight: The act requires AI systems to be designed and operated in a manner that allows for human oversight, particularly for high-risk AI systems.5. Labeling and Marketing: The act includes provisions for the labeling and marketing of AI products and services, ensuring that consumers are informed about the AI technologies they use.6. Liability: The act establishes rules for determining liability in the event of harm caused by AI systems.V. Implementation and EnforcementThe AI Act will be implemented through a combination of EU regulations and directives. EU institutions, such as the European Commission and the European Parliament, will be responsible for drafting and adopting the necessary regulations. National authorities in each EU member state will be responsible for enforcing the act.VI. ConclusionThe EU Artificial Intelligence Act represents a significant step forward in the regulation of AI technologies. By establishing a comprehensive and harmonized regulatory framework, the AI Act aims to ensure that AIis developed and used in a manner that is safe, ethical, and beneficial to society. As AI continues to evolve, the AI Act will serve as a model for other jurisdictions around the world.---欧盟人工智能法案：全面概述I. 引言欧洲联盟（EU）在人工智能（AI）领域的监管创新一直处于前沿。
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评估术语表►AAA:美国估价师协会►AACI:（加拿大评估协会）加拿大注册估价师►ABAR:（美国企业估价师协会）商业价值评估审查►ABV: (美国注册会计师协会)商业价值评估资格►AI:美国评估协会►AIC:加拿大评估协会►AICPA: 美国注册会计师协会►AITF:评估问题专责小组►AM: (美国评估师协会) 注册会员►AMC:评估管理公司►APB:（美国资产评估基金会）评估实务委员会►AQB:（美国资产评估基金会）估价师资格委员会►ARM: (美国评估师协会) 评估审查与管理认证►ASA:美国评估师协会/注册高级评估师►ASA BVS:美国评估师协会企业价值评估规范/企业评估准则►ASB:（美国资产评估基金会）评估标准委员会►ASFMRA:美国农场主评估协会►AVA:（美国企业价值评估分析师协会）认证价值分析师►AVM: 自动估价模型►BPO: 经纪人估值意见/经纪人价格意见►BVAL:（美国企业估价师协会）法律诉讼评估师►BVC:美国评估师协会企业估价委员会►CBA: (美国企业估价师协会) 注册企业价值评估师►CBV: 特许企业价值评估师►CFA: (特许金融分析师协会) 特许金融分析师►CFAI: 特许金融分析师协会►CICBV:加拿大特许企业价值评估师协会►CRA:（加拿大评估协会）住宅估价师►CVA:（全国企业价值评估分析师协会）注册价值分析师►FASA:（美国评估师协会）高级会员/荣誉会员►FCBV: （加拿大特许企业价值评估师协会）高级会员►FRICS: 英国皇家特许测量师学会资深会员►IAAO: 国际估税官协会►IACVA: 国际企业价值评估分析师协会/国际注册价值分析师协会►IBA:商业评估师协会/美国企业估价师协会►IEPR:波多黎各评估师协会►IIBV:国际企业价值评估学会►IRWA: 国际通行权协会►IVS: 国际评估准则►IVSC: 国际评估准则理事会►MAI: 美国评估协会会员►MBREA: 马萨诸塞州房地产估价师►MCBA:（美国企业估价师协会）高级注册评估师资格►MRICS:英国皇家特许测量师学会会员►NACVA:美国企业价值评估分析师协会/注册价值分析师全国联盟►NAIFA: 美国独立收费评估师协会►RICS: 英国皇家特许测量师学会►RICS RV:英国皇家特许测量师学会注册估价师►SRA:（美国评估协会）高级住宅估价师►SSVS:（美国注册会计师协会）企业价值评估准则►TAF:美国资产评估基金会/美国评估促进协会►USPAP: 专业估价操作统一标准►VRG:美国财务会计准则委员会美国财务会计准则委员会►VSC BVC:（美国评估师协会）商业评估委员会之下的估价标准委员会。
AI（人工智能）术语中英文对照表缩写英语汉语A网课代上Activation Function激活函数Adversarial Networks对抗网络Affine Layer仿射层agent代理/智能体algorithm算法alpha-beta pruningα-β剪枝anomaly detection异常检测approximation近似AGI Artificial General Intelligence通用人工智能AI Artificial Intelligence人工智能association analysis关联分析attention mechanism注意机制autoencoder自编码器ASR automatic speech recognition自动语音识别automatic summarization自动摘要average gradient平均梯度Average-Pooling平均池化BBP backpropagation反向传播BPTT Backpropagation Through Time 通过时间的反向传播BN Batch Normalization分批标准化Bayesian network贝叶斯网络Bias-Variance Dilemma偏差/方差困境Bi-LSTM Bi-directional Long-Short TermMemory双向长短期记忆bias偏置/偏差big data大数据Boltzmann machine玻尔兹曼机CCPU Central Processing Unit中央处理器chunk词块clustering聚类cluster analysis聚类分析co-adapting共适应co-occurrence共现Computation Cost计算成本Computational Linguistics计算语言学computer vision计算机视觉concept drift概念漂移CRF conditional random field条件随机域/场convergence收敛CA conversational agent会话代理convexity凸性CNN convolutional neural network卷积神经网络Cost Function成本函数cross entropy交叉熵DDecision Boundary决策边界Decision Trees决策树DBN Deep Belief Network深度信念网络DCGAN Deep Convolutional GenerativeAdversarial Network深度卷积生成对抗网络DL deep learning深度学习DNN deep neural network深度神经网络Deep Q-Learning深度Q学习DQN Deep Q-Network深度Q网络DNC differentiable neural computer可微分神经计算机dimensionality reduction algorithm降维算法discriminative model判别模型discriminator判别器divergence散度domain adaption领域自适应DropoutDynamic Fusion动态融合EEmbedding嵌入emotional analysis情绪分析End-to-End端到端EM Expectation-Maximization期望最大化Exploding GradientProblem梯度爆炸问题ELM Extreme Learning Machine超限学习机FFAIR Facebook Artificial IntelligenceResearchFacebook人工智能研究所factorization因子分解feature engineering特征工程Featured Learning特征学习Feedforward Neural Networks前馈神经网络Ggame theory博弈论GMM Gaussian Mixture Model高斯混合模型GA Genetic Algorithm遗传算法Generalization泛化GAN Generative Adversarial Networks生成对抗网络Generative Model生成模型Generator生成器Global Optimization全局优化GNMT Google Neural Machine Translation谷歌神经机器翻译Gradient Descent梯度下降graph theory图论GPU graphics processing unit 图形处理单元/图形处理器HHDM hidden dynamic model隐动态模型hidden layer隐藏层HMM Hidden Markov Model隐马尔可夫模型hybrid computing混合计算hyperparameter超参数IICA Independent Component Analysis独立成分分析input输入ICML International Conference for Machine Learning国际机器学习大会language phenomena语言现象latent dirichlet allocation隐含狄利克雷分布JJSD Jensen-Shannon Divergence JS距离KK-Means Clustering K-均值聚类K-NN K-Nearest Neighbours AlgorithmK-最近邻算法Knowledge Representation知识表征KB knowledge base知识库LLatent Dirichlet Allocation隐狄利克雷分布LSA latent semantic analysis潜在语义分析learner学习器Linear Regression线性回归log likelihood对数似然Logistic Regression Logistic回归LSTM Long-Short Term Memory长短期记忆loss损失MMT machine translation机器翻译Max-Pooling最大池化Maximum Likelihood最大似然minimax game最小最大博弈Momentum动量MLP Multilayer Perceptron多层感知器multi-document summarization多文档摘要MLP multi layered perceptron多层感知器multimodal learning多模态学习multiple linear regression多元线性回归NNaive Bayes Classifier朴素贝叶斯分类器named entity recognition命名实体识别Nash equilibrium纳什均衡NLG natural language generation自然语言生成NLP natural language processing自然语言处理NLL Negative Log Likelihood负对数似然NMT Neural Machine Translation神经机器翻译NTM Neural Turing Machine神经图灵机NCE noise-contrastive estimation噪音对比估计non-convex optimization非凸优化non-negative matrix factorization非负矩阵分解Non-Saturating Game非饱和博弈Oobjective function目标函数Off-Policy离策略On-Policy在策略one shot learning一次性学习output输出PParameter参数parse tree解析树part-of-speech tagging词性标注PSO Particle Swarm Optimization粒子群优化算法perceptron感知器polarity detection极性检测pooling池化PPGN Plug and Play Generative Network即插即用生成网络PCA principal component analysis主成分分析Probability Graphical Model概率图模型QQNN Quantized Neural Network量子化神经网络quantum computer量子计算机Quantum Computing量子计算RRBF Radial Basis Function径向基函数Random Forest Algorithm随机森林算法ReLU Rectified Linear Unit 线性修正单元/线性修正函数RNN Recurrent Neural Network循环神经网络recursive neural network递归神经网络RL reinforcement learning强化学习representation表征representation learning表征学习Residual Mapping残差映射Residual Network残差网络RBM Restricted Boltzmann Machine受限玻尔兹曼机Robot机器人Robustness稳健性RE Rule Engine规则引擎Ssaddle point鞍点Self-Driving自动驾驶SOM self organised map自组织映射Semi-Supervised Learning半监督学习sentiment analysis情感分析SLAM simultaneous localization and mapping同步定位与地图构建SVD Singular Value Decomposition奇异值分解Spectral Clustering谱聚类Speech Recognition语音识别SGD stochastic gradient descent随机梯度下降supervised learning监督学习SVM Support Vector Machine支持向量机synset同义词集Tt-SNE T-Distribution Stochastic Neighbour EmbeddingT-分布随机近邻嵌入tensor张量TPU Tensor Processing Units张量处理单元the least square method最小二乘法Threshold阙值Time Step时间步骤tokenization标记化treebank树库transfer learning迁移学习Turing Machine图灵机Uunsupervised learning无监督学习VVanishing Gradient Problem梯度消失问题VC Theory Vapnik–Chervonenkis theory 万普尼克-泽范兰杰斯理论von Neumann architecture 冯·诺伊曼架构/结构WWGAN Wasserstein GANW weight权重word embedding词嵌入WSD word sense disambiguation词义消歧XYZZSL zero-shot learning零次学习zero-data learning零数据学习。
人工智能概论中英文术语对照表动作action专家系统Expert system人工智能语言AI language祖先过滤形策略ancestry-filtered form strategy与节点AND node与或图AND/OR graph与或树AND/OR tree回答语句answer statement人工智能artificial intelligence,AI原子公式atomic formula自动定理证明automatic theorem provingB规则B-rule倒退值backed-up value回溯backtracking盲目搜索，无信息搜索blind search宽度优先搜索breadth-first search子句clause组合爆炸combinatorial explosion冲突解决conflict resolution合取式conjunct合取conjunction合取范式conjunctive normal form连词，连接词connective一致解图consistant solution graph控制策略control strategy费用cost演绎deduction深度优先搜索depth-first search推导表，引导图derivation graph差别difference有向图directed graph析取式disjunct析取disjunction谓词演算辖域domain in predicate calculus论域，文字域domain of discourse搜索算法的效率efficiency of search algorithm空子句empty clause等价equivalence估计费用estimated cost估值函数evaluation function存在量词existential quantifier扩展节点expending node节点的扩展expansion of nodeF规则F-rule事实fact一阶谓词演算first order predicate calculus 博弈game图graph图表示法graph notation图搜索graph search图搜索控制策略graph-search control strategy 启发函数heuristic function启发信息heuristic information启发搜索heuristic search蕴涵，蕴涵式implication推理inference智能intelligence解释器interpreter知识knowledge知识获取knowledge acquisition全局数据库Global database知识库knowledge base知识工程knowledge engineering学习learning启发式搜索Heuristic search线形输入形策略linear-input form strategy文字literal逻辑logic逻辑连词logic connective逻辑推理logic reasoning匹配match模式匹配match pattern母式matrix最一般合一者most general unifierNP完全问题NP-complete problem算符、算子、操作符operator最优解树optimal solution tree有序搜索ordered search谓词predicate谓词演算predicate calculus谓词逻辑predicate logic前缀prefix本原问题primitive problem问题归约problem-reduction问题求解problem solving产生式production产生式规则production rule量词quantifier推理reasoning正向推理forward reasoning逆向推理backward reasoning推理机reasoning machine归约reduction反演refutation反演树refutation tree归结resolution归结原理resolution principle归结反演resolution refutation归结式resolvent可满足性satisfiability模式识别Pattern recognition量词辖域scope of quantifier搜索search, searching搜索算法searching algorithm搜索图searching graph搜索策略searching strategy搜索树searching tree句子sentence解图solution graph解树solution tree可解节点solvable node可解标示过程solvable labeling procedure 状态state状态空间state space代换例substitution instance代换substitution重言式tautology项term定理证明theorem-proving不确定性uncertainty合一unifier最一般合一most general unifier全称量词universal quantifier不可满足集unsatisfiable set不可解标示过程unsolvable-labeling procedure 不可解节点unsolvable node永真式validity合适公式、合式公式well-formed formula (wff)谓词演算公式wffs of predicate calculus人工神经网络artificial neural network遗传算法genetic algorithm机器学习machine learning。
人工智能英汉Aβα-Pruning, βα-剪枝, (2) Acceleration Coefficient, 加速系数, (8) Activation Function, 激活函数, (4) Adaptive Linear Neuron, 自适应线性神经元,(4)Adenine, 腺嘌呤, (11)Agent, 智能体, (6)Agent Communication Language, 智能体通信语言, (11)Agent-Oriented Programming, 面向智能体的程序设计, (6)Agglomerative Hierarchical Clustering, 凝聚层次聚类, (5)Analogism, 类比推理, (5)And/Or Graph, 与或图, (2)Ant Colony Optimization (ACO), 蚁群优化算法, (8)Ant Colony System (ACS), 蚁群系统, (8) Ant-Cycle Model, 蚁周模型, (8)Ant-Density Model, 蚁密模型, (8)Ant-Quantity Model, 蚁量模型, (8)Ant Systems, 蚂蚁系统, (8)Applied Artificial Intelligence, 应用人工智能, (1)Approximate Nondeterministic Tree Search (ANTS), 近似非确定树搜索, (8) Artificial Ant, 人工蚂蚁, (8)Artificial Intelligence (AI), 人工智能, (1) Artificial Neural Network (ANN), 人工神经网络, (1), (3)Artificial Neural System, 人工神经系统，(3) Artificial Neuron, 人工神经元, (3) Associative Memory, 联想记忆, (4) Asynchronous Mode, 异步模式, (4) Attractor, 吸引子, (4)Automatic Theorem Proving, 自动定理证明,(1)Automatic Programming, 自动程序设计, (1) Average Reward, 平均收益, (6) Axon, 轴突, (4)Axon Hillock, 轴突丘, (4)BBackward Chain Reasoning, 逆向推理, (3) Bayesian Belief Network, 贝叶斯信念网, (5) Bayesian Decision, 贝叶斯决策, (3) Bayesian Learning, 贝叶斯学习, (5) Bayesian Network贝叶斯网, (5)Bayesian Rule, 贝叶斯规则, (3)Bayesian Statistics, 贝叶斯统计学, (3) Biconditional, 双条件, (3)Bi-Directional Reasoning, 双向推理, (3) Biological Neuron, 生物神经元, (4) Biological Neural System, 生物神经系统, (4) Blackboard System, 黑板系统, (8)Blind Search, 盲目搜索, (2)Boltzmann Machine, 波尔兹曼机, (3) Boltzmann-Gibbs Distribution, 波尔兹曼-吉布斯分布, (3)Bottom-Up, 自下而上, (4)Building Block Hypotheses, 构造块假说, (7) CCell Body, 细胞体, (3)Cell Membrane, 细胞膜, (3)Cell Nucleus, 细胞核, (3)Certainty Factor, 可信度, (3)Child Machine, 婴儿机器, (1)Chinese Room, 中文屋, (1) Chromosome, 染色体, (6)Class-conditional Probability, 类条件概率,(3), (5)Classifier System, 分类系统, (6)Clause, 子句, (3)Cluster, 簇, (5)Clustering Analysis, 聚类分析, (5) Cognitive Science, 认知科学, (1) Combination Function, 整合函数, (4) Combinatorial Optimization, 组合优化, (2) Competitive Learning, 竞争学习, (4) Complementary Base, 互补碱基, (11) Computer Games, 计算机博弈, (1) Computer Vision, 计算机视觉, (1)Conflict Resolution, 冲突消解, (3) Conjunction, 合取, (3)Conjunctive Normal Form (CNF), 合取范式,(3)Collapse, 坍缩, (11)Connectionism, 连接主义, (3) Connective, 连接词, (3)Content Addressable Memory, 联想记忆, (4) Control Policy, 控制策略, (6)Crossover, 交叉, (7)Cytosine, 胞嘧啶, (11)DData Mining, 数据挖掘, (1)Decision Tree, 决策树, (5) Decoherence, 消相干, (11)Deduction, 演绎, (3)Default Reasoning, 默认推理（缺省推理）,(3)Defining Length, 定义长度, (7)Rule (Delta Rule), 德尔塔规则, 18(3) Deliberative Agent, 慎思型智能体, (6) Dempster-Shafer Theory, 证据理论, (3) Dendrites, 树突, (4)Deoxyribonucleic Acid (DNA), 脱氧核糖核酸, (6), (11)Disjunction, 析取, (3)Distributed Artificial Intelligence (DAI), 分布式人工智能, (1)Distributed Expert Systems, 分布式专家系统,(9)Divisive Hierarchical Clustering, 分裂层次聚类, (5)DNA Computer, DNA计算机, (11)DNA Computing, DNA计算, (11) Discounted Cumulative Reward, 累计折扣收益, (6)Domain Expert, 领域专家, (10) Dominance Operation, 显性操作, (7) Double Helix, 双螺旋结构, (11)Dynamical Network, 动态网络, (3)E8-Puzzle Problem, 八数码问题, (2) Eletro-Optical Hybrid Computer, 光电混合机, (11)Elitist strategy for ant systems (EAS), 精化蚂蚁系统, (8)Energy Function, 能量函数, (3) Entailment, 永真蕴含, (3) Entanglement, 纠缠, (11)Entropy, 熵, (5)Equivalence, 等价式, (3)Error Back-Propagation, 误差反向传播, (4) Evaluation Function, 评估函数, (6) Evidence Theory, 证据理论, (3) Evolution, 进化, (7)Evolution Strategies (ES), 进化策略, (7) Evolutionary Algorithms (EA), 进化算法, (7) Evolutionary Computation (EC), 进化计算,(7)Evolutionary Programming (EP), 进化规划,(7)Existential Quantification, 存在量词, (3) Expert System, 专家系统, (1)Expert System Shell, 专家系统外壳, (9) Explanation-Based Learning, 解释学习, (5) Explanation Facility, 解释机构, (9)FFactoring, 因子分解, (11)Feedback Network, 反馈型网络, (4) Feedforward Network, 前馈型网络, (1) Feasible Solution, 可行解, (2)Finite Horizon Reward, 横向有限收益, (6) First-order Logic, 一阶谓词逻辑, (3) Fitness, 适应度, (7)Forward Chain Reasoning, 正向推理, (3) Frame Problem, 框架问题, (1)Framework Theory, 框架理论, (3)Free-Space Optical Interconnect, 自由空间光互连, (11)Fuzziness, 模糊性, (3)Fuzzy Logic, 模糊逻辑, (3)Fuzzy Reasoning, 模糊推理, (3)Fuzzy Relation, 模糊关系, (3)Fuzzy Set, 模糊集, (3)GGame Theory, 博弈论, (8)Gene, 基因, (7)Generation, 代, (6)Genetic Algorithms, 遗传算法, (7)Genetic Programming, 遗传规划（遗传编程）,(7)Global Search, 全局搜索, (2)Gradient Descent, 梯度下降, (4)Graph Search, 图搜索, (2)Group Rationality, 群体理性, (8) Guanine, 鸟嘌呤, (11)HHanoi Problem, 梵塔问题, (2)Hebbrian Learning, 赫伯学习, (4)Heuristic Information, 启发式信息, (2) Heuristic Search, 启发式搜索, (2)Hidden Layer, 隐含层, (4)Hierarchical Clustering, 层次聚类, (5) Holographic Memory, 全息存储, (11) Hopfield Network, 霍普菲尔德网络, (4) Hybrid Agent, 混合型智能体, (6)Hype-Cube Framework, 超立方体框架, (8)IImplication, 蕴含, (3)Implicit Parallelism, 隐并行性, (7) Individual, 个体, (6)Individual Rationality, 个体理性, (8) Induction, 归纳, (3)Inductive Learning, 归纳学习, (5) Inference Engine, 推理机, (9)Information Gain, 信息增益, (3)Input Layer, 输入层, (4)Interpolation, 插值, (4)Intelligence, 智能, (1)Intelligent Control, 智能控制, (1) Intelligent Decision Supporting System (IDSS), 智能决策支持系统，(1) Inversion Operation, 倒位操作, (7)JJoint Probability Distribution, 联合概率分布,(5) KK-means, K-均值, (5)K-medoids, K-中心点, (3)Knowledge, 知识, (3)Knowledge Acquisition, 知识获取, (9) Knowledge Base, 知识库, (9)Knowledge Discovery, 知识发现, (1) Knowledge Engineering, 知识工程, (1) Knowledge Engineer, 知识工程师, (9) Knowledge Engineering Language, 知识工程语言, (9)Knowledge Interchange Format (KIF), 知识交换格式, (8)Knowledge Query and ManipulationLanguage (KQML), 知识查询与操纵语言，(8)Knowledge Representation, 知识表示, (3)LLearning, 学习, (3)Learning by Analog, 类比学习, (5) Learning Factor, 学习因子, (8)Learning from Instruction, 指导式学习, (5) Learning Rate, 学习率, (6)Least Mean Squared (LSM), 最小均方误差,(4)Linear Function, 线性函数, (3)List Processing Language (LISP), 表处理语言, (10)Literal, 文字, (3)Local Search, 局部搜索, (2)Logic, 逻辑, (3)Lyapunov Theorem, 李亚普罗夫定理, (4) Lyapunov Function, 李亚普罗夫函数, (4)MMachine Learning, 机器学习, (1), (5) Markov Decision Process (MDP), 马尔科夫决策过程, (6)Markov Chain Model, 马尔科夫链模型, (7) Maximum A Posteriori (MAP), 极大后验概率估计, (5)Maxmin Search, 极大极小搜索, (2)MAX-MIN Ant Systems (MMAS), 最大最小蚂蚁系统, (8)Membership, 隶属度, (3)Membership Function, 隶属函数, (3) Metaheuristic Search, 元启发式搜索, (2) Metagame Theory, 元博弈理论, (8) Mexican Hat Function, 墨西哥草帽函数, (4) Migration Operation, 迁移操作, (7) Minimum Description Length (MDL), 最小描述长度, (5)Minimum Squared Error (MSE), 最小二乘法,(4)Mobile Agent, 移动智能体, (6)Model-based Methods, 基于模型的方法, (6) Model-free Methods, 模型无关方法, (6) Modern Heuristic Search, 现代启发式搜索,(2)Monotonic Reasoning, 单调推理, (3)Most General Unification (MGU), 最一般合一, (3)Multi-Agent Systems, 多智能体系统, (8) Multi-Layer Perceptron, 多层感知器, (4) Mutation, 突变, (6)Myelin Sheath, 髓鞘, (4)(μ+1)-ES, (μ+1) -进化规划, (7)(μ+λ)-ES, (μ+λ) -进化规划, (7) (μ,λ)-ES, (μ,λ) -进化规划, (7)NNaïve Bayesian Classifiers, 朴素贝叶斯分类器, (5)Natural Deduction, 自然演绎推理, (3) Natural Language Processing, 自然语言处理,(1)Negation, 否定, (3)Network Architecture, 网络结构, (6)Neural Cell, 神经细胞, (4)Neural Optimization, 神经优化, (4) Neuron, 神经元, (4)Neuron Computing, 神经计算, (4)Neuron Computation, 神经计算, (4)Neuron Computer, 神经计算机, (4) Niche Operation, 生态操作, (7) Nitrogenous base, 碱基, (11)Non-Linear Dynamical System, 非线性动力系统, (4)Non-Monotonic Reasoning, 非单调推理, (3) Nouvelle Artificial Intelligence, 行为智能,(6)OOccam’s Razor, 奥坎姆剃刀, (5)(1+1)-ES, (1+1) -进化规划, (7)Optical Computation, 光计算, (11)Optical Computing, 光计算, (11)Optical Computer, 光计算机, (11)Optical Fiber, 光纤, (11)Optical Waveguide, 光波导, (11)Optical Interconnect, 光互连, (11) Optimization, 优化, (2)Optimal Solution, 最优解, (2)Orthogonal Sum, 正交和, (3)Output Layer, 输出层, (4)Outer Product, 外积法, 23(4)PPanmictic Recombination, 混杂重组, (7) Particle, 粒子, (8)Particle Swarm, 粒子群, (8)Particle Swarm Optimization (PSO), 粒子群优化算法, (8)Partition Clustering, 划分聚类, (5) Partitioning Around Medoids, K-中心点, (3) Pattern Recognition, 模式识别, (1) Perceptron, 感知器, (4)Pheromone, 信息素, (8)Physical Symbol System Hypothesis, 物理符号系统假设, (1)Plausibility Function, 不可驳斥函数（似然函数）, (3)Population, 物种群体, (6)Posterior Probability, 后验概率, (3)Priori Probability, 先验概率, (3), (5) Probability, 随机性, (3)Probabilistic Reasoning, 概率推理, (3) Probability Assignment Function, 概率分配函数, (3)Problem Solving, 问题求解, (2)Problem Reduction, 问题归约, (2)Problem Decomposition, 问题分解, (2) Problem Transformation, 问题变换, (2) Product Rule, 产生式规则, (3)Product System, 产生式系统, (3) Programming in Logic (PROLOG), 逻辑编程, (10)Proposition, 命题, (3)Propositional Logic, 命题逻辑, (3)Pure Optical Computer, 全光计算机, (11)QQ-Function, Q-函数, (6)Q-learning, Q-学习, (6)Quantifier, 量词, (3)Quantum Circuit, 量子电路, (11)Quantum Fourier Transform, 量子傅立叶变换, (11)Quantum Gate, 量子门, (11)Quantum Mechanics, 量子力学, (11) Quantum Parallelism, 量子并行性, (11) Qubit, 量子比特, (11)RRadial Basis Function (RBF), 径向基函数,(4)Rank based ant systems (ASrank), 基于排列的蚂蚁系统, (8)Reactive Agent, 反应型智能体, (6) Recombination, 重组, (6)Recurrent Network, 循环网络, (3) Reinforcement Learning, 强化学习, (3) Resolution, 归结, (3)Resolution Proof, 归结反演, (3) Resolution Strategy, 归结策略, (3) Reasoning, 推理, (3)Reward Function, 奖励函数, (6) Robotics, 机器人学, (1)Rote Learning, 机械式学习, (5)SSchema Theorem, 模板定理, (6) Search, 搜索, (2)Selection, 选择, (7)Self-organizing Maps, 自组织特征映射, (4) Semantic Network, 语义网络, (3)Sexual Differentiation, 性别区分, (7) Shor’s algorithm, 绍尔算法, (11)Sigmoid Function, Sigmoid 函数（S型函数）,(4)Signal Function, 信号函数, (3)Situated Artificial Intelligence, 现场式人工智能, (1)Spatial Light Modulator (SLM), 空间光调制器, (11)Speech Act Theory, 言语行为理论, (8) Stable State, 稳定状态, (4)Stability Analysis, 稳定性分析, (4)State Space, 状态空间, (2)State Transfer Function, 状态转移函数,(6)Substitution, 置换, (3)Stochastic Learning, 随机型学习, (4) Strong Artificial Intelligence (AI), 强人工智能, (1)Subsumption Architecture, 包容结构, (6) Superposition, 叠加, (11)Supervised Learning, 监督学习, (4), (5) Swarm Intelligence, 群智能, (8)Symbolic Artificial Intelligence (AI), 符号式人工智能（符号主义）, (3) Synapse, 突触, (4)Synaptic Terminals, 突触末梢, (4) Synchronous Mode, 同步模式, (4)TThreshold, 阈值, (4)Threshold Function, 阈值函数, (4) Thymine, 胸腺嘧啶, (11)Topological Structure, 拓扑结构, (4)Top-Down, 自上而下, (4)Transfer Function, 转移函数, (4)Travel Salesman Problem, 旅行商问题, (4) Turing Test, 图灵测试, (1)UUncertain Reasoning, 不确定性推理, (3)Uncertainty, 不确定性, (3)Unification, 合一, (3)Universal Quantification, 全称量词, (4) Unsupervised Learning, 非监督学习, (4), (5)WWeak Artificial Intelligence (Weak AI), 弱人工智能, (1)Weight, 权值, (4)Widrow-Hoff Rule, 维德诺-霍夫规则, (4)。
