NSF Org:	EIA DIVISION OF EXPERIMENTAL & INTEG ACTIVIT
Recipient:	UNIVERSITY OF CALIFORNIA, SAN DIEGO
Initial Amendment Date:	January 30, 1997
Latest Amendment Date:	January 30, 1997
Award Number:	9617307
Award Instrument:	Standard Grant
Program Manager:	Frederica Darema EIA  DIVISION OF EXPERIMENTAL & INTEG ACTIVIT CSE  Directorate for Computer and Information Science and Engineering
Start Date:	February 1, 1997
End Date:	January 31, 1999 (Estimated)
Total Intended Award Amount:	$40,000.00
Total Awarded Amount to Date:	$40,000.00
Funds Obligated to Date:	FY 1997 = $40,000.00
History of Investigator:	Garrison Cottrell (Principal Investigator) gary@cs.ucsd.edu Eric Mjolsness (Co-Principal Investigator) Richard Belew (Co-Principal Investigator) Charles Elkan (Co-Principal Investigator)
Recipient Sponsored Research Office:	University of California-San Diego 9500 GILMAN DR LA JOLLA CA  US  92093-0021 (858)534-4896
Sponsor Congressional District:	50
Primary Place of Performance:	University of California-San Diego 9500 GILMAN DR LA JOLLA CA  US  92093-0021
Primary Place of Performance Congressional District:	50
Unique Entity Identifier (UEI):	UYTTZT6G9DT1
Parent UEI:
NSF Program(s):	CISE Research Resources
Primary Program Source:	app-0197
Program Reference Code(s):	9218, HPCC
Program Element Code(s):	289000
Award Agency Code:	4900
Fund Agency Code:	4900
Assistance Listing Number(s):	47.070

ABSTRACT

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.

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