Schedule

Tuesday, September 23
<Workshops and Tutorials>
 
Wednesday, September 24
8:45-09:00 <Welcome>
09:00-10:00 Keynote Talk
Claudia D'Amato:
On the Need for Explanations and Semantics-Aware Machine Learning with Knowledge Graphs
10:00-10:25 Johan Kwisthout, Silja Renooij:
Inverse Marginalisation for Safely Expanding Bayesian Networks
10:25-10:55 <Coffee break>
10:55-11:20 Houda Briwa, Anders Madsen, Maria Chiara Leva:
Classifying Control Room Operators’ Performance Using Bayesian Networks
11:20-11:45 Anders Madsen, Somesh Bhattacharya, Christian Dausel Jensen, Rasmus Løvenstein Olsen, Hans-Peter Schwefel:
Wrong Data Detection in Electricity Grids Using Bayesian Networks
11:45-12:10 Annet Onnes, Silja Renooij:
Maximum Entropy-based Quantification for Probability Elicitation in Bayesian Networks
12:10-12:35 Janneke H. Bolt, Arjen Hommersom, Silja Renooij:
Involving Uncertainty in Bayesian Network Tuning
12:35-13:50 <Lunch break>
13:50-14:15 Salvador Madrigal, Cyprien Gilet, Vu-Linh Nguyen, Sebastien Destercke:
Discrete Minimax Probabilistic Classifier Chains for Multi-Label Classification Under Label Imbalance
14:15-14:40 Serafín Moral-García, Rafael Cabañas, Antonio Salmeron:
Noise-Robust Weighted Logistic Regression Based on Outlier Detection with Expectation Maximization
14:40-15:05 Iván Pérez, Jirka Vomlel, Patrícia Martinková:
From RBMs to BN2A models: Parameter Transformation for Interpretable Educational Diagnostics
15:05-15:30 Florian Andreas Marwitz, Ralf Möller, Magnus Bender, Marcel Gehrke:
Denoising the Future: Top-p Distributions for Moving Through Time
15:30-16:00 <Coffee break>
16:00-16:25 David Nieto-Barba, Enrique Miranda, Ignacio Montes:
Distortions of lower probabilities as a tool for avoiding conflict
16:25-16:50 Henri Prade, Gilles Richard:
Analogical proportions between probabilities
16:50-17:15 Chenrui Zhu, Vu-Linh Nguyen, Marie-Hélène Masson, Sebastien Destercke:
Robust Explanations: The Case of Prime Implicants
18:00- <Reception Villa>
 
Thursday, September 25
09:00-10:00 Keynote Talk
Vanina Martinez:
Making Sense of Messy Graphs: Addressing Inconsistency and Uncertainty in Data-Graphs
10:00-10:25 Anthony Hunter:
Using Sentence Embeddings to Identify Conflicts in Propositional Logic
10:25-10:55 <Coffee break>
10:55-11:20 Nazlı Nur Karabulut, Tanya Braun:
Counting Agents in Partially Observable Stochastic Games
11:20-11:45 Pratik Karmakar, Antoine Gauquier, Pierre Senellart:
Expected Shapley Value is Shapley Value for Expected Utility Game
11:45-12:10 Marco Sangalli, Thomas Krak, Erik Quaeghebeur:
Upper Expected Meeting Times for Interdependent Stochastic Agents
12:10-12:35 Lea Bauer, Jonas Karge:
Elicit and Weigh: A Voting-Based Approach to Optimal Weights in Imprecise Linear Pooling
12:35-13:50 <Lunch break>
13:50-14:15 Franz Baader, Renata Wassermann:
Gärdenfors's Supplementary Postulates for Partial Product Contractions
14:15-14:40 Alexander Hahn, Gabriele Kern-Isberner, Lars-Phillip Spiegel, Christoph Beierle:
Explaining Changes in Total Preorders and Ranking Functions
14:40-15:05 Jonas Philipp Haldimann, Aron Spang, Lars-Phillip Spiegel, Christoph Beierle:
Implementing Lexicographic Inference Using Partial MaxSAT
15:05-15:30 Konstantinos Georgatos:
Conditional Logics of Nondeterministic Change
15:30-16:00 <Coffee break>
16:00-16:25 Lydia Castronovo, Tommaso Flaminio, Lluis Godo, Giuseppe Sanfilippo:
Towards an algebraic and probabilistic setting for iterated Boolean conditionals
16:25-16:50 Tommaso Flaminio, Lluis Godo, Giuliano Rosella:
On measuring the possibility of selection-function based conditionals, general updates, and qualitative capacities.
16:50-17:15 Armand Gaudillier, Khaled Belahcène, Wassila Ouerdane, Sebastien Destercke:
Possibilistic logic and inference for linear systems
18:00- <Conference Dinner>
 
Friday, September 26
09:00-10:00 Keynote Talk
Tommie Meyer:
Defeasible Reasoning - the Status Quo
10:00-10:25 Toshiko Wakaki:
Assumption-Based Argumentation for General Extended Disjunctive Logic Programming with Negation as Failure in the Head
10:25-10:55 <Coffee break>
10:55-11:20 Shawn Bowers, Martin Caminada, Bertram Ludäscher:
Winning by Numbers: Connecting Strong Admissibility to Optimal Play in Argumentation
11:20-11:45 Hiba Abderrazik, Dragan Doder:
First steps towards forgetting in ASPIC+
11:45-12:10 Martin Caminada:
Strong Admissibility and Infinite Argumentation Frameworks
12:10-12:35 Anshu Xiong, Songmao Zhang:
Recognizing the Impact among Relevant Elements for Reaching Stability in Incomplete Argumentation Frameworks
12:35-13:50 <Lunch break>
13:50-14:15 Carl Corea, Timotheus Kampik, Nico Potyka:
Privacy-Preserving Inconsistency Measurement
14:15-14:40 Tomoaki Kawano:
Dynamic Logic for Quantum Probability
14:40-15:05 Polina Gordienko, Christoph Jansen, Thomas Augustin, Martin Rechenauer:
Consensus in Motion: A Case of Dynamic Rationality of Sequential Learning in Probability Aggregation
15:05-15:30 Cory Butz, Camilla Lewis, Alejandro Santoscoy-Rivero, Anders Madsen:
Arithmetic Circuit Compilation using Symbolic Probabilistic Inference and Indicator-Determined Buckets
15:30-15:55 Andrew Lewis-Smith, Zhiguang Zhao:
A Kripke Semantics for Monadic BL Chains
15:55-16:10 <Closing>
16:10-16:40 <Farewell coffee break>

Invited speakers

Claudia d'Amato (University of Bari, Italy)
On the Need for Explanations and Semantics-Aware Machine Learning with Knowledge Graphs
Despite the large usage of Knowledge Graphs (KGs) in in- dustry as well as academia, it is well known that they suffer of incom- pleteness and noise, being the result of a complex building process. In order to improve the quality of KGs, numeric based Machine Learning (ML) solutions are mostly adopted, given their proved ability to scale on very large KGs. They are usually grounded on the graph structure and they generally consist of series of numbers without any obvious human interpretation, thus possibly affecting the interpretability, the explain- ability and sometimes the trustworthiness of the results. Nevertheless, KGs may rely on expressive representation languages, e.g. RDFS and OWL, that are also endowed with deductive reasoning capabilities, but both expressiveness and reasoning are most of the time disregarded by the majority of the numeric methods that have been developed so far. In this talk, the role and the value added that the semantics may have for ML solutions, including also symbolic approaches, will be argued. Addi- tionally, the importance of tacking into account semantics when comput- ing explanations for tasks such as link prediction will be also addressed. Hence the research directions on empowering ML and explanation solu- tions by injecting background knowledge will be presented jointly with the analysis of the most urgent issues that need to be solved.
Vanina Martinez (Artificial Intelligence Research Institute, Spain)
Making Sense of Messy Graphs: Addressing Inconsistency and Uncertainty in Data-Graphs
Graph databases are becoming widely successful as data models that allow for effective representation and processing of complex relationships among various types of data. Data graphs are a particular type of graph database whose representation enables both data to values in the paths and in the nodes to be treated as first-class citizens by the query language. As with any other type of data repository, data graphs may suffer from errors and discrepancies with respect to the real-world data they intend to represent. In the last 5 years, we have investigated notions of inconsistency and uncertainty modelling in data graphs. In this talk, I will present some results on repairing and consistent query answering for data-graphs when considering alternative transformations of the data-graph: either removing (subset), adding (superset), or modifying (updating) nodes, data-values on nodes, and edges. I will also present the work we did on exploring the notion of probabilistic unclean data-graphs, in order to capture the idea that the observed (unclean) data-graph is actually the noisy version of a clean one that correctly models the world, but that we know only partially. As the factors that lead to such a state of affairs may be many, e.g., all different types of clerical errors or unintended transformations of the data, and depend heavily on the application domain, we assume an epistemic probabilistic model that describes the distribution over all possible ways in which the clean (uncertain) data-graph could have been polluted. Based on this model, we define two computational problems: data cleaning and probabilistic query answering.
Tommie Meyer (CAIR, University of Cape Town, South Africa)
Defeasible Reasoning - the Status Quo
Preferential approaches to dealing with defeasible reasoning in the propositional case have turned out to be particularly promising, mainly because they are based on an elegant, comprehensive and well-studied framework for non-monotonic reasoning proposed by Kraus, Lehmann, and Magidor, and often referred to as the KLM approach. In the first part of this talk I will provide an introduction to propositional KLM-style defeasible reasoning. This will be followed by an overview of attempts to extend KLM-style defeasible reasoning beyond propositional logic. More specifically, I'll look at the application of the KLM approach to description logics and some restricted first-order logics. In doing so, I'll describe what has worked well, but also on what are the (many) remaining challenges.

Proceedings

The proceedings will be published by Springer within the LNCS Series. A link will be made available here as soon as the proceedings are available. In the following, you will find a list of accepted papers:

Contact

In case of questions, please write one of the organizers: Kai Sauerwald (firstname.second [at] fernuni-hagen.de) or Matthias Thimm (firstname.second [at] fernuni-hagen.de), FernUniversität in Hagen, 58084, Hagen, Germany.