Talk Announcement Monday, October 24, 2016, 14:30 IDSIA Meeting Room Prof. Marc Pouly, Lucerne University of Applied Sciences and Arts Algebraic Information Theory. A talk with definitely more open questions than answers. Abstract. Quantitative information theories, such as Hartley’s relational information theory or Shannon’s statistical information theory, have beyond doubt been a success story. However, these theories are based on the fundamental assumption that information can be measured and expressed in bits. Alternatively, the valuation algebra framework proposed by Shenoy and Kohlas, which is known to provide the formal requirements for the application of variable elimination and other inference schemes, equipped with an additional axiom allows for the definition of a qualitative information theory that in a generic way establishes an order of information without quantifying individual information pieces. As with many other information theories it is to a large extend an open question how these theories relate to each other and whether they are consistent, but recent investigations indicate that specific formalisms of imprecise probabilities could provide the missing link for such a deeper understanding. About the speaker. Marc Pouly studied computer science and mathematics at the University of Fribourg in Switzerland. After his PhD in artificial intelligence and information theory, Marc joined the Cork Constraint Computation Center in Ireland, where he worked on discrete optimization problems and constraint programming, and later the Interdisciplinary Center for Security, Reliability and Trust at the University of Luxembourg, where he investigated the application of different information theories in the context of information security. Since 2011 Marc Pouly is professor at the Lucerne University of Applied Sciences and Arts in Switzerland and since recently head of the computer science master¹s program. He has been active in teaching artificial intelligence and information security and pursues various research projects in the context of industrial optimization, machine learning and security.