Invited Speakers

Abstracts

Adnan Darwiche -- Tractable Boolean Circuits: Applications and Compilation Algorithms

Tractable Boolean circuits have been playing an increasingly important role in AI and beyond, being also the basis for tractable probabilistic circuits. This includes (1) providing a systematic approach for tackling problems beyond NP, (2) allowing one to learn from certain combinations of knowledge and data, and (3) reasoning about the behavior of some machine learning systems. In this talk, I will review the basics and applications of tractable Boolean circuits, while also discussing the compilation of Boolean formula into tractable circuits: a critical process which can benefit from additional efforts by the broad computer science community.

Leslie Lamport -- If You're Not Writing a Program, Don't Use a Programming Language

Algorithms are not programs. They can and should be written with math rather than programming languages or pseudo-languages. This applies to many more algorithms than the ones taught in algorithm courses.

Note: This will be a Q & A session for the following talk https://youtu.be/wQiWwQcMKuw The talk itself is 50 minutes; the video includes Q&A and is longer. Participants are asked to watch the video before the talk.

Stuart Russell -- Logic, Probability, Knowledge, and Learning

One purpose of learning is to accumulate knowledge, which then becomes an input to enable further learning. I will examine this idea first in the context of logic and then in the context of probability. The idea becomes particularly powerful with probabilistic formalisms that draw on the expressive power of first-order logic, although there is still a long way to go before the potential of cumulative learning is fulfilled.

Bio:

Stuart Russell is a Professor of Computer Science at the University of California at Berkeley, holder of the Smith-Zadeh Chair in Engineering, and Director of the Center for Human-Compatible AI. He is a recipient of the IJCAI Computers and Thought Award and from 2012 to 2014 held the Chaire Blaise Pascal in Paris. He is an Honorary Fellow of Wadham College, Oxford, an Andrew Carnegie Fellow, and a Fellow of the American Association for Artificial Intelligence, the Association for Computing Machinery, and the American Association for the Advancement of Science. His book “Artificial Intelligence: A Modern Approach” (with Peter Norvig) is the standard text in AI, used in 1500 universities in 135 countries. His research covers a wide range of topics in artificial intelligence, with an emphasis on the long-term future of artificial intelligence and its relation to humanity. He has developed a new global seismic monitoring system for the nuclear-test-ban treaty and is currently working to ban lethal autonomous weapons.

Peter Stuckey -- From CLP(R) to MiniZinc: There and Back Again

Constraint logic programming (CLP) was a revolution in declarative programming showing how we could answer very interesting and complex questions by a combination of programmed search and constraint solving. But constraint programming (CP) moved away from its logic programming roots to concentrate on modelling, simply specifying a system of constraints, in the process losing the ability to do complex meta-search. MiniZinc is one of the leading constraint programming modelling languages. It was originally designed to tackle complex CP problems, typically small systems of complex constraints. But its uses have changed, often it is used to solve very large systems of simple constraints. This meant that many of the original assumptions in the design of MiniZinc are invalid. In this talk we will examine a new architecture for MiniZinc, which uses constraint solving for model optimization, and includes incremental solving and backtracking. In some sense the new architecture makes MiniZinc a CLP system, bringing us back to the roots of the field.

This is joint work with Guido Tack, Graeme Gange, and Jip Dekker.