Bio

Claudia Schulz received her B.Sc. in Cognitive Science from the University of Osnabrück in 2011. She then decided to specialise in Artificial Intelligence, receiving an M.Sc. in Artificial Intelligence from Imperial College London in 2012. Since 2012, Claudia is a Ph.D. candidate at Imperial College London interested in logic-based formalisms in Artificial Intelligence used for the representation of knowledge and for decision making based on the represented knowledge. Claudia is a keen lecturer and teaching assistant, which won her Imperial College’s Best Graduate Teaching Assistant Award in 2015. She was also involved in setting up the Imperial College ACM Student Chapter and served as its chair in 2014/15. Apart from academia, Claudia enjoys the outdoors and is an enthusiastic climber and runner.

Explaining Logic Programming with Argumentation

Explaining Logic Programming with Argumentation

Argumentation Theory and Logic Programming are two prominent approaches in the field of knowledge representation and reasoning, a sub-field of Artificial Intelligence. One of the applications of such approaches are recommendation systems, to be used for example for making medical treatment decisions. The main difference between Argumentation Theory and Logic Programming is that the former focuses on human-like reasoning, thus sometimes neglecting the efficiency of the reasoning procedure, whereas the latter is concerned with the efficient computation of solutions to a reasoning problem, resulting in a less human-understandable process. In recent years, Logic Programming has been frequently applied for the computation of reasoning problems in Argumentation Theory and has been found an efficient method for determining solutions to those problems. My research is concerned with the opposite direction, i.e. with using ideas from Argumentation Theory to improve Logic Programming techniques. One of the shortcomings of Logic Programming is that it does not provide any explanation of the solution computed for a given problem. For recommendation systems based on Logic Programming, this means that there is no explanation for a recommendation made by the system. I thus created a mechanism to explain Logic Programming solutions in a human-like argumentative style by applying ideas from the field of Argumentation Theory. A medical treatment recommendation can thus be automatically explained in the style of two physicians arguing about the best treatment.