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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.
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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.
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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.
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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.
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