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Monday, September 5 • 13:30 - 14:00
CP and AI Workshop - Invited Talk: From Constraint Programming to Probabilistic Programming to Approximate Programming: Observations and Thoughts
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  • 13h30: Introduction: Eugene Freuder
  • 13h35: Invited talk: Rina Dechter

Abstract: Languages and algorithms of Constraint Programming have made a significant impact on Artificial Intelligence, and were driven to a large extent by the Artificial Intelligence agenda. An area where cross-fertilization has been particularly productive is in Graphical Models. Much of this work is at the level of (propositional) solvers and algorithms only. However, the experience gained in the CP community in developing Constraint Programming languages may not have gone far enough in penetrating the rest of AI.  An excellent opportunity for changing that exists in the recent area of Probabilistic Programming.

CP may join the language effort by extending its constraint languages to accommodate uncertainty and continuous variables, and develop solvers that can effectively handle first-order deterministic and probabilistic queries.  In addition, equipping such languages with anytime approximation solvers that have predictive performance guarantees could lead to new paradigms (e.g., “Approximate Programming”).

I will present some observations and thoughts along those lines.

 

Moderators
avatar for Eugene Freuder

Eugene Freuder

UCC, Insight Centre for Data Analytics

Speakers
avatar for Rina Dechter

Rina Dechter

University of California, Irvine


Monday September 5, 2016 13:30 - 14:00
B203 Bosco building

Attendees (5)