CISE Research Instrumentation: Active Learning for Text, Scene, and Biosequence Analysis

9617307加里森·W·查尔斯·埃尔坎，加州大学圣地亚哥分校Cise研究仪器：文本、场景和生物序列分析的主动学习通过自主学习适应不断变化的环境和不断变化的目标是加州大学圣迭戈分校人工智能实验室研究的中心主题。通过这笔赠款获得的工作站、机器人和神经网络硬件将使最近开发的学习方法能够在四个大规模的现实世界应用程序中进行测试。-信息检索的学习语义表示-嵌入式虚拟代理-Meme：一种用于序列分析的新软件工具-嵌入式代理中的实时人脸和对象识别。第一个子项目将在全国TREC竞赛中应用新的算法来重新呈现文件并响应人类对千兆字节文本的反馈。另一个项目将提供给全球的网络软件代理，他们现在在模拟的人工生活环境中学习和互动。第三个项目将把在DNA和蛋白质序列中寻找模式的新软件扩大到能够自动分析整个基因组的工具。最后，第四个项目将通过在带有可控摄像头的移动机器人上实施这些方法来验证用于识别和跟踪目标的新的主动视觉方法。

9617307 Cottrell, Garrison W. Charles Elkan, University of California, San Diego CISE Research Instrumentation: Active Learning for Text, Scene, and Biosequence Analysis Adaptation to changing environments and changing goals through autonomous learning is a central theme of research in the artificial intelligence laboratory at UCSD. The workstations, robots, and neural network hardware acquired through this grant will enable recently developed learning methods to be tested in four large-scale, real-world applications.-Learning Semantic Representations for Information Retrieval-Embedded Virtual Agents-MEME: A New Software Tool for Sequence Analysis-Real Time Face and Object Recognition in Embedded Agents. The first subproject will apply new algorithms for document rerepresentation and response to human feedback to gigabytes of text in the national TREC competition. Another project will port to the worldwide web software agents that learn and interact now in a simulated artificial life environment. A third project will scale up new software for finding patterns in DNA and protein sequences into a tool capable of autonomously analyzing entire genomes. Finally, a fourth project will validate new active vision methods for recognizing and tracking objects by implementing these methods in mobile robots with controllable cameras.