MI20-HLC


Tutorials


Bratko
Professor Ivan Bratko, University of Ljubljana

Tutorial title: "Qualitative Modelling and Learning"


Abstract:
Traditionally, system simulation is based on numerical models which produce precise numerical results about the behaviour of modelled systems. Such precise numerical answers often contain much more information than actually needed. Also, excessively detailed  information may be hard to understand and thus undesirable from the point of view of Explainable AI.  In every day life, humans use common sense to reason about their environment qualitatively, without numbers. The field of qualitative modelling and reasoning in AI is concerned with qualitative representations of static and dynamic systems, and with algorithms for qualitative simulation. These tools can be used for automated common sense reasoning, explanation of how the system works, and even for invention from first principles. This tutorial will cover: basic principles of qualitative representation and modelling, qualitative simulation with QSIM, learning qualitative models from observations, and task planning with qualitative models. Examples of applications in robotics and systems control will be include: quadcopter learning to fly, rescue robot learning to climb obstacles, humanoid robot learning to walk.

Short biography:
Professor Ivan Bratko is head of Artificial intelligence Laboratory, Faculty of Computer and Information Sc. of Ljubljana University. He has conducted research in machine learning, knowledge-based systems, qualitative modelling, intelligent robotics, heuristic programming and computer chess. He has published over 200 scientific papers and a number of books, including Prolog Programming for Artificial Intelligence (Addison-Wesley/Pearson Education, third edition, 2001), KARDIO: a Study in Deep and Qualitative Knowledge for Expert Systems (MIT Press, 1989; co-authored by I. Mozetič and N. Lavrač), and Machine Learning and Data Mining: Methods and Applications (Wiley, 1998; co-edited by R.S. Michalski and M. Kubat). He has been member of the editorial boards of a number of scientific journals, including Artificial Intelligence, Machine Learning, Journal of AI Research, Journal of ML Research, and KAIS (Journal of Knowledge and Information Systems). He was one of the founders and the first chairman of SLAIS (Slovenian AI Society) and chairman of ISSEK, International School for the Synthesis of Expert Knowledge, based in Udine, Italy. He is member of SAZU (Slovene Academy of Arts and Sciences) and a Fellow of ECCAI. He has been visiting professor or visiting scientist at various universities, including Edinburgh University, Strathclyde University, Sydney University, University of New South Wales, Polytechnic University of Madrid, University of Klagenfurt, Delft University of Technology.
deRaedt
Professor Luc De Raedt, Department of Computer Science, Katholieke Universiteit Leuven

Tutorial title: "Probabilistic Programming and Statistical Relational AI"


Abstract:
The tutorial will provide a motivation for, an overview of and an introduction to the fields of statistical relational learning and probabilistic programming. These combine rich expressive relational representations with the ability to learn, represent and reason about uncertainty. The tutorial will introduce a number of core concepts concerning representation and inference. It shall focus on probabilistic extensions of logic programming languages, such as CLP(BN), BLPs, ICL, PRISM, ProbLog, LPADs, CP-logic, SLPs and DYNA, but also discusses relations to alternative probabilistic programming languages such as Church, IBAL and BLOG and to some extent to statistical relational learning models such as RBNs, MLNs, and PRMs. The concepts will be illustrated on a wide variety of tasks, including models representing Bayesian networks, probabilistic graphs, stochastic grammars, etc. This should allow participants to start writing their own probabilistic programs. We further provide an overview of the different inference mechanisms developed in the field, and discuss their suitability for the different concepts. We also touch upon approaches to learn the parameters of probabilistic programs, show how deep learning and probabilistic programming can be combined, and mention a number of applications in areas such as robotics, vision, natural language processing, web mining, and bioinformatics. This tutorial is based on joint work and previous tutorials with Angelika Kimmig.

Short biography:
Professor Luc De Raedt is a full professor and head of the lab for Declarative Languages and Artificial Intelligence at KU Leuven.  He is a former chair of Computer Science of the University of Freiburg in Germany. He received an ERC AdG on automated data science, he is a fellow of EurAI and AAAI, and he chaired several conferences in artificial intelligence and machine learning (such as ICML 05 and ECAI 12). Luc's research interests are in Artificial Intelligence, Machine Learning and Data Mining, as well as their applications. He is well known for his contributions in the areas of  learning and reasoning, in particular, for his contributions to statistical relational learning and inductive programming.
UlrikeHahn
Professor Ulrike Hahn, Department of Psychological Sciences, Birkbeck University of London

Tutorial title: "Argumentation: A brief overview of Psychological Research and the Framework(s) that motivate it"


Abstract:
This tutorial will introduce psychological research on argumentation in the last two decades. Detailing both the nature of empirical studies and the motivating frameworks. Implications for the design of computational systems are discussed.

Short biography:
Professor of Psychology in the Department of Psychological Sciences, has been awarded the Alexander von Humboldt Foundation Anneliese Maier Research Award. This award is presented to world class researchers in the humanities and social sciences with the aim of encouraging collaboration between international researchers in Germany. Winners work on research projects funded for up to five years. Professor Hahn’s research investigates aspects of human cognition including argumentation, decision-making, concept acquisition, and language learning. Her work involves both experimentation and modelling. She is Director of the Centre for Cognition, Computation and Modelling which was launched earlier in 2013.
Sanborn
Professor Adam Sanborn, University of Warwick

Tutorial title: "Representing Categories in the Human Mind"

Abstract :
Categorization is a fundamental cognitive process which impacts not just our decisions, but also our memory and perception of the world. The classic conception of a category is as a set of in-or-out rules that carves nature at its joints. In modern models, rule-based representations continue to play a key role, as they explain many empirical effects. However, human category representations are also graded, which suggests that the “family resemblance” between category members is also important. To capture all of these effects, modern models of categorization supplement family resemblance with either rule-based systems or selective attention. In this tutorial, I review a number of influential models of human categorization and the empirical data that motivates them. I then propose a new model that explains both rule-like and family-resemblance-like effects using only a family resemblance representation.

Short biography:
Adam Sanborn is a cognitive psychologist interested in how rational people's behaviour is: whether the biases that people show correspond to normative statistical models and approximations to statistical models. He has studied these ideas in various areas of cognition, including categorization, perception, decision making, learning, reasoning, and intuitive physics. He received his PhD in Cognitive Science and Psychological and Brain Sciences from Indiana University and worked as a postdoc at the Gatsby Computational Neuroscience Unit of University College London. He is currently an Associate Professor at the University of Warwick. In 2019, he was awarded an ERC Consolidator grant to study how sampling algorithms can explain human cognition.