no code implementations • 6 Dec 2021 • Alexander Lavin, David Krakauer, Hector Zenil, Justin Gottschlich, Tim Mattson, Johann Brehmer, Anima Anandkumar, Sanjay Choudry, Kamil Rocki, Atılım Güneş Baydin, Carina Prunkl, Brooks Paige, Olexandr Isayev, Erik Peterson, Peter L. McMahon, Jakob Macke, Kyle Cranmer, Jiaxin Zhang, Haruko Wainwright, Adi Hanuka, Manuela Veloso, Samuel Assefa, Stephan Zheng, Avi Pfeffer
We present the "Nine Motifs of Simulation Intelligence", a roadmap for the development and integration of the essential algorithms necessary for a merger of scientific computing, scientific simulation, and artificial intelligence.
no code implementations • 5 Oct 2021 • Avi Pfeffer, Michael Harradon, Joseph Campolongo, Sanja Cvijic
These representations are defined implicitly using a set of standardized operations that can be performed on them.
no code implementations • 15 May 2017 • Avi Pfeffer
Probabilistic modeling enables combining domain knowledge with learning from data, thereby supporting learning from fewer training instances than purely data-driven methods.
no code implementations • 27 Apr 2017 • Avi Pfeffer, Brian Ruttenberg, Lee Kellogg, Michael Howard, Catherine Call, Alison O'Connor, Glenn Takata, Scott Neal Reilly, Terry Patten, Jason Taylor, Robert Hall, Arun Lakhotia, Craig Miles, Dan Scofield, Jared Frank
Artificial intelligence methods have often been applied to perform specific functions or tasks in the cyber-defense realm.
no code implementations • 10 Jun 2016 • Avi Pfeffer, Brian Ruttenberg, William Kretschmer
Reasoning on large and complex real-world models is a computationally difficult task, yet one that is required for effective use of many AI applications.
no code implementations • 11 Sep 2015 • Avi Pfeffer, Brian Ruttenberg, Amy Sliva, Michael Howard, Glenn Takata
In this paper, we present a new inference framework, lazy factored inference (LFI), that enables factored algorithms to be used for models with infinitely many variables.
no code implementations • 11 Jul 2014 • Brian E. Ruttenberg, Avi Pfeffer
Existing decision-theoretic reasoning frameworks such as decision networks use simple data structures and processes.
no code implementations • 15 Jan 2014 • Yaakov Gal, Avi Pfeffer
This paper presents Networks of Influence Diagrams (NID), a compact, natural and highly expressive language for reasoning about agents beliefs and decision-making processes.
no code implementations • 11 May 2012 • Fabio Cozman, Avi Pfeffer
This is the Proceedings of the Twenty-Seventh Conference on Uncertainty in Artificial Intelligence, which was held in Barcelona, Spain, July 14 - 17 2011.