AAAI Fall Symposium Series

E439884

The AAAI Fall Symposium Series is a recurring set of focused, small-scale research meetings in artificial intelligence that bring together scholars and practitioners to discuss emerging topics and challenges in the field.

Try in SPARQL Jump to: Surface forms Statements Referenced by

All labels observed (1)

Label Occurrences
AAAI Fall Symposium Series canonical 1

Statements (47)

Predicate Object
instanceOf academic conference series
artificial intelligence event
encourages community building in specialized AI subfields
early-stage research discussion
field artificial intelligence
focus emerging topics in artificial intelligence
focused research meetings
format symposium
frequency annual
hasComponent multiple parallel symposia
hasProceedings online symposium technical reports
hasTheme applications of AI
emerging AI research areas
interdisciplinary AI topics
includesActivity panel discussions
paper presentations
poster sessions
working group discussions
language English
organizer AAAI NERFINISHED
Association for the Advancement of Artificial Intelligence NERFINISHED
participationMode in-person
sometimes hybrid or virtual
purpose bring together researchers and practitioners in AI
discuss emerging topics and challenges in AI
region North America
relatedTo AAAI Conference on Artificial Intelligence NERFINISHED
AAAI Spring Symposium Series NERFINISHED
scale small-scale
scheduling held over several consecutive days
season fall
sponsor AAAI NERFINISHED
sponsoredBy AAAI Technical Program NERFINISHED
startYear early 1990s
submissionType extended abstracts
peer-reviewed papers
position papers
targetAudience AI practitioners
AI researchers
graduate students in AI
topicSelectionMethod call for symposium proposals
typicalCity Arlington, Virginia NERFINISHED
typicalLocation United States NERFINISHED
typicalTimeOfYear November
October
typicalVenue Westin Arlington Gateway NERFINISHED
website https://www.aaai.org/Symposia/Fall/ffs.php

Referenced by (1)

Full triples — surface form annotated when it differs from this entity's canonical label.