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

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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.