Naturally-Inspired Artificial Intelligence

AAAI 2008 Fall Symposium Series

November 7-9, Arlington, VA

The divide between how biological and computational systems solve cognitive problems and adjust to novel circumstances is readily apparent. While animals display marked flexibility in adjusting to new situations, our current computational approaches excel in well-defined, formally structured domains.

We are interested in new approaches to bridging this gap. Our perspective is that studies of natural and artificial intelligences can and should be mutually informative. Even young animals solve historically difficult computational problems, and we believe understanding how they do this will enable the creation of more sophisticated artificial systems. Conversely, computational models provide structure and insight into understanding animal learning and cognition. By combining biological and computational perspectives, we expect to obtain new insights that further the classical goals of artificial intelligence.

This symposium is intended to bring together researchers working on models that pertain directly to both natural and machine cognition. Particular methodology, motivation, or implementation decisions do not constrain our interests--we expect that relevant work may touch on themes as diverse as human experiments, neural models, engineering of complex systems, mathematical analysis, programming language design, and high-level cognitive models, to name only a few possibilities. We are interested in any work that has a clearly described relationship between a line of investigation and the larger problem of producing computational models that illuminate the peculiar nature and capabilities of cognition.

Organizing Committee

Jacob Beal (BBN Technologies), Paul Bello (ONR) Nick Cassimatis (RPI), Michael Coen (UW Madison), Patrick Winston (MIT)


Friday, November 7

Saturday, November 8

Sunday, November 9