欢迎您到威斯康星大学的CSE人工智能网站！ We are active in all major areas of AI, including machine learning, natural language processing, planning, reasoning, robotics, and vision.我们正积极在各主要地区的禽流感，包括机器学习，自然语言处理，规划，推理，机器人技术和视力。 We are ranked in the Top Five departments in AI by US News & World Report. We work closely with the  Database Systems, Information Retrieval, and Intelligent Internet Systems group and the  Computer Graphics, Computer Vision and Animation group, as well as with collaborators throughout UW and at nearby companies like Microsoft, Amazon, Boeing, and Google.我们排在五大部门认可的美国新闻与世界报道。与我们紧密合作，  数据库系统，信息检索，互联网系统及智能组和 计算机图形学，计算机视觉和动画组，以及与整个合作者华盛顿大学和微软这样的公司附近，亚马逊，波音，谷歌。

AI at UW is a growing community with a large number of current projects as well as past successes. These pages give you a sampler of ongoing and past work.威斯康星大学人工智能在过去的成功是一个社会的数目越来越庞大的经常项目与以及。这些网页给你一个采样工作正在进行和过去。 Enjoy!享受！ 

Ubiquitous AI 无所不在的人工智能  Robotics 机器人  Planning 规划  Neural Systems 神经系统  Machine Vision 机器视觉  Machine Learning and Data Mining 机器学习和数据挖掘  Learning Human Behavior 人类行为学 Knowledge Representation and Reasoning 知识表示和推理  Intelligent User Interfaces 智能用户界面  Intelligent Internet Systems 智能网络系统

AI Demos人工智能演示 

 Cross-lingual Image Search 跨语言图片搜索 
 TextRunner TextRunner 
New projects!新项目！ 

 MDP Solvers That Scale MDP的解算器，定额 
 Intelligence in Wikipedia Project 智能工程在维基百科 
 Sensing and Modeling Dynamic Social Networks 传感与社会网络的动态建模 
 ARNAULD: Preference Elicitation For Interface Optimization ARNAULD：偏好接口启发式优化 
 Markov Logic Networks: Probabilistic first-order knowledge bases 马尔可夫逻辑网络：概率的一阶知识基地 
 Planning for Concurrent Durative Uncertain Actions: Augmenting Markov Decision Processes to handle concurrent temporally-extended actions. 规划并行不确定长效措施：增广马尔可夫决策过程在处理并发暂时性的扩大行动。 
 Spectral graph partitioning: Clustering and learning in networks of symmetric and asymmetric relationships. 光谱图的分区：聚类和学习的关系网络中的不对称和对称。 
 KnowItAll: Automated, domain-independent, web-scale information extraction and evaluation. KnowItAll：自动，域名，独立的，网络规模的信息提取和评价。 
 Statistical Relational Learning: Learning from noisy data in rich representations. 统计关系学习：学习丰富的陈述从嘈杂的数据。 
 Collective Knowledge Bases: Merging knowledge from a multitude of sources. 集体知识基础：知识的来源合并多种从。 
 Large-Scale Machine Learning: Mining massive data streams. 大机器学习：挖掘大量数据流。 
 Assisted Cognition: Computer systems to aid people with Alzheimer's disease. 辅助认知：电脑系统，以协助病患者与老年痴呆症。 
 CORE: Optimizing search algorithms using Bayesian models to predict running time. 核心：优化搜索算法使用贝叶斯模型来预测运行时间。 

In the list below current projects are marked with a在低于目前的项目清单标有  while past work is noted with a而过去的工作是一个注意到  . 。

Intelligent Internet Systems  智能网络系统 

 Intelligence in Wikipedia Project 智能工程在维基百科 
 KnowItAll: Automated, domain-independent, web-scale information extraction and evaluation. KnowItAll：自动，域名，独立的，网络规模的信息提取和评价。 
 Opine 觉得 
 Tukwila: Data integration system for heterogeneous data on the web. 塔奇拉：数据网络集成异构系统的数据。 
 Mulder: A natural-language question-answering service that Believes. 马尔德：一种自然语言提问应答服务，如认为。 
 Tiramisu: Declarative Web-site management. 提拉米苏：声明网址管理。 
 The Internet Softbot: The mother of all intelligent internet systems. 互联网Softbot：该系统的所有智能母亲上网。 
 Site popularity meta-search: Re-ranking results of web engines using web page popularity. 网站受欢迎元搜索：结果网页发动机再使用网页受欢迎程度排名。 
 Grouper: Document clustering for improved search results on the web. 石斑鱼：文档的Web聚类结果对改进搜索。 
 ReferralWeb: Explore the social networks that exist on the Web. ReferralWeb：探索网络社会的网络上存在。 

Intelligent and  Personalizable User Interfaces智能化和 个性化用户界面 

 ARNAULD: Preference Elicitation For Interface Optimization ARNAULD：偏好接口启发式优化 
 SUPPLE: Automatic Generation of User Interfaces 柔软：用户界面的自动生成 
 Provably Reliable Question-Answering Interfaces: Natural language interfaces that are guaranteed to answer "easy questions" correctly. 可证明可靠的自动问答接口：自然语言回答接口， 保证 “简单的问题”正确。 
 Adaptive Web sites: Sites that improve their organization by learning from visitor usage. 自适应Web站点：网站访问者使用能够改善他们从组织的学习。 
 Web Site Personalizers: Intermediaries between servers and visitors that automatically adapt and customize content for wireless web visitors. 网站Personalizers：旅客和游客的中介服务器之间自动适应和无线网络定制内容。 
 Programming by Demonstration: Using AI techniques to improve user interfaces. 规划由示范：利用人工智能技术来提高用户界面。 
 Adaptive interfaces for machine learning systems . 自适应学习系统的人机界面 。 

Knowledge Representation and Reasoning知识表示和推理 

 CORE: Optimizing search algorithms using Bayesian models to predict running time. 核心：优化搜索算法使用贝叶斯模型来预测运行时间。 
 Structural Modeling for Anatomy: Representing knowledge about human anatomy. 解剖结构建模：人体解剖代表知识。 
 Walksat: Stochastic local search for satisfiability. Walksat：满足性的随机局部搜索。 

LearningHuman Behavior 人类行为学

 Sensing and Modeling Dynamic Social Networks 传感与社会网络的动态建模 
 Activity Recognition 活动识别 
 Assisted Cognition: Computer systems to aid people with Alzheimer's Disease. 辅助认知：电脑系统，以帮助人们与阿尔茨海默病的。 

MachineLearning and Data Mining 机器学习和数据挖掘

 Machine Learning in Biology 在生物学学习机 
 Markov Logic Networks: Probabilistic first-order knowledge bases 马尔可夫逻辑网络：概率的一阶知识基地 
 Statistical Relational Learning: Learning from noisy data in rich representations 统计关系学习：学习丰富的陈述从嘈杂的数据 
 Collective Knowledge Bases: Merging knowledge from a multitude of sources 集体知识基础：许多知识的来源合并从一 
 Large-Scale Machine Learning: Mining massive data streams 大机器学习：挖掘大量的数据流 
 Belief networks . 信仰网络 。 
 Spectral graph partitioning: Clustering and learning in networks of symmetric and asymmetric relationships 光谱图的分区：聚类和学习的关系网络中的不对称和对称 
 Statistical machine learning . 统计机器学习 。 
 LSD: Learning source descriptions for data integration. 迷幻剂：数据集成学习源的描述。 
 CMM: Converting model ensembles into a single comprehensible model. 三坐标测量机：模式转换成一个单一的套装理解模式。 
 RISE: High-performance concept learner, unifies rule induction and instance-based learning. 崛起：高性能概念学习，统一规则的诱导和基于实例的学习。 
 Naive Bayes . 朴素贝叶斯 。 
 MetaCost: Making error-based learners cost-sensitive. MetaCost：决策错误为基础的学习者成本敏感。 
 Process-Oriented Evaluation: Avoiding overfitting by estimating a hypothesis' generalization error as a function of the search process that led to it. 面向过程的评价：它避免过学习的搜索过程，导致估计一个假设'泛化误差作为一个功能。 

NeuralSystems 神经系统 Machine Vision  机器视觉 

 3D object recognition . 三维物体识别 。 
 Content-based image retrieval . 基于内容的图像检索 。 

Neural Systems  神经系统 

 Probabilistic models of the brain . 大脑的概率模型 。 
 Spike-based computing and learning: For instance, Temporal sequence learning . 穗为基础计算和学习：例如， 时间序列学习 。 
 Learning algorithms for vision: For instance, Invariant coding under image transformations . 对于视觉学习算法：例如， 在图像变换编码不变 。 
 Brain-computer interfaces: EEG-based systems that allow completely paralyzed patients to interact with a computer. 脑机接口：脑波为基础的系统，使完全瘫痪的病人与计算机交互。 

Planning规划
See  this page看到 这个页面 
Robotics  机器人 

 Monte Carlo Localization (MCL): Particle filters for state estimation in mobile robotics. 蒙特卡洛定位（韧带）：粒子滤波器的移动机器人在状态估计。 
 Multirobot systems: Navigation and coordination of multiple robots. 多机器人系统：导航及多机器人协调。 
 Mobile robot control: Probabilistic techniques that can handle position and sensor uncertainty. 移动机器人控制：概率技术，可处理的位置和传感器的不确定性。 
 Museum tour-guides: Rhino and Minerva guide visitors through crowded museums. 参观博物馆导游：犀牛和Minerva引导游客穿过拥挤的博物馆。 

UbiquitousAI 无所不在的人工智能 

 Assisted Cognition: Computer systems to aid people with Alzheimer's Disease. 辅助认知：电脑系统，以帮助人们与阿尔茨海默病的。