The AAAI Feigenbaum Prize is awarded biennially to recognize and encourage outstanding Artificial Intelligence research advances that are made by using experimental methods of computer science. The “laboratories” for the experimental work are real-world domains, and the power of the research results are demonstrated in those domains. The Feigenbaum Prize may be given for a sustained record of high-impact seminal contributions to experimental AI research; or it may be given to reward singular remarkable innovation and achievement in experimental AI research. The prize is $10,000 and is provided by the Feigenbaum Nii Foundation and administered by AAAI.
Edward Feigenbaum is a Kumagai Professor of Computer Science Emeritus at Stanford University. Feigenbaum earned his Ph.D at Carnegie Mellon University from 1956–1959. In the 1960s and 1970s, he was a pioneer in AI research as experimental computer science, and in the applications of AI research. In 1986, he was elected to the National Academy of Engineering, and in 1995, he received computer science’s highest research honor — The ACM Turing Award. Feigenbaum was the second president of the American Association for Artificial Intelligence, serving from 1980–1981, and was elected to AAAI Fellowship in 1990.
The first Feigenbaum Prize was awarded in 2011 in conjunction with the Twenty-Fifth Annual AAAI Conference on Artificial Intelligence (AAAI-11), held August 7–11, in San Francisco, California. The next Feigenbaum Prize will be awarded at AAAI-23 in Washington, D.C., USA, February 7-14, 2023. Please submit your nomination via the following form no later than September 23, 2023.
Feigenbaum Prize Nomination Form
For additional information, please contact firstname.lastname@example.org.
Carla P. Gomes Cornell University
For high-impact contributions to the field of artificial intelligence through innovations in constraint reasoning, optimization, the integration of reasoning and learning, and through founding the field of Computational Sustainability with impactful AI applications in ecology, species conservation, environmental sustainability, and materials discovery for clean energy.
Stuart Russell University of California, Berkeley
In recognition of his high-impact contributions to the field of artificial intelligence through innovation and achievement in probabilistic knowledge representation, reasoning, and learning, including its application to global seismic monitoring for the Comprehensive Nuclear-Test-Ban Treaty.
Yoav Shoham (Stanford University/Google)
For high-impact basic research in artificial intelligence — including knowledge representation, multiagent systems, and computational game theory — and translating the basic research into impactful and innovative commercial products.
Eric Horvitz (Microsoft Research)
For sustained and high-impact contributions to the field of artificial intelligence through the development of computational models of perception, reflection and action, and their application in time-critical decision making, and intelligent information, traffic and healthcare systems.
IBM Watson Team*
For demonstrating that a synthesis of AI techniques, including symbolic knowledge representation, natural language understanding, and statistical machine learning, can achieve human-level performance in real-time factual question-answering.
*Team Members: Sugato Bagchi Michael Barborak, Branimir Boguraev, Eric Brown, David Carmel, Jennifer Chu-Carroll, Jaroslaw Cwiklik, Edward Epstein, James Fan, David Ferrucci, Tong-Haing Fin, David Gondek, Bhavani Iyer, Aditya Kalyanpur, Hiroshi Kanayama, Adam Lally, Jonathan Lenchner, Anthony Levas, Burn Lewis, Michael McCord, Erik Mueller, J. William Murdock, Yue Pan, Siddharth Patwardhan, John Prager, Marshall Schor, Dafna Sheinwald, David Shepler, Kohichi Takeda, Gerald Tesauro, Chang Wang, Chris Welty, Wlodek Zadrozny, Lei Zhang
Sebastian Thrun (Stanford University) and William A. “Red” Whittaker (Carnegie Mellon University)
For their influential contributions to artificial intelligence via achievements in autonomous vehicle research, including experimental efforts and research leadership of teams addressing challenges with the fielding of robotic systems in the open world.